A Differential Geometric Approach to Nonlinear Filtering: The Projection Filter
Brigo, D.; Hanzon, B.; LeGland, F.
1998-01-01
This paper presents a new and systematic method of approximating exact nonlinear filters with finite dimensional filters, using the differential geometric approach to statistics. The projection filter is defined rigorously in the case of exponential families. A convenient exponential family is
The statistical bandwidth of Butterworth filters
Davy, J. L.; Dunn, I. P.
1987-06-01
The precision of standard architectural acoustic measurements is a function of the statistical bandwidth of the band pass filters used in the measurements. The International and United States Standards on octave and fractional octave-band filters which specify the band pass filters used in architectural acoustics measurements give the effective bandwidth, but unfortunately not the statistical bandwidth of the filters. Both these Standards are currently being revised and both revisions require the use of Butterworth filter characteristics. In this paper it is shown theoretically that the ratio of statistical bandwidth to effective bandwidth for an nth order Butterworth band pass filter is {2n}/{(2n-1)}. This is verified experimentally for third-octave third-order Butterworth band pass filters. It is also shown experimentally that this formula is approximately correct for some non-Butterworth third-octave third-order band pass filters. Because of the importance of Butterworth filters in the revised Standards, the theory of Butterworth filters is reviewed and the formulae for Butterworth filters given in both revised Standards are derived.
Statistically-Efficient Filtering in Impulsive Environments: Weighted Myriad Filters
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Juan G. Gonzalez
2002-01-01
Full Text Available Linear filtering theory has been largely motivated by the characteristics of Gaussian signals. In the same manner, the proposed Myriad Filtering methods are motivated by the need for a flexible filter class with high statistical efficiency in non-Gaussian impulsive environments that can appear in practice. Myriad filters have a solid theoretical basis, are inherently more powerful than median filters, and are very general, subsuming traditional linear FIR filters. The foundation of the proposed filtering algorithms lies in the definition of the myriad as a tunable estimator of location derived from the theory of robust statistics. We prove several fundamental properties of this estimator and show its optimality in practical impulsive models such as the ÃŽÂ±-stable and generalized-t. We then extend the myriad estimation framework to allow the use of weights. In the same way as linear FIR filters become a powerful generalization of the mean filter, filters based on running myriads reach all of their potential when a weighting scheme is utilized. We derive the Ã¢Â€ÂœnormalÃ¢Â€Â equations for the optimal myriad filter, and introduce a suboptimal methodology for filter tuning and design. The strong potential of myriad filtering and estimation in impulsive environments is illustrated with several examples.
Morphological representation of order-statistics filters.
Charif-Chefchaouni, M; Schonfeld, D
1995-01-01
We propose a comprehensive theory for the morphological bounds on order-statistics filters (and their repeated iterations). Conditions are derived for morphological openings and closings to serve as bounds (lower and upper, respectively) on order-statistics filters (and their repeated iterations). Under various assumptions, morphological open-closings and close-openings are also shown to serve as (tighter) bounds (lower and upper, respectively) on iterations of order-statistics filters. Simulations of the application of the results presented to image restoration are finally provided.
SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering
International Nuclear Information System (INIS)
Iliopoulos, AS; Sun, X; Floros, D; Zhang, Y; Yin, FF; Ren, L; Pitsianis, N
2016-01-01
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 as 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
SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering
Energy Technology Data Exchange (ETDEWEB)
Iliopoulos, AS; Sun, X [Duke University, Durham, NC (United States); Floros, D [Aristotle University of Thessaloniki (Greece); Zhang, Y; Yin, FF; Ren, L [Duke University Medical Center, Durham, NC (United States); Pitsianis, N [Aristotle University of Thessaloniki (Greece); Duke University, Durham, NC (United States)
2016-06-15
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 as 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
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
International Nuclear Information System (INIS)
Candy, J.
2007-01-01
A introduction to particle filtering is discussed starting with an overview of Bayesian inference from batch to sequential processors. Once the evolving Bayesian paradigm is established, simulation-based methods using sampling theory and Monte Carlo realizations are discussed. Here the usual limitations of nonlinear approximations and non-gaussian processes prevalent in classical nonlinear processing algorithms (e.g. Kalman filters) are no longer a restriction to perform Bayesian inference. It is shown how the underlying hidden or state variables are easily assimilated into this Bayesian construct. Importance sampling methods are then discussed and shown how they can be extended to sequential solutions implemented using Markovian state-space models as a natural evolution. With this in mind, the idea of a particle filter, which is a discrete representation of a probability distribution, is developed and shown how it can be implemented using sequential importance sampling/resampling methods. Finally, an application is briefly discussed comparing the performance of the particle filter designs with classical nonlinear filter implementations
SAR Interferogram Filtering of Shearlet Domain Based on Interferometric Phase Statistics
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Yonghong He
2017-02-01
Full Text Available This paper presents a new filtering approach for Synthetic Aperture Radar (SAR interferometric phase noise reduction in the shearlet domain, depending on the coherent statistical characteristics. Shearlets provide a multidirectional and multiscale decomposition that have advantages over wavelet filtering methods when dealing with noisy phase fringes. Phase noise in SAR interferograms is directly related to the interferometric coherence and the look number of the interferogram. Therefore, an optimal interferogram filter should incorporate information from both of them. The proposed method combines the phase noise standard deviation with the shearlet transform. Experimental results show that the proposed method can reduce the interferogram noise while maintaining the spatial resolution, especially in areas with low coherence.
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Shkvarko Yuriy
2006-01-01
Full Text Available We address a new approach to solve the ill-posed nonlinear inverse problem of high-resolution numerical reconstruction of the spatial spectrum pattern (SSP of the backscattered wavefield sources distributed over the remotely sensed scene. An array or synthesized array radar (SAR that employs digital data signal processing is considered. By exploiting the idea of combining the statistical minimum risk estimation paradigm with numerical descriptive regularization techniques, we address a new fused statistical descriptive regularization (SDR strategy for enhanced radar imaging. Pursuing such an approach, we establish a family of the SDR-related SSP estimators, that encompass a manifold of existing beamforming techniques ranging from traditional matched filter to robust and adaptive spatial filtering, and minimum variance methods.
Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks
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Muhammad Akram
2017-06-01
Full Text Available In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.
Filter Tuning Using the Chi-Squared Statistic
Lilly-Salkowski, Tyler
2017-01-01
The Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF) performs orbit determination (OD) for the Aqua and Aura satellites. Both satellites are located in low Earth orbit (LEO), and are part of what is considered the A-Train satellite constellation. Both spacecraft are currently in the science phase of their respective missions. The FDF has recently been tasked with delivering definitive covariance for each satellite.The main source of orbit determination used for these missions is the Orbit Determination Toolkit developed by Analytical Graphics Inc. (AGI). This software uses an Extended Kalman Filter (EKF) to estimate the states of both spacecraft. The filter incorporates force modelling, ground station and space network measurements to determine spacecraft states. It also generates a covariance at each measurement. This covariance can be useful for evaluating the overall performance of the tracking data measurements and the filter itself. An accurate covariance is also useful for covariance propagation which is utilized in collision avoidance operations. It is also valuable when attempting to determine if the current orbital solution will meet mission requirements in the future.This paper examines the use of the Chi-square statistic as a means of evaluating filter performance. The Chi-square statistic is calculated to determine the realism of a covariance based on the prediction accuracy and the covariance values at a given point in time. Once calculated, it is the distribution of this statistic that provides insight on the accuracy of the covariance.For the EKF to correctly calculate the covariance, error models associated with tracking data measurements must be accurately tuned. Over estimating or under estimating these error values can have detrimental effects on the overall filter performance. The filter incorporates ground station measurements, which can be tuned based on the accuracy of the individual ground stations. It also includes
A filtering approach to edge preserving MAP estimation of images.
Humphrey, David; Taubman, David
2011-05-01
The authors present a computationally efficient technique for maximum a posteriori (MAP) estimation of images in the presence of both blur and noise. The image is divided into statistically independent regions. Each region is modelled with a WSS Gaussian prior. Classical Wiener filter theory is used to generate a set of convex sets in the solution space, with the solution to the MAP estimation problem lying at the intersection of these sets. The proposed algorithm uses an underlying segmentation of the image, and a means of determining the segmentation and refining it are described. The algorithm is suitable for a range of image restoration problems, as it provides a computationally efficient means to deal with the shortcomings of Wiener filtering without sacrificing the computational simplicity of the filtering approach. The algorithm is also of interest from a theoretical viewpoint as it provides a continuum of solutions between Wiener filtering and Inverse filtering depending upon the segmentation used. We do not attempt to show here that the proposed method is the best general approach to the image reconstruction problem. However, related work referenced herein shows excellent performance in the specific problem of demosaicing.
PARTICLE FILTER BASED VEHICLE TRACKING APPROACH WITH IMPROVED RESAMPLING STAGE
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Wei Leong Khong
2014-02-01
Full Text Available Optical sensors based vehicle tracking can be widely implemented in traffic surveillance and flow control. The vast development of video surveillance infrastructure in recent years has drawn the current research focus towards vehicle tracking using high-end and low cost optical sensors. However, tracking vehicles via such sensors could be challenging due to the high probability of changing vehicle appearance and illumination, besides the occlusion and overlapping incidents. Particle filter has been proven as an approach which can overcome nonlinear and non-Gaussian situations caused by cluttered background and occlusion incidents. Unfortunately, conventional particle filter approach encounters particle degeneracy especially during and after the occlusion. Particle filter with sampling important resampling (SIR is an important step to overcome the drawback of particle filter, but SIR faced the problem of sample impoverishment when heavy particles are statistically selected many times. In this work, genetic algorithm has been proposed to be implemented in the particle filter resampling stage, where the estimated position can converge faster to hit the real position of target vehicle under various occlusion incidents. The experimental results show that the improved particle filter with genetic algorithm resampling method manages to increase the tracking accuracy and meanwhile reduce the particle sample size in the resampling stage.
New Statistics for Texture Classification Based on Gabor Filters
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J. Pavlovicova
2007-09-01
Full Text Available The paper introduces a new method of texture segmentation efficiency evaluation. One of the well known texture segmentation methods is based on Gabor filters because of their orientation and spatial frequency character. Several statistics are used to extract more information from results obtained by Gabor filtering. Big amount of input parameters causes a wide set of results which need to be evaluated. The evaluation method is based on the normal distributions Gaussian curves intersection assessment and provides a new point of view to the segmentation method selection.
Robust filtering for uncertain systems a parameter-dependent approach
Gao, Huijun
2014-01-01
This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties, and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and H¥ filtering, are addressed, and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features: · design approaches to robust filters arranged according to varying complexity level, and emphasizing robust filtering in the parameter-dependent framework for the first time; ·...
INFLUENCE OF STOCHASTIC NOISE STATISTICS ON KALMAN FILTER PERFORMANCE BASED ON VIDEO TARGET TRACKING
Institute of Scientific and Technical Information of China (English)
Chen Ken; Napolitano; Zhang Yun; Li Dong
2010-01-01
The system stochastic noises involved in Kalman filtering are preconditioned on being ideally white and Gaussian distributed. In this research,efforts are exerted on exploring the influence of the noise statistics on Kalman filtering from the perspective of video target tracking quality. The correlation of tracking precision to both the process and measurement noise covariance is investigated; the signal-to-noise power density ratio is defined; the contribution of predicted states and measured outputs to Kalman filter behavior is discussed; the tracking precision relative sensitivity is derived and applied in this study case. The findings are expected to pave the way for future study on how the actual noise statistics deviating from the assumed ones impacts on the Kalman filter optimality and degradation in the application of video tracking.
Statistical-uncertainty-based adaptive filtering of lidar signals
International Nuclear Information System (INIS)
Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.
2000-01-01
An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H 2 O and the N 2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America
An approach for fixed coefficient RNS-based FIR filter
Srinivasa Reddy, Kotha; Sahoo, Subhendu Kumar
2017-08-01
In this work, an efficient new modular multiplication method for {2k-1, 2k, 2k+1-1} moduli set is proposed to implement a residue number system (RNS)-based fixed coefficient finite impulse response filter. The new multiplication approach reduces the number of partial products by using pre-loaded product block. The reduction in partial products with the proposed modular multiplication improves the clock frequency and reduces the area and power as compared with the conventional modular multiplication. Further, the present approach eliminates a binary number to residue number converter circuit, which is usually needed at the front end of RNS-based system. In this work, two fixed coefficient filter architectures with the new modular multiplication approach are proposed. The filters are implemented using Verilog hardware description language. The United Microelectronics Corporation 90 nm technology library has been used for synthesis and the results area, power and delay are obtained with the help of Cadence register transfer level compiler. The power delay product (PDP) is also considered for performance comparison among the proposed filters. One of the proposed architecture is found to improve PDP gain by 60.83% as compared with the filter implemented with conventional modular multiplier. The filters functionality is validated with the help of Altera DSP Builder.
Ferrante, Oscar; Patacca, Alessia; Di Caro, Valeria; Della Libera, Chiara; Santandrea, Elisa; Chelazzi, Leonardo
2018-05-01
The cognitive system has the capacity to learn and make use of environmental regularities - known as statistical learning (SL), including for the implicit guidance of attention. For instance, it is known that attentional selection is biased according to the spatial probability of targets; similarly, changes in distractor filtering can be triggered by the unequal spatial distribution of distractors. Open questions remain regarding the cognitive/neuronal mechanisms underlying SL of target selection and distractor filtering. Crucially, it is unclear whether the two processes rely on shared neuronal machinery, with unavoidable cross-talk, or they are fully independent, an issue that we directly addressed here. In a series of visual search experiments, participants had to discriminate a target stimulus, while ignoring a task-irrelevant salient distractor (when present). We systematically manipulated spatial probabilities of either one or the other stimulus, or both. We then measured performance to evaluate the direct effects of the applied contingent probability distribution (e.g., effects on target selection of the spatial imbalance in target occurrence across locations) as well as its indirect or "transfer" effects (e.g., effects of the same spatial imbalance on distractor filtering across locations). By this approach, we confirmed that SL of both target and distractor location implicitly bias attention. Most importantly, we described substantial indirect effects, with the unequal spatial probability of the target affecting filtering efficiency and, vice versa, the unequal spatial probability of the distractor affecting target selection efficiency across locations. The observed cross-talk demonstrates that SL of target selection and distractor filtering are instantiated via (at least partly) shared neuronal machinery, as further corroborated by strong correlations between direct and indirect effects at the level of individual participants. Our findings are compatible
Raymond L. Czaplewski
2015-01-01
Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of...
International Nuclear Information System (INIS)
Gershgorin, B.; Majda, A.J.
2011-01-01
A statistically exactly solvable model for passive tracers is introduced as a test model for the authors' Nonlinear Extended Kalman Filter (NEKF) as well as other filtering algorithms. The model involves a Gaussian velocity field and a passive tracer governed by the advection-diffusion equation with an imposed mean gradient. The model has direct relevance to engineering problems such as the spread of pollutants in the air or contaminants in the water as well as climate change problems concerning the transport of greenhouse gases such as carbon dioxide with strongly intermittent probability distributions consistent with the actual observations of the atmosphere. One of the attractive properties of the model is the existence of the exact statistical solution. In particular, this unique feature of the model provides an opportunity to design and test fast and efficient algorithms for real-time data assimilation based on rigorous mathematical theory for a turbulence model problem with many active spatiotemporal scales. Here, we extensively study the performance of the NEKF which uses the exact first and second order nonlinear statistics without any approximations due to linearization. The role of partial and sparse observations, the frequency of observations and the observation noise strength in recovering the true signal, its spectrum, and fat tail probability distribution are the central issues discussed here. The results of our study provide useful guidelines for filtering realistic turbulent systems with passive tracers through partial observations.
Argonne National Laboratory - West's approach to filter characterization
International Nuclear Information System (INIS)
Miller, T. A.
1999-01-01
Like other DOE facilities, ANL-W uses a variety of nuclear grade, industrial grade, or furnace-type particulate filters to control airborne radioactivity and hazardous contaminants in radiological containment structures or processes. As designed, these filters entrain and ultimately concentrate contaminants in the media. Toxic metal contaminants include cadmium, chromium, lead; and mercury present in sufficient concentrations to exhibit the hazardous waste characteristic of toxicity as defined in 40 CFR 261.24. Radionuclide contaminants deposited in the media may at times accumulate in sufficient quantity to classify the filter as transuranic or remote-handled waste. Upon their removal from the ventilation system, these particulate filters become wastes, which must be characterized to determine their hazardous and radioactive classifications. A well defined filter characterization process is essential for the proper/consistent waste characterization and minimization and for maintaining personnel radiological exposures as-low-as-reasonably-achievable (ALARA) (1,2). ANL-W has developed an approach to filter sampling and characterization to meet these needs. The ANL-W filter sampling and characterization process is designed to ensure representative sampling and/or process knowledge is utilized in characterizing the filters. The data obtained through sampling and/or process knowledge is used to show compliance with the Resource Conservation and Recovery Act (3) and Treatment/Storage/Disposal Facility Waste Acceptance Criteria. The ANL-W filter characterization involves the collection of process information, filter handling and sampling, sample analysis, data management filter characterization, and waste handling. Each element of the process is streamlined to ensure proper characterization while minimizing radiological exposure to maintenance workers, samplers, laboratory personnel, and waste handlers
International Nuclear Information System (INIS)
Li, Yanwen; Wang, Chao; Gong, Jinfeng
2016-01-01
An accurate battery State of Charge estimation plays an important role in battery electric vehicles. This paper makes two contributions to the existing literature. (1) A recursive least squares method with fuzzy adaptive forgetting factor has been presented to update the model parameters close to the real value more quickly. (2) The statistical information of the innovation sequence obeying chi-square distribution has been introduced to identify model uncertainty, and a novel combination algorithm of strong tracking unscented Kalman filter and adaptive unscented Kalman filter has been developed to estimate SOC (State of Charge). Experimental results indicate that the novel algorithm has a good performance in estimating the battery SOC against initial SOC errors and voltage sensor drift. A comparison with the unscented Kalman filter-based algorithms and adaptive unscented Kalman filter-based algorithms shows that the proposed SOC estimation method has better accuracy, robustness and convergence behavior. - Highlights: • Recursive least squares method with fuzzy adaptive forgetting factor is presented. • The innovation obeying chi-square distribution is used to identify uncertainty. • A combination Karman filter approach for State of Charge estimation is presented. • The performance of the proposed method is verified by comparison results.
Towards a collaborative filtering approach to medication reconciliation.
Hasan, Sharique; Duncan, George T; Neill, Daniel B; Padman, Rema
2008-11-06
A physicians prescribing decisions depend on knowledge of the patients medication list. This knowledge is often incomplete, and errors or omissions could result in adverse outcomes. To address this problem, the Joint Commission recommends medication reconciliation for creating a more accurate list of a patients medications. In this paper, we develop techniques for automatic detection of omissions in medication lists, identifying drugs that the patient may be taking but are not on the patients medication list. Our key insight is that this problem is analogous to the collaborative filtering framework increasingly used by online retailers to recommend relevant products to customers. The collaborative filtering approach enables a variety of solution techniques, including nearest neighbor and co-occurrence approaches. We evaluate the effectiveness of these approaches using medication data from a long-term care center in the Eastern US. Preliminary results suggest that this framework may become a valuable tool for medication reconciliation.
Statistical sampling approaches for soil monitoring
Brus, D.J.
2014-01-01
This paper describes three statistical sampling approaches for regional soil monitoring, a design-based, a model-based and a hybrid approach. In the model-based approach a space-time model is exploited to predict global statistical parameters of interest such as the space-time mean. In the hybrid
A Novel Approach to the Design of Passive Filters in Electric Grids
Filho da Costa Castro, José; Lima, Lucas Ramalho; Belchior, Fernando Nunes; Ribeiro, Paulo Fernando
2016-12-01
The design of shunt passive filters has been a topic of constant research since the 70's. Due to the lower cost, passive shunt filters are still considered a preferred option. This paper presents a novel approach for the placement and sizing of passive filters through ranking solutions based on the minimization of the total harmonic distortion (THDV) of the supply system rather than one specific bus, without neglecting the individual harmonic distortions. The developed method was implemented using Matlab/Simulink and applied to a test system. The results shown that is possible to minimize the total voltage harmonic distortion using a system approach during the filter selection. Additionally, since the method is mainly based on a heurist approach, it avoids the complexity associated with of use of advanced mathematical tools such as artificial intelligence techniques. The analyses contemplate a sinusoidal voltage utility and also the condition with background distortion utility.
Statistical inference an integrated Bayesianlikelihood approach
Aitkin, Murray
2010-01-01
Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing.After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre
Filter multiplexing by use of spatial Code Division Multiple Access approach.
Solomon, Jonathan; Zalevsky, Zeev; Mendlovic, David; Monreal, Javier Garcia
2003-02-10
The increasing popularity of optical communication has also brought a demand for a broader bandwidth. The trend, naturally, was to implement methods from traditional electronic communication. One of the most effective traditional methods is Code Division Multiple Access. In this research, we suggest the use of this approach for spatial coding applied to images. The approach is to multiplex several filters into one plane while keeping their mutual orthogonality. It is shown that if the filters are limited by their bandwidth, the output of all the filters can be sampled in the original image resolution and fully recovered through an all-optical setup. The theoretical analysis of such a setup is verified in an experimental demonstration.
NEW APPROACH TO MODELLING OF SAND FILTER CLOGGING BY SEPTIC TANK EFFLUENT
Directory of Open Access Journals (Sweden)
Jakub Nieć
2016-04-01
Full Text Available The deep bed filtration model elaborated by Iwasaki has many applications, e.g. solids removal from wastewater. Its main parameter, filter coefficient, is directly related to removal efficiency and depends on filter depth and time of operation. In this paper the authors have proposed a new approach to modelling, describing dry organic mass from septic tank effluent and biomass distribution in a sand filter. In this approach the variable filter coefficient value was used as affected by depth and time of operation and the live biomass concentration distribution was approximated by a logistic function. Relatively stable biomass contents in deeper beds compartments were observed in empirical studies. The Iwasaki equations associated with the logistic function can predict volatile suspended solids deposition and biomass content in sand filters. The comparison between the model and empirical data for filtration lasting 10 and 20 days showed a relatively good agreement.
A reduced-order filtering approach for 3D dynamical electrical impedance tomography
International Nuclear Information System (INIS)
Voutilainen, A; Lehikoinen, A; Vauhkonen, M; Kaipio, J P
2011-01-01
Recently, it has been shown that the state estimation approach to process tomography can provide estimates that are significantly better than (a sequence of) conventional stationary snapshot estimates. One of the main obstacles of the adoption of the recursive state estimation algorithms, most commonly different versions of the Kalman filter, is the computational complexity. This is due to both the required large dimension for the state variable and the need to use iterative versions of the Kalman filter in such cases in which there are large contrasts or varying background. In this paper, we propose to use a reduced-order representation for the state variable. In particular, we propose to use the proper orthogonal decomposition-related basis for the state. We consider a simulation study with fluctuating background conductivity, and, in particular, with fluctuating contact impedances. We compare the proposed approach to three different versions of the Kalman filter having different computational complexities. We show that this approach allows the reduction of the dimension of the problem approximately by an order of magnitude and yields essentially as accurate estimates as the most accurate traditional Kalman filter version, the iterated extended Kalman filter
Superresolution restoration of an image sequence: adaptive filtering approach.
Elad, M; Feuer, A
1999-01-01
This paper presents a new method based on adaptive filtering theory for superresolution restoration of continuous image sequences. The proposed methodology suggests least squares (LS) estimators which adapt in time, based on adaptive filters, least mean squares (LMS) or recursive least squares (RLS). The adaptation enables the treatment of linear space and time-variant blurring and arbitrary motion, both of them assumed known. The proposed new approach is shown to be of relatively low computational requirements. Simulations demonstrating the superresolution restoration algorithms are presented.
Energy Technology Data Exchange (ETDEWEB)
Porter, Reid B [Los Alamos National Laboratory; Hush, Don [Los Alamos National Laboratory
2009-01-01
Just as linear models generalize the sample mean and weighted average, weighted order statistic models generalize the sample median and weighted median. This analogy can be continued informally to generalized additive modeels in the case of the mean, and Stack Filters in the case of the median. Both of these model classes have been extensively studied for signal and image processing but it is surprising to find that for pattern classification, their treatment has been significantly one sided. Generalized additive models are now a major tool in pattern classification and many different learning algorithms have been developed to fit model parameters to finite data. However Stack Filters remain largely confined to signal and image processing and learning algorithms for classification are yet to be seen. This paper is a step towards Stack Filter Classifiers and it shows that the approach is interesting from both a theoretical and a practical perspective.
Directory of Open Access Journals (Sweden)
Liang Meng
Full Text Available A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data, but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach--linking statistical, computational, and experimental neuroscience--provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.
A Novel Nonadditive Collaborative-Filtering Approach Using Multicriteria Ratings
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Yi-Chung Hu
2013-01-01
Full Text Available Although single-criterion recommender systems have been successfully used in several applications, multicriteria rating systems which allow users to specify ratings for various content attributes of individual items are gaining importance in recommendation context. An overall rating of an unrated item is often obtained by the weighted average method (WAM when criterion weights are available. However, the assumption of additivity for the WAM is not always reasonable. For this reason, this paper presents a new collaborative-filtering approach using multicriteria ratings, in which a nonadditive technique in Multicriteria decision making (MCDM, namely, the Choquet integral, is used to aggregate multicriteria ratings for unrated items. Subsequently, the system can recommend items with higher overall ratings for each user. The degrees of importance of the respective criteria are determined by a genetic algorithm. In contrast to the additive weighted average aggregation, the Choquet integral does not ignore the interaction among criteria. The applicability of the proposed approach to the recommendation of the initiators on a group-buying website is examined. Experimental results demonstrate that the generalization ability of the proposed approach performs well compared with other similarity-based collaborative-filtering approaches using multicriteria ratings.
Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach
Miran, Sina; Akram, Sahar; Sheikhattar, Alireza; Simon, Jonathan Z.; Zhang, Tao; Babadi, Behtash
2018-01-01
Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach) or vice versa (the encoding approach). To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1) Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2) Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3) Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our proposed
Real-Time Tracking of Selective Auditory Attention From M/EEG: A Bayesian Filtering Approach
Directory of Open Access Journals (Sweden)
Sina Miran
2018-05-01
Full Text Available Humans are able to identify and track a target speaker amid a cacophony of acoustic interference, an ability which is often referred to as the cocktail party phenomenon. Results from several decades of studying this phenomenon have culminated in recent years in various promising attempts to decode the attentional state of a listener in a competing-speaker environment from non-invasive neuroimaging recordings such as magnetoencephalography (MEG and electroencephalography (EEG. To this end, most existing approaches compute correlation-based measures by either regressing the features of each speech stream to the M/EEG channels (the decoding approach or vice versa (the encoding approach. To produce robust results, these procedures require multiple trials for training purposes. Also, their decoding accuracy drops significantly when operating at high temporal resolutions. Thus, they are not well-suited for emerging real-time applications such as smart hearing aid devices or brain-computer interface systems, where training data might be limited and high temporal resolutions are desired. In this paper, we close this gap by developing an algorithmic pipeline for real-time decoding of the attentional state. Our proposed framework consists of three main modules: (1 Real-time and robust estimation of encoding or decoding coefficients, achieved by sparse adaptive filtering, (2 Extracting reliable markers of the attentional state, and thereby generalizing the widely-used correlation-based measures thereof, and (3 Devising a near real-time state-space estimator that translates the noisy and variable attention markers to robust and statistically interpretable estimates of the attentional state with minimal delay. Our proposed algorithms integrate various techniques including forgetting factor-based adaptive filtering, ℓ1-regularization, forward-backward splitting algorithms, fixed-lag smoothing, and Expectation Maximization. We validate the performance of our
Selection vector filter framework
Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.
2003-10-01
We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.
Adaptive particle filter for localization problem in service robotics
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Heilig Alexander
2018-01-01
Full Text Available In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.
Particle filters, a quasi-Monte-Carlo-solution for segmentation of coronaries.
Florin, Charles; Paragios, Nikos; Williams, Jim
2005-01-01
In this paper we propose a Particle Filter-based approach for the segmentation of coronary arteries. To this end, successive planes of the vessel are modeled as unknown states of a sequential process. Such states consist of the orientation, position, shape model and appearance (in statistical terms) of the vessel that are recovered in an incremental fashion, using a sequential Bayesian filter (Particle Filter). In order to account for bifurcations and branchings, we consider a Monte Carlo sampling rule that propagates in parallel multiple hypotheses. Promising results on the segmentation of coronary arteries demonstrate the potential of the proposed approach.
A ROle-Oriented Filtering (ROOF) approach for collaborative recommendation
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.
A statistical approach to plasma profile analysis
International Nuclear Information System (INIS)
Kardaun, O.J.W.F.; McCarthy, P.J.; Lackner, K.; Riedel, K.S.
1990-05-01
A general statistical approach to the parameterisation and analysis of tokamak profiles is presented. The modelling of the profile dependence on both the radius and the plasma parameters is discussed, and pertinent, classical as well as robust, methods of estimation are reviewed. Special attention is given to statistical tests for discriminating between the various models, and to the construction of confidence intervals for the parameterised profiles and the associated global quantities. The statistical approach is shown to provide a rigorous approach to the empirical testing of plasma profile invariance. (orig.)
International Nuclear Information System (INIS)
Harlim, John; Mahdi, Adam; Majda, Andrew J.
2014-01-01
A central issue in contemporary science is the development of nonlinear data driven statistical–dynamical models for time series of noisy partial observations from nature or a complex model. It has been established recently that ad-hoc quadratic multi-level regression models can have finite-time blow-up of statistical solutions and/or pathological behavior of their invariant measure. Recently, a new class of physics constrained nonlinear regression models were developed to ameliorate this pathological behavior. Here a new finite ensemble Kalman filtering algorithm is developed for estimating the state, the linear and nonlinear model coefficients, the model and the observation noise covariances from available partial noisy observations of the state. Several stringent tests and applications of the method are developed here. In the most complex application, the perfect model has 57 degrees of freedom involving a zonal (east–west) jet, two topographic Rossby waves, and 54 nonlinearly interacting Rossby waves; the perfect model has significant non-Gaussian statistics in the zonal jet with blocked and unblocked regimes and a non-Gaussian skewed distribution due to interaction with the other 56 modes. We only observe the zonal jet contaminated by noise and apply the ensemble filter algorithm for estimation. Numerically, we find that a three dimensional nonlinear stochastic model with one level of memory mimics the statistical effect of the other 56 modes on the zonal jet in an accurate fashion, including the skew non-Gaussian distribution and autocorrelation decay. On the other hand, a similar stochastic model with zero memory levels fails to capture the crucial non-Gaussian behavior of the zonal jet from the perfect 57-mode model
An efficient multiple particle filter based on the variational Bayesian approach
Ait-El-Fquih, Boujemaa
2015-12-07
This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.
Bi-variate statistical attribute filtering : A tool for robust detection of faint objects
Teeninga, Paul; Moschini, Ugo; Trager, Scott C.; Wilkinson, M.H.F.
2013-01-01
We present a new method for morphological connected attribute filtering for object detection in astronomical images. In this approach, a threshold is set on one attribute (power), based on its distribution due to noise, as a function of object area. The results show an order of magnitude higher
Digital Path Approach Despeckle Filter for Ultrasound Imaging and Video
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Marek Szczepański
2017-01-01
Full Text Available We propose a novel filtering technique capable of reducing the multiplicative noise in ultrasound images that is an extension of the denoising algorithms based on the concept of digital paths. In this approach, the filter weights are calculated taking into account the similarity between pixel intensities that belongs to the local neighborhood of the processed pixel, which is called a path. The output of the filter is estimated as the weighted average of pixels connected by the paths. The way of creating paths is pivotal and determines the effectiveness and computational complexity of the proposed filtering design. Such procedure can be effective for different types of noise but fail in the presence of multiplicative noise. To increase the filtering efficiency for this type of disturbances, we introduce some improvements of the basic concept and new classes of similarity functions and finally extend our techniques to a spatiotemporal domain. The experimental results prove that the proposed algorithm provides the comparable results with the state-of-the-art techniques for multiplicative noise removal in ultrasound images and it can be applied for real-time image enhancement of video streams.
Gas Path Health Monitoring for a Turbofan Engine Based on a Nonlinear Filtering Approach
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Yiqiu Lv
2013-01-01
Full Text Available Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonlinear and non-Gaussian problems. Its mathematical formulation is based on the assumption that the probability density function (PDF of the state vector can be approximated to be Gaussian. Recent investigations have focused on the particle filter (PF based on Monte Carlo sampling algorithms for tackling strong nonlinear and non-Gaussian models. Considering the aircraft engine is a complicated machine, operating under a harsh environment, and polluted by complex noises, the PF might be an available way to monitor gas path health for aircraft engines. Up to this point in time a number of Kalman filtering approaches have been used for aircraft turbofan engine gas path health estimation, but the particle filters have not been used for this purpose and a systematic comparison has not been published. This paper presents gas path health monitoring based on the PF and the constrained extend Kalman particle filter (cEKPF, and then compares the estimation accuracy and computational effort of these filters to the EKF for aircraft engine performance estimation under rapid faults and general deterioration. Finally, the effects of the constraint mechanism and particle number on the cEKPF are discussed. We show in this paper that the cEKPF outperforms the EKF, PF and EKPF, and conclude that the cEKPF is the best choice for turbofan engine health monitoring.
Zhou, Anran; Xie, Weixin; Pei, Jihong
2018-06-01
Accurate detection of maritime targets in infrared imagery under various sea clutter conditions is always a challenging task. The fractional Fourier transform (FRFT) is the extension of the Fourier transform in the fractional order, and has richer spatial-frequency information. By combining it with the high order statistic filtering, a new ship detection method is proposed. First, the proper range of angle parameter is determined to make it easier for the ship components and background to be separated. Second, a new high order statistic curve (HOSC) at each fractional frequency point is designed. It is proved that maximal peak interval in HOSC reflects the target information, while the points outside the interval reflect the background. And the value of HOSC relative to the ship is much bigger than that to the sea clutter. Then, search the curve's maximal target peak interval and extract the interval by bandpass filtering in fractional Fourier domain. The value outside the peak interval of HOSC decreases rapidly to 0, so the background is effectively suppressed. Finally, the detection result is obtained by the double threshold segmenting and the target region selection method. The results show the proposed method is excellent for maritime targets detection with high clutters.
Artificial intelligence approaches in statistics
International Nuclear Information System (INIS)
Phelps, R.I.; Musgrove, P.B.
1986-01-01
The role of pattern recognition and knowledge representation methods from Artificial Intelligence within statistics is considered. Two areas of potential use are identified and one, data exploration, is used to illustrate the possibilities. A method is presented to identify and separate overlapping groups within cluster analysis, using an AI approach. The potential of such ''intelligent'' approaches is stressed
Statistical analysis and Kalman filtering applied to nuclear materials accountancy
International Nuclear Information System (INIS)
Annibal, P.S.
1990-08-01
Much theoretical research has been carried out on the development of statistical methods for nuclear material accountancy. In practice, physical, financial and time constraints mean that the techniques must be adapted to give an optimal performance in plant conditions. This thesis aims to bridge the gap between theory and practice, to show the benefits to be gained from a knowledge of the facility operation. Four different aspects are considered; firstly, the use of redundant measurements to reduce the error on the estimate of the mass of heavy metal in an 'accountancy tank' is investigated. Secondly, an analysis of the calibration data for the same tank is presented, establishing bounds for the error and suggesting a means of reducing them. Thirdly, a plant-specific method of producing an optimal statistic from the input, output and inventory data, to help decide between 'material loss' and 'no loss' hypotheses, is developed and compared with existing general techniques. Finally, an application of the Kalman Filter to materials accountancy is developed, to demonstrate the advantages of state-estimation techniques. The results of the analyses and comparisons illustrate the importance of taking into account a complete and accurate knowledge of the plant operation, measurement system, and calibration methods, to derive meaningful results from statistical tests on materials accountancy data, and to give a better understanding of critical random and systematic error sources. The analyses were carried out on the head-end of the Fast Reactor Reprocessing Plant, where fuel from the prototype fast reactor is cut up and dissolved. However, the techniques described are general in their application. (author)
Piretzidis, Dimitrios; Sideris, Michael G.
2017-09-01
Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be
Generalized Selection Weighted Vector Filters
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Rastislav Lukac
2004-09-01
Full Text Available This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03 in Grado, Italy.
A unified approach for suppressing sidelobes arising in the spectral response of rugate filters
International Nuclear Information System (INIS)
Abo-Zahhad, M.; Bataineh, M.
2000-01-01
This paper suggests a universal approach to reduce the side lobes which usually appear at both sides of a stop band of a ru gate filter. Both quin tic matching layers and anodization functions are to used to improve the filter's response. The proposed technique could be used to control the ripples level by properly choosing the refractive index profile after amending it to include mat aching layers and/or modulating its profile with a slowly varying anodization (or ta perine) function. Two illustrative examples are given to demonstrate the robustness of the proposed technique. The given examples suggest that combining both effects on the index of refraction profile lead to the lowest possible ripple level. A multichannel filter response is obtained by wavelet cons traction of the refractive index profile with potential applications in multimode lasers and wavelength division multiple xin networks. The obtained results demonstrate the applicability of the adopted approach to design ripple free ru gate filters. The extension to stack filters and other wave guiding structures are also visible. (authors). 14 refs., 8 figs
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A Francina Webster
Full Text Available Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD values derived from toxicogenomics data be used as point of departure (PoD values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd and carcinogenic (4, 8 mkd doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses.
Ghumare, Eshwar; Schrooten, Maarten; Vandenberghe, Rik; Dupont, Patrick
2015-08-01
Kalman filter approaches are widely applied to derive time varying effective connectivity from electroencephalographic (EEG) data. For multi-trial data, a classical Kalman filter (CKF) designed for the estimation of single trial data, can be implemented by trial-averaging the data or by averaging single trial estimates. A general linear Kalman filter (GLKF) provides an extension for multi-trial data. In this work, we studied the performance of the different Kalman filtering approaches for different values of signal-to-noise ratio (SNR), number of trials and number of EEG channels. We used a simulated model from which we calculated scalp recordings. From these recordings, we estimated cortical sources. Multivariate autoregressive model parameters and partial directed coherence was calculated for these estimated sources and compared with the ground-truth. The results showed an overall superior performance of GLKF except for low levels of SNR and number of trials.
A Filtering Approach for Image-Guided Surgery With a Highly Articulated Surgical Snake Robot.
Tully, Stephen; Choset, Howie
2016-02-01
The objective of this paper is to introduce a probabilistic filtering approach to estimate the pose and internal shape of a highly flexible surgical snake robot during minimally invasive surgery. Our approach renders a depiction of the robot that is registered to preoperatively reconstructed organ models to produce a 3-D visualization that can be used for surgical feedback. Our filtering method estimates the robot shape using an extended Kalman filter that fuses magnetic tracker data with kinematic models that define the motion of the robot. Using Lie derivative analysis, we show that this estimation problem is observable, and thus, the shape and configuration of the robot can be successfully recovered with a sufficient number of magnetic tracker measurements. We validate this study with benchtop and in-vivo image-guidance experiments in which the surgical robot was driven along the epicardial surface of a porcine heart. This paper introduces a filtering approach for shape estimation that can be used for image guidance during minimally invasive surgery. The methods being introduced in this paper enable informative image guidance for highly articulated surgical robots, which benefits the advancement of robotic surgery.
A filtering approach to image reconstruction in 3D SPECT
International Nuclear Information System (INIS)
Bronnikov, Andrei V.
2000-01-01
We present a new approach to three-dimensional (3D) image reconstruction using analytical inversion of the exponential divergent beam transform, which can serve as a mathematical model for cone-beam 3D SPECT imaging. We apply a circular cone-beam scan and assume constant attenuation inside a convex area with a known boundary, which is satisfactory in brain imaging. The reconstruction problem is reduced to an image restoration problem characterized by a shift-variant point spread function which is given analytically. The method requires two computation steps: backprojection and filtering. The modulation transfer function (MTF) of the filter is derived by means of an original methodology using the 2D Laplace transform. The filter is implemented in the frequency domain and requires 2D Fourier transform of transverse slices. In order to obtain a shift-invariant cone-beam projection-backprojection operator we resort to an approximation, assuming that the collimator has a relatively large focal length. Nevertheless, numerical experiments demonstrate surprisingly good results for detectors with relatively short focal lengths. The use of a wavelet-based filtering algorithm greatly improves the stability to Poisson noise. (author)
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
International Nuclear Information System (INIS)
Zhang, Yan; Tang, Baoping; Chen, Rengxiang; Liu, Ziran
2016-01-01
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
A statistical approach to instrument calibration
Robert R. Ziemer; David Strauss
1978-01-01
Summary - It has been found that two instruments will yield different numerical values when used to measure identical points. A statistical approach is presented that can be used to approximate the error associated with the calibration of instruments. Included are standard statistical tests that can be used to determine if a number of successive calibrations of the...
Permutation statistical methods an integrated approach
Berry, Kenneth J; Johnston, Janis E
2016-01-01
This research monograph provides a synthesis of a number of statistical tests and measures, which, at first consideration, appear disjoint and unrelated. Numerous comparisons of permutation and classical statistical methods are presented, and the two methods are compared via probability values and, where appropriate, measures of effect size. Permutation statistical methods, compared to classical statistical methods, do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This text takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field in statistics. This topic is new in that it took modern computing power to make permutation methods available to people working in the mainstream of research. This research monograph addresses a statistically-informed audience, and can also easily serve as a ...
An efficient multiple particle filter based on the variational Bayesian approach
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2015-01-01
) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs
Tine after tine: a varied approach to the removal of a long-standing IVC filter
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Andrew Delozier
2017-06-01
Full Text Available Inferior vena cava filters are important tools used to help prevent life-threatening pulmonary embolisms in hospitalized patients with contraindications to pharmacological prophylactic anticoagulation. This is a case report of a patient who had an inferior vena cava filter placed after a traumatic subdural hematoma. He made a complete recovery but was lost to follow-up until he presented 1825 days after filter deployment with abdominal pain discovered to be from penetration of the filter tines outside the lumen and into adjacent structures. We describe a case complicated by fibrotic tine entrapment with penetration to surrounding structures and discuss the technical approach used to free and eventually remove the long-standing filter.
Institute of Scientific and Technical Information of China (English)
Xiaogu ZHENG
2009-01-01
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.
Hadwin, Paul J; Peterson, Sean D
2017-04-01
The Bayesian framework for parameter inference provides a basis from which subject-specific reduced-order vocal fold models can be generated. Previously, it has been shown that a particle filter technique is capable of producing estimates and associated credibility intervals of time-varying reduced-order vocal fold model parameters. However, the particle filter approach is difficult to implement and has a high computational cost, which can be barriers to clinical adoption. This work presents an alternative estimation strategy based upon Kalman filtering aimed at reducing the computational cost of subject-specific model development. The robustness of this approach to Gaussian and non-Gaussian noise is discussed. The extended Kalman filter (EKF) approach is found to perform very well in comparison with the particle filter technique at dramatically lower computational cost. Based upon the test cases explored, the EKF is comparable in terms of accuracy to the particle filter technique when greater than 6000 particles are employed; if less particles are employed, the EKF actually performs better. For comparable levels of accuracy, the solution time is reduced by 2 orders of magnitude when employing the EKF. By virtue of the approximations used in the EKF, however, the credibility intervals tend to be slightly underpredicted.
Foussier, Jerome; Teichmann, Daniel; Jia, Jing; Misgeld, Berno; Leonhardt, Steffen
2014-05-09
Extracting cardiorespiratory signals from non-invasive and non-contacting sensor arrangements, i.e. magnetic induction sensors, is a challenging task. The respiratory and cardiac signals are mixed on top of a large and time-varying offset and are likely to be disturbed by measurement noise. Basic filtering techniques fail to extract relevant information for monitoring purposes. We present a real-time filtering system based on an adaptive Kalman filter approach that separates signal offsets, respiratory and heart signals from three different sensor channels. It continuously estimates respiration and heart rates, which are fed back into the system model to enhance performance. Sensor and system noise covariance matrices are automatically adapted to the aimed application, thus improving the signal separation capabilities. We apply the filtering to two different subjects with different heart rates and sensor properties and compare the results to the non-adaptive version of the same Kalman filter. Also, the performance, depending on the initialization of the filters, is analyzed using three different configurations ranging from best to worst case. Extracted data are compared with reference heart rates derived from a standard pulse-photoplethysmographic sensor and respiration rates from a flowmeter. In the worst case for one of the subjects the adaptive filter obtains mean errors (standard deviations) of -0.2 min(-1) (0.3 min(-1)) and -0.7 bpm (1.7 bpm) (compared to -0.2 min(-1) (0.4 min(-1)) and 42.0 bpm (6.1 bpm) for the non-adaptive filter) for respiration and heart rate, respectively. In bad conditions the heart rate is only correctly measurable when the Kalman matrices are adapted to the target sensor signals. Also, the reduced mean error between the extracted offset and the raw sensor signal shows that adapting the Kalman filter continuously improves the ability to separate the desired signals from the raw sensor data. The average total computational time needed
Functional integral approach to classical statistical dynamics
International Nuclear Information System (INIS)
Jensen, R.V.
1980-04-01
A functional integral method is developed for the statistical solution of nonlinear stochastic differential equations which arise in classical dynamics. The functional integral approach provides a very natural and elegant derivation of the statistical dynamical equations that have been derived using the operator formalism of Martin, Siggia, and Rose
IAE-adaptive Kalman filter for INS/GPS integrated navigation system
Institute of Scientific and Technical Information of China (English)
Bian Hongwei; Jin Zhihua; Tian Weifeng
2006-01-01
A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.
Statistical Data Processing with R – Metadata Driven Approach
Directory of Open Access Journals (Sweden)
Rudi SELJAK
2016-06-01
Full Text Available In recent years the Statistical Office of the Republic of Slovenia has put a lot of effort into re-designing its statistical process. We replaced the classical stove-pipe oriented production system with general software solutions, based on the metadata driven approach. This means that one general program code, which is parametrized with process metadata, is used for data processing for a particular survey. Currently, the general program code is entirely based on SAS macros, but in the future we would like to explore how successfully statistical software R can be used for this approach. Paper describes the metadata driven principle for data validation, generic software solution and main issues connected with the use of statistical software R for this approach.
Accounting for model error due to unresolved scales within ensemble Kalman filtering
Mitchell, Lewis; Carrassi, Alberto
2014-01-01
We propose a method to account for model error due to unresolved scales in the context of the ensemble transform Kalman filter (ETKF). The approach extends to this class of algorithms the deterministic model error formulation recently explored for variational schemes and extended Kalman filter. The model error statistic required in the analysis update is estimated using historical reanalysis increments and a suitable model error evolution law. Two different versions of the method are describe...
Lee, K. David; Wiesenfeld, Eric; Gelfand, Andrew
2007-04-01
One of the greatest challenges in modern combat is maintaining a high level of timely Situational Awareness (SA). In many situations, computational complexity and accuracy considerations make the development and deployment of real-time, high-level inference tools very difficult. An innovative hybrid framework that combines Bayesian inference, in the form of Bayesian Networks, and Possibility Theory, in the form of Fuzzy Logic systems, has recently been introduced to provide a rigorous framework for high-level inference. In previous research, the theoretical basis and benefits of the hybrid approach have been developed. However, lacking is a concrete experimental comparison of the hybrid framework with traditional fusion methods, to demonstrate and quantify this benefit. The goal of this research, therefore, is to provide a statistical analysis on the comparison of the accuracy and performance of hybrid network theory, with pure Bayesian and Fuzzy systems and an inexact Bayesian system approximated using Particle Filtering. To accomplish this task, domain specific models will be developed under these different theoretical approaches and then evaluated, via Monte Carlo Simulation, in comparison to situational ground truth to measure accuracy and fidelity. Following this, a rigorous statistical analysis of the performance results will be performed, to quantify the benefit of hybrid inference to other fusion tools.
A statistical mechanical approach to restricted integer partition functions
Zhou, Chi-Chun; Dai, Wu-Sheng
2018-05-01
The main aim of this paper is twofold: (1) suggesting a statistical mechanical approach to the calculation of the generating function of restricted integer partition functions which count the number of partitions—a way of writing an integer as a sum of other integers under certain restrictions. In this approach, the generating function of restricted integer partition functions is constructed from the canonical partition functions of various quantum gases. (2) Introducing a new type of restricted integer partition functions corresponding to general statistics which is a generalization of Gentile statistics in statistical mechanics; many kinds of restricted integer partition functions are special cases of this restricted integer partition function. Moreover, with statistical mechanics as a bridge, we reveal a mathematical fact: the generating function of restricted integer partition function is just the symmetric function which is a class of functions being invariant under the action of permutation groups. Using this approach, we provide some expressions of restricted integer partition functions as examples.
Energy-Efficient Distributed Filtering in Sensor Networks: A Unified Switched System Approach.
Zhang, Dan; Shi, Peng; Zhang, Wen-An; Yu, Li
2016-04-21
This paper is concerned with the energy-efficient distributed filtering in sensor networks, and a unified switched system approach is proposed to achieve this goal. For the system under study, the measurement is first sampled under nonuniform sampling periods, then the local measurement elements are selected and quantized for transmission. Then, the transmission rate is further reduced to save constrained power in sensors. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction, the signal quantization, and the measurement missing phenomena. Sufficient conditions are obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H∞ performance level. Both simulation and experiment studies are given to show the effectiveness of the proposed new design technique.
Parametric statistical inference basic theory and modern approaches
Zacks, Shelemyahu; Tsokos, C P
1981-01-01
Parametric Statistical Inference: Basic Theory and Modern Approaches presents the developments and modern trends in statistical inference to students who do not have advanced mathematical and statistical preparation. The topics discussed in the book are basic and common to many fields of statistical inference and thus serve as a jumping board for in-depth study. The book is organized into eight chapters. Chapter 1 provides an overview of how the theory of statistical inference is presented in subsequent chapters. Chapter 2 briefly discusses statistical distributions and their properties. Chapt
Nonparametric statistics a step-by-step approach
Corder, Gregory W
2014-01-01
"…a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught."" -CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical powerSPSS® (Version 21) software and updated screen ca
Observation Quality Control with a Robust Ensemble Kalman Filter
Roh, Soojin
2013-12-01
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Observation Quality Control with a Robust Ensemble Kalman Filter
Roh, Soojin; Genton, Marc G.; Jun, Mikyoung; Szunyogh, Istvan; Hoteit, Ibrahim
2013-01-01
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Statistical analysis and digital processing of the Mössbauer spectra
International Nuclear Information System (INIS)
Prochazka, Roman; Tucek, Jiri; Mashlan, Miroslav; Pechousek, Jiri; Tucek, Pavel; Marek, Jaroslav
2010-01-01
This work is focused on using the statistical methods and development of the filtration procedures for signal processing in Mössbauer spectroscopy. Statistical tools for noise filtering in the measured spectra are used in many scientific areas. The use of a pure statistical approach in accumulated Mössbauer spectra filtration is described. In Mössbauer spectroscopy, the noise can be considered as a Poisson statistical process with a Gaussian distribution for high numbers of observations. This noise is a superposition of the non-resonant photons counting with electronic noise (from γ-ray detection and discrimination units), and the velocity system quality that can be characterized by the velocity nonlinearities. The possibility of a noise-reducing process using a new design of statistical filter procedure is described. This mathematical procedure improves the signal-to-noise ratio and thus makes it easier to determine the hyperfine parameters of the given Mössbauer spectra. The filter procedure is based on a periodogram method that makes it possible to assign the statistically important components in the spectral domain. The significance level for these components is then feedback-controlled using the correlation coefficient test results. The estimation of the theoretical correlation coefficient level which corresponds to the spectrum resolution is performed. Correlation coefficient test is based on comparison of the theoretical and the experimental correlation coefficients given by the Spearman method. The correctness of this solution was analyzed by a series of statistical tests and confirmed by many spectra measured with increasing statistical quality for a given sample (absorber). The effect of this filter procedure depends on the signal-to-noise ratio and the applicability of this method has binding conditions
Statistical analysis and digital processing of the Mössbauer spectra
Prochazka, Roman; Tucek, Pavel; Tucek, Jiri; Marek, Jaroslav; Mashlan, Miroslav; Pechousek, Jiri
2010-02-01
This work is focused on using the statistical methods and development of the filtration procedures for signal processing in Mössbauer spectroscopy. Statistical tools for noise filtering in the measured spectra are used in many scientific areas. The use of a pure statistical approach in accumulated Mössbauer spectra filtration is described. In Mössbauer spectroscopy, the noise can be considered as a Poisson statistical process with a Gaussian distribution for high numbers of observations. This noise is a superposition of the non-resonant photons counting with electronic noise (from γ-ray detection and discrimination units), and the velocity system quality that can be characterized by the velocity nonlinearities. The possibility of a noise-reducing process using a new design of statistical filter procedure is described. This mathematical procedure improves the signal-to-noise ratio and thus makes it easier to determine the hyperfine parameters of the given Mössbauer spectra. The filter procedure is based on a periodogram method that makes it possible to assign the statistically important components in the spectral domain. The significance level for these components is then feedback-controlled using the correlation coefficient test results. The estimation of the theoretical correlation coefficient level which corresponds to the spectrum resolution is performed. Correlation coefficient test is based on comparison of the theoretical and the experimental correlation coefficients given by the Spearman method. The correctness of this solution was analyzed by a series of statistical tests and confirmed by many spectra measured with increasing statistical quality for a given sample (absorber). The effect of this filter procedure depends on the signal-to-noise ratio and the applicability of this method has binding conditions.
A new method for E-government procurement using collaborative filtering and Bayesian approach.
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
Directory of Open Access Journals (Sweden)
Shuai Zhang
2013-01-01
Full Text Available 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 Statistical Approach to Optimizing Concrete Mixture Design
Ahmad, Shamsad; Alghamdi, Saeid A.
2014-01-01
A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33). A total of 27 concrete mixtures with three replicate...
Condition Monitoring of a Process Filter Applying Wireless Vibration Analysis
Directory of Open Access Journals (Sweden)
Pekka KOSKELA
2011-05-01
Full Text Available This paper presents a novel wireless vibration-based method for monitoring the degree of feed filter clogging. In process industry, these filters are applied to prevent impurities entering the process. During operation, the filters gradually become clogged, decreasing the feed flow and, in the worst case, preventing it. The cleaning of the filter should therefore be carried out predictively in order to avoid equipment damage and unnecessary process downtime. The degree of clogging is estimated by first calculating the time domain indices from low frequency accelerometer samples and then taking the median of the processed values. Nine different statistical quantities are compared based on the estimation accuracy and criteria for operating in resource-constrained environments with particular focus on energy efficiency. The initial results show that the method is able to detect the degree of clogging, and the approach may be applicable to filter clogging monitoring.
Blended particle filters for large-dimensional chaotic dynamical systems
Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.
2014-01-01
A major challenge in contemporary data science is the development of statistically accurate particle filters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particle filters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particle filters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particle filter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886
A Kalman filter approach for the determination of celestial reference frames
Soja, Benedikt; Gross, Richard; Jacobs, Christopher; Chin, Toshio; Karbon, Maria; Nilsson, Tobias; Heinkelmann, Robert; Schuh, Harald
2017-04-01
The coordinate model of radio sources in International Celestial Reference Frames (ICRF), such as the ICRF2, has traditionally been a constant offset. While sufficient for a large part of radio sources considering current accuracy requirements, several sources exhibit significant temporal coordinate variations. In particular, the group of the so-called special handling sources is characterized by large fluctuations in the source positions. For these sources and for several from the "others" category of radio sources, a coordinate model that goes beyond a constant offset would be beneficial. However, due to the sheer amount of radio sources in catalogs like the ICRF2, and even more so with the upcoming ICRF3, it is difficult to find the most appropriate coordinate model for every single radio source. For this reason, we have developed a time series approach to the determination of celestial reference frames (CRF). We feed the radio source coordinates derived from single very long baseline interferometry (VLBI) sessions sequentially into a Kalman filter and smoother, retaining their full covariances. The estimation of the source coordinates is carried out with a temporal resolution identical to the input data, i.e. usually 1-4 days. The coordinates are assumed to behave like random walk processes, an assumption which has already successfully been made for the determination of terrestrial reference frames such as the JTRF2014. To be able to apply the most suitable process noise value for every single radio source, their statistical properties are analyzed by computing their Allan standard deviations (ADEV). Additional to the determination of process noise values, the ADEV allows drawing conclusions whether the variations in certain radio source positions significantly deviate from random walk processes. Our investigations also deal with other means of source characterization, such as the structure index, in order to derive a suitable process noise model. The Kalman
On robust signal reconstruction in noisy filter banks
Vikalo, H; Hassibi, B; Kailath, T; 10.1016/j.sigpro.2004.08.011
2005-01-01
We study the design of synthesis filters in noisy filter bank systems using an H/sup infinity / estimation point of view. The H/sup infinity / approach is most promising in situations where the statistical properties of the disturbances (arising from quantization, compression, etc.) in each subband of the filter bank is unknown, or is too difficult to model and analyze. For the important special case of unitary analysis polyphase matrices we obtain an explicit expression for the minimum achievable disturbance attenuation. For arbitrary analysis polyphase matrices, standard state-space H/sup infinity / techniques can be employed to obtain numerical solutions. When the synthesis filters are restricted to being FIR, as is often the case in practice, the design can be cast as a finite-dimensional semi-definite program. In this case, we can effectively exploit the inherent non-uniqueness of the H/sup infinity / solution to optimize for an additional criteria. By optimizing for average performance in addition to th...
Advances in statistical multisource-multitarget information fusion
Mahler, Ronald PS
2014-01-01
This is the sequel to the 2007 Artech House bestselling title, Statistical Multisource-Multitarget Information Fusion. That earlier book was a comprehensive resource for an in-depth understanding of finite-set statistics (FISST), a unified, systematic, and Bayesian approach to information fusion. The cardinalized probability hypothesis density (CPHD) filter, which was first systematically described in the earlier book, has since become a standard multitarget detection and tracking technique, especially in research and development.Since 2007, FISST has inspired a considerable amount of research
Filtering Non-Linear Transfer Functions on Surfaces.
Heitz, Eric; Nowrouzezahrai, Derek; Poulin, Pierre; Neyret, Fabrice
2014-07-01
Applying non-linear transfer functions and look-up tables to procedural functions (such as noise), surface attributes, or even surface geometry are common strategies used to enhance visual detail. Their simplicity and ability to mimic a wide range of realistic appearances have led to their adoption in many rendering problems. As with any textured or geometric detail, proper filtering is needed to reduce aliasing when viewed across a range of distances, but accurate and efficient transfer function filtering remains an open problem for several reasons: transfer functions are complex and non-linear, especially when mapped through procedural noise and/or geometry-dependent functions, and the effects of perspective and masking further complicate the filtering over a pixel's footprint. We accurately solve this problem by computing and sampling from specialized filtering distributions on the fly, yielding very fast performance. We investigate the case where the transfer function to filter is a color map applied to (macroscale) surface textures (like noise), as well as color maps applied according to (microscale) geometric details. We introduce a novel representation of a (potentially modulated) color map's distribution over pixel footprints using Gaussian statistics and, in the more complex case of high-resolution color mapped microsurface details, our filtering is view- and light-dependent, and capable of correctly handling masking and occlusion effects. Our approach can be generalized to filter other physical-based rendering quantities. We propose an application to shading with irradiance environment maps over large terrains. Our framework is also compatible with the case of transfer functions used to warp surface geometry, as long as the transformations can be represented with Gaussian statistics, leading to proper view- and light-dependent filtering results. Our results match ground truth and our solution is well suited to real-time applications, requires only a few
Stochastic global optimization as a filtering problem
International Nuclear Information System (INIS)
Stinis, Panos
2012-01-01
We present a reformulation of stochastic global optimization as a filtering problem. The motivation behind this reformulation comes from the fact that for many optimization problems we cannot evaluate exactly the objective function to be optimized. Similarly, we may not be able to evaluate exactly the functions involved in iterative optimization algorithms. For example, we may only have access to noisy measurements of the functions or statistical estimates provided through Monte Carlo sampling. This makes iterative optimization algorithms behave like stochastic maps. Naive global optimization amounts to evolving a collection of realizations of this stochastic map and picking the realization with the best properties. This motivates the use of filtering techniques to allow focusing on realizations that are more promising than others. In particular, we present a filtering reformulation of global optimization in terms of a special case of sequential importance sampling methods called particle filters. The increasing popularity of particle filters is based on the simplicity of their implementation and their flexibility. We utilize the flexibility of particle filters to construct a stochastic global optimization algorithm which can converge to the optimal solution appreciably faster than naive global optimization. Several examples of parametric exponential density estimation are provided to demonstrate the efficiency of the approach.
GPR Raw-Data Order Statistic Filtering and Split-Spectrum Processing to Detect Moisture
Directory of Open Access Journals (Sweden)
Gokhan Kilic
2014-05-01
Full Text Available Considerable research into the area of bridge health monitoring has been undertaken; however, information is still lacking on the effects of certain defects, such as moisture ingress, on the results of ground penetrating radar (GPR surveying. In this paper, this issue will be addressed by examining the results of a GPR bridge survey, specifically the effect of moisture in the predicted position of the rebars. It was found that moisture ingress alters the radargram to indicate distortion or skewing of the steel reinforcements, when in fact destructive testing was able to confirm that no such distortion or skewing had occurred. Additionally, split-spectrum processing with order statistic filters was utilized to detect moisture ingress from the GPR raw data.
Adaptive kernels in approximate filtering of state-space models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
2017-01-01
Roč. 31, č. 6 (2017), s. 938-952 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : filtering * nonlinear filters * Bayesian filtering * sequential Monte Carlo * approximate filtering Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.708, year: 2016 http://library.utia.cs.cz/separaty/2016/AS/dedecius-0466448.pdf
A robust approach to optimal matched filter design in ultrasonic non-destructive evaluation (NDE)
Li, Minghui; Hayward, Gordon
2017-02-01
The matched filter was demonstrated to be a powerful yet efficient technique to enhance defect detection and imaging in ultrasonic non-destructive evaluation (NDE) of coarse grain materials, provided that the filter was properly designed and optimized. In the literature, in order to accurately approximate the defect echoes, the design utilized the real excitation signals, which made it time consuming and less straightforward to implement in practice. In this paper, we present a more robust and flexible approach to optimal matched filter design using the simulated excitation signals, and the control parameters are chosen and optimized based on the real scenario of array transducer, transmitter-receiver system response, and the test sample, as a result, the filter response is optimized and depends on the material characteristics. Experiments on industrial samples are conducted and the results confirm the great benefits of the method.
A novel approach to achieving modular retrovirus clearance for a parvovirus filter.
Stuckey, Juliana; Strauss, Daniel; Venkiteshwaran, Adith; Gao, Jinxin; Luo, Wen; Quertinmont, Michelle; O'Donnell, Sean; Chen, Dayue
2014-01-01
Viral filtration is routinely incorporated into the downstream purification processes for the production of biologics produced in mammalian cell cultures (MCC) to remove potential viral contaminants. In recent years, the use of retentive filters designed for retaining parvovirus (~20 nm) has become an industry standard in a conscious effort to further improve product safety. Since retentive filters remove viruses primarily by the size exclusion mechanism, it is expected that filters designed for parvovirus removal can effectively clear larger viruses such as retroviruses (~100 nm). In an attempt to reduce the number of viral clearance studies, we have taken a novel approach to demonstrate the feasibility of claiming modular retrovirus clearance for Asahi Planova 20N filters. Porcine parvovirus (PPV) and xenotropic murine leukemia virus (XMuLV) were co-spiked into six different feedstreams and then subjected to laboratory scale Planova 20N filtration. Our results indicate that Planova 20N filters consistently retain retroviruses and no retrovirus has ever been detected in the filtrates even when significant PPV breakthrough is observed. Based on the data from multiple in-house viral validation studies and the results from the co-spiking experiments, we have successfully claimed a modular retrovirus clearance of greater than 6 log10 reduction factors (LRF) to support clinical trial applications in both USA and Europe. © 2013 American Institute of Chemical Engineers.
International Nuclear Information System (INIS)
Scolnic, Daniel M.; Riess, Adam G.; Huber, Mark E.; Rest, Armin; Stubbs, Christoper W.; Tonry, John L.
2009-01-01
Large photometric surveys of transient phenomena, such as Panoramic Survey Telescope and Rapid Response System and Large Synoptic Survey Telescope, will locate thousands to millions of Type Ia supernova (SN Ia) candidates per year, a rate prohibitive for acquiring spectroscopy to determine each candidate's type and redshift. In response, we have developed an economical approach to identifying SNe Ia and their redshifts using an uncommon type of optical filter which has multiple, discontinuous passbands on a single substrate. Observation of a supernova through a specially designed pair of these 'cross-correlation filters' measures the approximate amplitude and phase of the cross-correlation between the spectrum and a SN Ia template, a quantity typically used to determine the redshift and type of a high-redshift SN Ia. Simulating the use of these filters, we obtain a sample of SNe Ia which is ∼98% pure with individual redshifts measured to σ z = 0.01 precision. The advantages of this approach over standard broadband photometric methods are that it is insensitive to reddening, independent of the color data used for subsequent distance determinations which reduce selection or interpretation bias, and because it makes use of the spectral features its reliability is greater. A great advantage over long-slit spectroscopy comes from increased throughput, enhanced multiplexing, and reduced setup time resulting in a net gain in speed of up to ∼30 times. This approach is also insensitive to host galaxy contamination. Prototype filters were built and successfully used on Magellan with LDSS-3 to characterize three SuperNova Legacy Survey candidates. We discuss how these filters can provide critical information for the upcoming photometric supernova surveys.
Mauldin, F William; Lin, Dan; Hossack, John A
2011-11-01
A general filtering method, called the singular value filter (SVF), is presented as a framework for principal component analysis (PCA) based filter design in medical ultrasound imaging. The SVF approach operates by projecting the original data onto a new set of bases determined from PCA using singular value decomposition (SVD). The shape of the SVF weighting function, which relates the singular value spectrum of the input data to the filtering coefficients assigned to each basis function, is designed in accordance with a signal model and statistical assumptions regarding the underlying source signals. In this paper, we applied SVF for the specific application of clutter artifact rejection in diagnostic ultrasound imaging. SVF was compared to a conventional PCA-based filtering technique, which we refer to as the blind source separation (BSS) method, as well as a simple frequency-based finite impulse response (FIR) filter used as a baseline for comparison. The performance of each filter was quantified in simulated lesion images as well as experimental cardiac ultrasound data. SVF was demonstrated in both simulation and experimental results, over a wide range of imaging conditions, to outperform the BSS and FIR filtering methods in terms of contrast-to-noise ratio (CNR) and motion tracking performance. In experimental mouse heart data, SVF provided excellent artifact suppression with an average CNR improvement of 1.8 dB with over 40% reduction in displacement tracking error. It was further demonstrated from simulation and experimental results that SVF provided superior clutter rejection, as reflected in larger CNR values, when filtering was achieved using complex pulse-echo received data and non-binary filter coefficients.
A novel particle filter approach for indoor positioning by fusing WiFi and inertial sensors
Directory of Open Access Journals (Sweden)
Zhu Nan
2015-12-01
Full Text Available WiFi fingerprinting is the method of recording WiFi signal strength from access points (AP along with the positions at which they were recorded, and later matching those to new measurements for indoor positioning. Inertial positioning utilizes the accelerometer and gyroscopes for pedestrian positioning. However, both methods have their limitations, such as the WiFi fluctuations and the accumulative error of inertial sensors. Usually, the filtering method is used for integrating the two approaches to achieve better location accuracy. In the real environments, especially in the indoor field, the APs could be sparse and short range. To overcome the limitations, a novel particle filter approach based on Rao Blackwellized particle filter (RBPF is presented in this paper. The indoor environment is divided into several local maps, which are assumed to be independent of each other. The local areas are estimated by the local particle filter, whereas the global areas are combined by the global particle filter. The algorithm has been investigated by real field trials using a WiFi tablet on hand with an inertial sensor on foot. It could be concluded that the proposed method reduces the complexity of the positioning algorithm obviously, as well as offers a significant improvement in position accuracy compared to other conventional algorithms, allowing indoor positioning error below 1.2 m.
Two-dimensional filtering of SPECT images using the Metz and Wiener filters
International Nuclear Information System (INIS)
King, M.A.; Schwinger, R.B.; Penney, B.C.; Doherty, P.W.
1984-01-01
Presently, single photon emission computed tomographic (SPECT) images are usually reconstructed by arbitrarily selecting a one-dimensional ''window'' function for use in reconstruction. A better method would be to automatically choose among a family of two-dimensional image restoration filters in such a way as to produce ''optimum'' image quality. Two-dimensional image processing techniques offer the advantages of a larger statistical sampling of the data for better noise reduction, and two-dimensional image deconvolution to correct for blurring during acquisition. An investigation of two such ''optimal'' digital image restoration techniques (the count-dependent Metz filter and the Wiener filter) was made. They were applied both as two-dimensional ''window'' functions for preprocessing SPECT images, and for filtering reconstructed images. Their performance was compared by measuring image contrast and per cent fractional standard deviation (% FSD) in multiple-acquisitions of the Jaszczak SPECT phantom at two different count levels. A statistically significant increase in image contrast and decrease in % FSD was observed with these techniques when compared to the results of reconstruction with a ramp filter. The adaptability of the techniques was manifested in a lesser % reduction in % FSD at the high count level coupled with a greater enhancement in image contrast. Using an array processor, processing time was 0.2 sec per image for the Metz filter and 3 sec for the Wiener filter. It is concluded that two-dimensional digital image restoration with these techniques can produce a significant increase in SPECT image quality
Filtering Redundant Data from RFID Data Streams
Directory of Open Access Journals (Sweden)
Hazalila Kamaludin
2016-01-01
Full Text Available Radio Frequency Identification (RFID enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.
Rotationally invariant correlation filtering
International Nuclear Information System (INIS)
Schils, G.F.; Sweeney, D.W.
1985-01-01
A method is presented for analyzing and designing optical correlation filters that have tailored rotational invariance properties. The concept of a correlation of an image with a rotation of itself is introduced. A unified theory of rotation-invariant filtering is then formulated. The unified approach describes matched filters (with no rotation invariance) and circular-harmonic filters (with full rotation invariance) as special cases. The continuum of intermediate cases is described in terms of a cyclic convolution operation over angle. The angular filtering approach allows an exact choice for the continuous trade-off between loss of the correlation energy (or specificity regarding the image) and the amount of rotational invariance desired
Braile vena cava filter and greenfield filter in terms of centralization.
de Godoy, José Maria Pereira; Menezes da Silva, Adinaldo A; Reis, Luis Fernando; Miquelin, Daniel; Torati, José Luis Simon
2013-01-01
The aim of this study was to evaluate complications experienced during implantation of the Braile Vena Cava filter (VCF) and the efficacy of the centralization mechanism of the filter. This retrospective cohort study evaluated all Braile Biomédica VCFs implanted from 2004 to 2009 in Hospital de Base Medicine School in São José do Rio Preto, Brazil. Of particular concern was the filter's symmetry during implantation and complications experienced during the procedure. All the angiographic examinations performed during the implantation of the filters were analyzed in respect to the following parameters: migration of the filter, non-opening or difficulties in the implantation and centralization of the filter. A total of 112 Braile CVFs were implanted and there were no reports of filter opening difficulties or in respect to migration. Asymmetry was observed in 1/112 (0.9%) cases. A statistically significant difference was seen on comparing historical data on decentralization of the Greenfield filter with the data of this study. The Braile Biomédico filter is an evolution of the Greenfield filter providing improved embolus capture and better implantation symmetry.
The discrete Kalman filtering approach for seismic signals deconvolution
International Nuclear Information System (INIS)
Kurniadi, Rizal; Nurhandoko, Bagus Endar B.
2012-01-01
Seismic signals are a convolution of reflectivity and seismic wavelet. One of the most important stages in seismic data processing is deconvolution process; the process of deconvolution is inverse filters based on Wiener filter theory. This theory is limited by certain modelling assumptions, which may not always valid. The discrete form of the Kalman filter is then used to generate an estimate of the reflectivity function. The main advantage of Kalman filtering is capability of technique to handling continually time varying models and has high resolution capabilities. In this work, we use discrete Kalman filter that it was combined with primitive deconvolution. Filtering process works on reflectivity function, hence the work flow of filtering is started with primitive deconvolution using inverse of wavelet. The seismic signals then are obtained by convoluting of filtered reflectivity function with energy waveform which is referred to as the seismic wavelet. The higher frequency of wavelet gives smaller wave length, the graphs of these results are presented.
Quality evaluation of no-reference MR images using multidirectional filters and image statistics.
Jang, Jinseong; Bang, Kihun; Jang, Hanbyol; Hwang, Dosik
2018-09-01
This study aimed to develop a fully automatic, no-reference image-quality assessment (IQA) method for MR images. New quality-aware features were obtained by applying multidirectional filters to MR images and examining the feature statistics. A histogram of these features was then fitted to a generalized Gaussian distribution function for which the shape parameters yielded different values depending on the type of distortion in the MR image. Standard feature statistics were established through a training process based on high-quality MR images without distortion. Subsequently, the feature statistics of a test MR image were calculated and compared with the standards. The quality score was calculated as the difference between the shape parameters of the test image and the undistorted standard images. The proposed IQA method showed a >0.99 correlation with the conventional full-reference assessment methods; accordingly, this proposed method yielded the best performance among no-reference IQA methods for images containing six types of synthetic, MR-specific distortions. In addition, for authentically distorted images, the proposed method yielded the highest correlation with subjective assessments by human observers, thus demonstrating its superior performance over other no-reference IQAs. Our proposed IQA was designed to consider MR-specific features and outperformed other no-reference IQAs designed mainly for photographic images. Magn Reson Med 80:914-924, 2018. © 2018 International Society for Magnetic Resonance in Medicine. © 2018 International Society for Magnetic Resonance in Medicine.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-08
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Ke Li
2016-01-01
Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.
Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing
2016-01-01
A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006
Multiple Maneuvering Target Tracking by Improved Particle Filter Based on Multiscan JPDA
Directory of Open Access Journals (Sweden)
Jing Liu
2012-01-01
Full Text Available The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.
Ozenbaugh, Richard Lee
2011-01-01
With today's electrical and electronics systems requiring increased levels of performance and reliability, the design of robust EMI filters plays a critical role in EMC compliance. Using a mix of practical methods and theoretical analysis, EMI Filter Design, Third Edition presents both a hands-on and academic approach to the design of EMI filters and the selection of components values. The design approaches covered include matrix methods using table data and the use of Fourier analysis, Laplace transforms, and transfer function realization of LC structures. This edition has been fully revised
Takahashi, Masahiro; Kimura, Fumiko; Umezawa, Tatsuya; Watanabe, Yusuke; Ogawa, Harumi
2016-01-01
Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. To investigate the influence of ASIR on calcium quantification in comparison to FBP. In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes. Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.
Decentralized Social Filtering based on Trust
Olsson, Tomas
1998-01-01
This paper describes a decentralised approach to social filtering based on trust between agents in a multiagent system. The social filtering in the proposed approach is built on the interactions between collaborative software agents performing content-based filtering. This means that it uses a mixture of content-based and social filtering and thereby, it takes advantage of both methods.
Schikora, Marek; Gning, Amadou; Mihaylova, Lyudmila; Cremers, Daniel; Koch, Wofgang; Streit, Roy
2012-01-01
This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables thi...
Poisson filtering of laser ranging data
Ricklefs, Randall L.; Shelus, Peter J.
1993-01-01
The filtering of data in a high noise, low signal strength environment is a situation encountered routinely in lunar laser ranging (LLR) and, to a lesser extent, in artificial satellite laser ranging (SLR). The use of Poisson statistics as one of the tools for filtering LLR data is described first in a historical context. The more recent application of this statistical technique to noisy SLR data is also described.
Bailey, R. E.; Smith, R. E.
1982-01-01
An investigation of pilot-induced oscillation suppression (PIOS) filters was performed using the USAF/Flight Dynamics Laboratory variable stability NT-33 aircraft, modified and operated by Calspan. This program examined the effects of PIOS filtering on the longitudinal flying qualities of fighter aircraft during the visual approach and landing task. Forty evaluations were flown to test the effects of different PIOS filters. Although detailed analyses were not undertaken, the results indicate that PIOS filtering can improve the flying qualities of an otherwise unacceptable aircraft configuration (Level 3 flying qualities). However, the ability of the filters to suppress pilot-induced oscillations appears to be dependent upon the aircraft configuration characteristics. Further, the data show that the filters can adversely affect landing flying qualities if improperly designed. The data provide an excellent foundation from which detail analyses can be performed.
PASA - A Program for Automated Protein NMR Backbone Signal Assignment by Pattern-Filtering Approach
International Nuclear Information System (INIS)
Xu Yizhuang; Wang Xiaoxia; Yang Jun; Vaynberg, Julia; Qin Jun
2006-01-01
We present a new program, PASA (Program for Automated Sequential Assignment), for assigning protein backbone resonances based on multidimensional heteronuclear NMR data. Distinct from existing programs, PASA emphasizes a per-residue-based pattern-filtering approach during the initial stage of the automated 13 C α and/or 13 C β chemical shift matching. The pattern filter employs one or multiple constraints such as 13 C α /C β chemical shift ranges for different amino acid types and side-chain spin systems, which helps to rule out, in a stepwise fashion, improbable assignments as resulted from resonance degeneracy or missing signals. Such stepwise filtering approach substantially minimizes early false linkage problems that often propagate, amplify, and ultimately cause complication or combinatorial explosion of the automation process. Our program (http://www.lerner.ccf.org/moleccard/qin/) was tested on four representative small-large sized proteins with various degrees of resonance degeneracy and missing signals, and we show that PASA achieved the assignments efficiently and rapidly that are fully consistent with those obtained by laborious manual protocols. The results demonstrate that PASA may be a valuable tool for NMR-based structural analyses, genomics, and proteomics
Optimal Nonlinear Filter for INS Alignment
Institute of Scientific and Technical Information of China (English)
赵瑞; 顾启泰
2002-01-01
All the methods to handle the inertial navigation system (INS) alignment were sub-optimal in the past. In this paper, particle filtering (PF) as an optimal method is used for solving the problem of INS alignment. A sub-optimal two-step filtering algorithm is presented to improve the real-time performance of PF. The approach combines particle filtering with Kalman filtering (KF). Simulation results illustrate the superior performance of these approaches when compared with extended Kalman filtering (EKF).
Collaborative filtering on a family of biological targets.
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.
Nonlinear Bayesian filtering and learning: a neuronal dynamics for perception.
Kutschireiter, Anna; Surace, Simone Carlo; Sprekeler, Henning; Pfister, Jean-Pascal
2017-08-18
The robust estimation of dynamical hidden features, such as the position of prey, based on sensory inputs is one of the hallmarks of perception. This dynamical estimation can be rigorously formulated by nonlinear Bayesian filtering theory. Recent experimental and behavioral studies have shown that animals' performance in many tasks is consistent with such a Bayesian statistical interpretation. However, it is presently unclear how a nonlinear Bayesian filter can be efficiently implemented in a network of neurons that satisfies some minimum constraints of biological plausibility. Here, we propose the Neural Particle Filter (NPF), a sampling-based nonlinear Bayesian filter, which does not rely on importance weights. We show that this filter can be interpreted as the neuronal dynamics of a recurrently connected rate-based neural network receiving feed-forward input from sensory neurons. Further, it captures properties of temporal and multi-sensory integration that are crucial for perception, and it allows for online parameter learning with a maximum likelihood approach. The NPF holds the promise to avoid the 'curse of dimensionality', and we demonstrate numerically its capability to outperform weighted particle filters in higher dimensions and when the number of particles is limited.
Filter and Filter Bank Design for Image Texture Recognition
Energy Technology Data Exchange (ETDEWEB)
Randen, Trygve
1997-12-31
The relevance of this thesis to energy and environment lies in its application to remote sensing such as for instance sea floor mapping and seismic pattern recognition. The focus is on the design of two-dimensional filters for feature extraction, segmentation, and classification of digital images with textural content. The features are extracted by filtering with a linear filter and estimating the local energy in the filter response. The thesis gives a review covering broadly most previous approaches to texture feature extraction and continues with proposals of some new techniques. 143 refs., 59 figs., 7 tabs.
Time-Filtered Navier-Stokes Approach and Emulation of Turbulence-Chemistry Interaction
Liu, Nan-Suey; Wey, Thomas; Shih, Tsan-Hsing
2013-01-01
This paper describes the time-filtered Navier-Stokes approach capable of capturing unsteady flow structures important for turbulent mixing and an accompanying subgrid model directly accounting for the major processes in turbulence-chemistry interaction. They have been applied to the computation of two-phase turbulent combustion occurring in a single-element lean-direct-injection combustor. Some of the preliminary results from this computational effort are presented in this paper.
Quantitative and statistical approaches to geography a practical manual
Matthews, John A
2013-01-01
Quantitative and Statistical Approaches to Geography: A Practical Manual is a practical introduction to some quantitative and statistical techniques of use to geographers and related scientists. This book is composed of 15 chapters, each begins with an outline of the purpose and necessary mechanics of a technique or group of techniques and is concluded with exercises and the particular approach adopted. These exercises aim to enhance student's ability to use the techniques as part of the process by which sound judgments are made according to scientific standards while tackling complex problems. After a brief introduction to the principles of quantitative and statistical geography, this book goes on dealing with the topics of measures of central tendency; probability statements and maps; the problem of time-dependence, time-series analysis, non-normality, and data transformations; and the elements of sampling methodology. Other chapters cover the confidence intervals and estimation from samples, statistical hy...
A Novel Kalman Filter for Human Motion Tracking With an Inertial-Based Dynamic Inclinometer.
Ligorio, Gabriele; Sabatini, Angelo M
2015-08-01
Design and development of a linear Kalman filter to create an inertial-based inclinometer targeted to dynamic conditions of motion. The estimation of the body attitude (i.e., the inclination with respect to the vertical) was treated as a source separation problem to discriminate the gravity and the body acceleration from the specific force measured by a triaxial accelerometer. The sensor fusion between triaxial gyroscope and triaxial accelerometer data was performed using a linear Kalman filter. Wrist-worn inertial measurement unit data from ten participants were acquired while performing two dynamic tasks: 60-s sequence of seven manual activities and 90 s of walking at natural speed. Stereophotogrammetric data were used as a reference. A statistical analysis was performed to assess the significance of the accuracy improvement over state-of-the-art approaches. The proposed method achieved, on an average, a root mean square attitude error of 3.6° and 1.8° in manual activities and locomotion tasks (respectively). The statistical analysis showed that, when compared to few competing methods, the proposed method improved the attitude estimation accuracy. A novel Kalman filter for inertial-based attitude estimation was presented in this study. A significant accuracy improvement was achieved over state-of-the-art approaches, due to a filter design that better matched the basic optimality assumptions of Kalman filtering. Human motion tracking is the main application field of the proposed method. Accurately discriminating the two components present in the triaxial accelerometer signal is well suited for studying both the rotational and the linear body kinematics.
International Nuclear Information System (INIS)
Ren, Qingguo; Dewan, Sheilesh Kumar; Li, Ming; Li, Jianying; Mao, Dingbiao; Wang, Zhenglei; Hua, Yanqing
2012-01-01
Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI vol ) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique
Energy Technology Data Exchange (ETDEWEB)
Ren, Qingguo, E-mail: renqg83@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Dewan, Sheilesh Kumar, E-mail: sheilesh_d1@hotmail.com [Department of Geriatrics, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Ming, E-mail: minli77@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Jianying, E-mail: Jianying.Li@med.ge.com [CT Imaging Research Center, GE Healthcare China, Beijing (China); Mao, Dingbiao, E-mail: maodingbiao74@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Wang, Zhenglei, E-mail: Williswang_doc@yahoo.com.cn [Department of Radiology, Shanghai Electricity Hospital, Shanghai 200050 (China); Hua, Yanqing, E-mail: cjr.huayanqing@vip.163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China)
2012-10-15
Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI{sub vol}) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique.
Statistical approach for selection of biologically informative genes.
Das, Samarendra; Rai, Anil; Mishra, D C; Rai, Shesh N
2018-05-20
Selection of informative genes from high dimensional gene expression data has emerged as an important research area in genomics. Many gene selection techniques have been proposed so far are either based on relevancy or redundancy measure. Further, the performance of these techniques has been adjudged through post selection classification accuracy computed through a classifier using the selected genes. This performance metric may be statistically sound but may not be biologically relevant. A statistical approach, i.e. Boot-MRMR, was proposed based on a composite measure of maximum relevance and minimum redundancy, which is both statistically sound and biologically relevant for informative gene selection. For comparative evaluation of the proposed approach, we developed two biological sufficient criteria, i.e. Gene Set Enrichment with QTL (GSEQ) and biological similarity score based on Gene Ontology (GO). Further, a systematic and rigorous evaluation of the proposed technique with 12 existing gene selection techniques was carried out using five gene expression datasets. This evaluation was based on a broad spectrum of statistically sound (e.g. subject classification) and biological relevant (based on QTL and GO) criteria under a multiple criteria decision-making framework. The performance analysis showed that the proposed technique selects informative genes which are more biologically relevant. The proposed technique is also found to be quite competitive with the existing techniques with respect to subject classification and computational time. Our results also showed that under the multiple criteria decision-making setup, the proposed technique is best for informative gene selection over the available alternatives. Based on the proposed approach, an R Package, i.e. BootMRMR has been developed and available at https://cran.r-project.org/web/packages/BootMRMR. This study will provide a practical guide to select statistical techniques for selecting informative genes
Singh, K.; Sandu, A.; Bowman, K. W.; Parrington, M.; Jones, D. B. A.; Lee, M.
2011-08-01
Chemistry transport models determine the evolving chemical state of the atmosphere by solving the fundamental equations that govern physical and chemical transformations subject to initial conditions of the atmospheric state and surface boundary conditions, e.g., surface emissions. The development of data assimilation techniques synthesize model predictions with measurements in a rigorous mathematical framework that provides observational constraints on these conditions. Two families of data assimilation methods are currently widely used: variational and Kalman filter (KF). The variational approach is based on control theory and formulates data assimilation as a minimization problem of a cost functional that measures the model-observations mismatch. The Kalman filter approach is rooted in statistical estimation theory and provides the analysis covariance together with the best state estimate. Suboptimal Kalman filters employ different approximations of the covariances in order to make the computations feasible with large models. Each family of methods has both merits and drawbacks. This paper compares several data assimilation methods used for global chemical data assimilation. Specifically, we evaluate data assimilation approaches for improving estimates of the summertime global tropospheric ozone distribution in August 2006 based on ozone observations from the NASA Tropospheric Emission Spectrometer and the GEOS-Chem chemistry transport model. The resulting analyses are compared against independent ozonesonde measurements to assess the effectiveness of each assimilation method. All assimilation methods provide notable improvements over the free model simulations, which differ from the ozonesonde measurements by about 20 % (below 200 hPa). Four dimensional variational data assimilation with window lengths between five days and two weeks is the most accurate method, with mean differences between analysis profiles and ozonesonde measurements of 1-5 %. Two sequential
Performance improvement of shunt active power filter based on non-linear least-square approach
DEFF Research Database (Denmark)
Terriche, Yacine
2018-01-01
Nowadays, the shunt active power filters (SAPFs) have become a popular solution for power quality issues. A crucial issue in controlling the SAPFs which is highly correlated with their accuracy, flexibility and dynamic behavior, is generating the reference compensating current (RCC). The synchron......Nowadays, the shunt active power filters (SAPFs) have become a popular solution for power quality issues. A crucial issue in controlling the SAPFs which is highly correlated with their accuracy, flexibility and dynamic behavior, is generating the reference compensating current (RCC......). The synchronous reference frame (SRF) approach is widely used for generating the RCC due to its simplicity and computation efficiency. However, the SRF approach needs precise information of the voltage phase which becomes a challenge under adverse grid conditions. A typical solution to answer this need....... This paper proposes an improved open loop strategy which is unconditionally stable and flexible. The proposed method which is based on non-linear least square (NLS) approach can extract the fundamental voltage and estimates its phase within only half cycle, even in the presence of odd harmonics and dc offset...
Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system
Institute of Scientific and Technical Information of China (English)
HAO Yan-ling; CHEN Ming-hui; LI Liang-jun; XU Bo
2008-01-01
There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system.This paper discussed the use of GPS,but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS).One method is based on the Kalman filter (KF),and the other is based on the robust filter.Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF,given substantial process noise or unknown noise statistics.So the robust filter is an effective and useful method for initial alignment of SINS.This research should make the use of SINS more popular,and is also a step for further research.
A Statistical Approach For Modeling Tropical Cyclones. Synthetic Hurricanes Generator Model
Energy Technology Data Exchange (ETDEWEB)
Pasqualini, Donatella [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-05-11
This manuscript brie y describes a statistical ap- proach to generate synthetic tropical cyclone tracks to be used in risk evaluations. The Synthetic Hur- ricane Generator (SynHurG) model allows model- ing hurricane risk in the United States supporting decision makers and implementations of adaptation strategies to extreme weather. In the literature there are mainly two approaches to model hurricane hazard for risk prediction: deterministic-statistical approaches, where the storm key physical parameters are calculated using physi- cal complex climate models and the tracks are usually determined statistically from historical data; and sta- tistical approaches, where both variables and tracks are estimated stochastically using historical records. SynHurG falls in the second category adopting a pure stochastic approach.
Modern analog filter analysis and design a practical approach
Raut, R
2011-01-01
Starting from the fundamentals, the present book describes methods of designing analog electronic filters and illustrates these methods by providing numerical and circuit simulation programs. The subject matters comprise many concepts and techniques that are not available in other text books on the market. To name a few - principle of transposition and its application in directly realizing current mode filters from well known voltage mode filters; an insight into the technological aspect of integrated circuit components used to implement an integrated circuit filter; a careful blending of basi
urCF: An Approach to Integrating User Reviews into Memory-Based Collaborative Filtering
Zhang, Zhenxue
2013-01-01
Blessed by the Internet age, many online retailers (e.g., Amazon.com) have deployed recommender systems to help their customers identify products that may be of their interest in order to improve cross-selling and enhance customer loyalty. Collaborative Filtering (CF) is the most successful technique among different approaches to generating…
Hong, Jiasheng; Medina, Francisco; Martiacuten, Ferran
2018-01-01
This book presents and discusses strategies for the design and implementation of common-mode suppressed balanced microwave filters, including, narrowband, wideband, and ultra-wideband filters This book examines differential-mode, or balanced, microwave filters by discussing several implementations of practical realizations of these passive components. Topics covered include selective mode suppression, designs based on distributed and semi-lumped approaches, multilayer technologies, defect ground structures, coupled resonators, metamaterials, interference techniques, and substrate integrated waveguides, among others. Divided into five parts, Balanced Microwave Filters begins with an introduction that presents the fundamentals of balanced lines, circuits, and networks. Part 2 covers balanced transmission lines with common-mode noise suppression, including several types of common-mode filters and the application of such filters to enhance common-mode suppression in balanced bandpass filters. Next, Part 3 exa...
An alarm filtering system for an automated process: a multiple-agent approach
International Nuclear Information System (INIS)
Khoualdi, Kamel
1994-01-01
Nowadays, the supervision process of industrial installations is more and more complex involving the automation of their control. A malfunction generates an avalanche of alarms. The operator, in charge of the supervision, must face the incident and execute right actions to recover a normal situation. Generally, he is drowned under the great number of alarms. Our aim, in the frame of our researches, is to perform an alarm filtering system for an automated metro line, to help the operator finding the main alarm responsible for the malfunction. Our works are divided into two parts, both dealing with study and development of an alarm filtering system but using two different approaches. The first part is developed in the frame of the SARA project (an operator assistance system for an automated metro line) which is an expert system prototype helping the operators of a command center. In this part, a centralized approach has been used representing the events with a single event graph and using a global procedure to perform diagnosis. This approach has itself shown its limits. In the second part of our works, we have considered the distributed artificial intelligence (DAI) techniques, and more especially the multi-agent approach. The multi-agent approach has been motivated by the natural distribution of the metro line equipment and by the fact that each equipment has its own local control and knowledge. Thus, each equipment has been considered as an autonomous agent. Through agents cooperation, the system is able to determine the main alarm and the faulty equipment responsible for the incident. A prototype, written in SPIRAL (a tool for knowledge-based system) is running on a workstation. This prototype has allowed the concretization and the validation of our multi-agent approach. (author) [fr
Lee, Alexandra J; Chang, Ivan; Burel, Julie G; Lindestam Arlehamn, Cecilia S; Mandava, Aishwarya; Weiskopf, Daniela; Peters, Bjoern; Sette, Alessandro; Scheuermann, Richard H; Qian, Yu
2018-04-17
Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and
Filtering Meteoroid Flights Using Multiple Unscented Kalman Filters
Sansom, E. K.; Bland, P. A.; Rutten, M. G.; Paxman, J.; Towner, M. C.
2016-11-01
Estimator algorithms are immensely versatile and powerful tools that can be applied to any problem where a dynamic system can be modeled by a set of equations and where observations are available. A well designed estimator enables system states to be optimally predicted and errors to be rigorously quantified. Unscented Kalman filters (UKFs) and interactive multiple models can be found in methods from satellite tracking to self-driving cars. The luminous trajectory of the Bunburra Rockhole fireball was observed by the Desert Fireball Network in mid-2007. The recorded data set is used in this paper to examine the application of these two techniques as a viable approach to characterizing fireball dynamics. The nonlinear, single-body system of equations, used to model meteoroid entry through the atmosphere, is challenged by gross fragmentation events that may occur. The incorporation of the UKF within an interactive multiple model smoother provides a likely solution for when fragmentation events may occur as well as providing a statistical analysis of the state uncertainties. In addition to these benefits, another advantage of this approach is its automatability for use within an image processing pipeline to facilitate large fireball data analyses and meteorite recoveries.
Daniel Goodman’s empirical approach to Bayesian statistics
Gerrodette, Tim; Ward, Eric; Taylor, Rebecca L.; Schwarz, Lisa K.; Eguchi, Tomoharu; Wade, Paul; Himes Boor, Gina
2016-01-01
Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.
SART-Type Half-Threshold Filtering Approach for CT Reconstruction.
Yu, Hengyong; Wang, Ge
2014-01-01
The [Formula: see text] regularization problem has been widely used to solve the sparsity constrained problems. To enhance the sparsity constraint for better imaging performance, a promising direction is to use the [Formula: see text] norm (0 < p < 1) and solve the [Formula: see text] minimization problem. Very recently, Xu et al. developed an analytic solution for the [Formula: see text] regularization via an iterative thresholding operation, which is also referred to as half-threshold filtering. In this paper, we design a simultaneous algebraic reconstruction technique (SART)-type half-threshold filtering framework to solve the computed tomography (CT) reconstruction problem. In the medical imaging filed, the discrete gradient transform (DGT) is widely used to define the sparsity. However, the DGT is noninvertible and it cannot be applied to half-threshold filtering for CT reconstruction. To demonstrate the utility of the proposed SART-type half-threshold filtering framework, an emphasis of this paper is to construct a pseudoinverse transforms for DGT. The proposed algorithms are evaluated with numerical and physical phantom data sets. Our results show that the SART-type half-threshold filtering algorithms have great potential to improve the reconstructed image quality from few and noisy projections. They are complementary to the counterparts of the state-of-the-art soft-threshold filtering and hard-threshold filtering.
Behavioral investment strategy matters: a statistical arbitrage approach
Sun, David; Tsai, Shih-Chuan; Wang, Wei
2011-01-01
In this study, we employ a statistical arbitrage approach to demonstrate that momentum investment strategy tend to work better in periods longer than six months, a result different from findings in past literature. Compared with standard parametric tests, the statistical arbitrage method produces more clearly that momentum strategies work only in longer formation and holding periods. Also they yield positive significant returns in an up market, but negative yet insignificant returns in a down...
A statistical approach to optimizing concrete mixture design.
Ahmad, Shamsad; Alghamdi, Saeid A
2014-01-01
A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (3(3)). A total of 27 concrete mixtures with three replicates (81 specimens) were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48), cementitious materials content (350, 375, and 400 kg/m(3)), and fine/total aggregate ratio (0.35, 0.40, and 0.45). The experimental data were utilized to carry out analysis of variance (ANOVA) and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.
A Statistical Approach to Optimizing Concrete Mixture Design
Directory of Open Access Journals (Sweden)
Shamsad Ahmad
2014-01-01
Full Text Available A step-by-step statistical approach is proposed to obtain optimum proportioning of concrete mixtures using the data obtained through a statistically planned experimental program. The utility of the proposed approach for optimizing the design of concrete mixture is illustrated considering a typical case in which trial mixtures were considered according to a full factorial experiment design involving three factors and their three levels (33. A total of 27 concrete mixtures with three replicates (81 specimens were considered by varying the levels of key factors affecting compressive strength of concrete, namely, water/cementitious materials ratio (0.38, 0.43, and 0.48, cementitious materials content (350, 375, and 400 kg/m3, and fine/total aggregate ratio (0.35, 0.40, and 0.45. The experimental data were utilized to carry out analysis of variance (ANOVA and to develop a polynomial regression model for compressive strength in terms of the three design factors considered in this study. The developed statistical model was used to show how optimization of concrete mixtures can be carried out with different possible options.
Marginalized approximate filtering of state-space models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil
2018-01-01
Roč. 32, č. 1 (2018), s. 1-12 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GA16-09848S Institutional support: RVO:67985556 Keywords : approximate filtering * marginalized filters * particle filtering Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.708, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/dedecius-0478074.pdf
Guenter Tulip Filter Retrieval Experience: Predictors of Successful Retrieval
International Nuclear Information System (INIS)
Turba, Ulku Cenk; Arslan, Bulent; Meuse, Michael; Sabri, Saher; Macik, Barbara Gail; Hagspiel, Klaus D.; Matsumoto, Alan H.; Angle, John F.
2010-01-01
We report our experience with Guenter Tulip filter placement indications, retrievals, and procedural problems, with emphasis on alternative retrieval techniques. We have identified 92 consecutive patients in whom a Guenter Tulip filter was placed and filter removal attempted. We recorded patient demographic information, filter placement and retrieval indications, procedures, standard and nonstandard filter retrieval techniques, complications, and clinical outcomes. The mean time to retrieval for those who experienced filter strut penetration was statistically significant [F(1,90) = 8.55, p = 0.004]. Filter strut(s) IVC penetration and successful retrieval were found to be statistically significant (p = 0.043). The filter hook-IVC relationship correlated with successful retrieval. A modified guidewire loop technique was applied in 8 of 10 cases where the hook appeared to penetrate the IVC wall and could not be engaged with a loop snare catheter, providing additional technical success in 6 of 8 (75%). Therefore, the total filter retrieval success increased from 88 to 95%. In conclusion, the Guenter Tulip filter has high successful retrieval rates with low rates of complication. Additional maneuvers such as a guidewire loop method can be used to improve retrieval success rates when the filter hook is endothelialized.
Statistical and machine learning approaches for network analysis
Dehmer, Matthias
2012-01-01
Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation
International Nuclear Information System (INIS)
Chen, Kuilin; Yu, Jie
2014-01-01
Highlights: • A novel hybrid modeling method is proposed for short-term wind speed forecasting. • Support vector regression model is constructed to formulate nonlinear state-space framework. • Unscented Kalman filter is adopted to recursively update states under random uncertainty. • The new SVR–UKF approach is compared to several conventional methods for short-term wind speed prediction. • The proposed method demonstrates higher prediction accuracy and reliability. - Abstract: Accurate wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. Particularly, reliable short-term wind speed prediction can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, this task remains challenging due to the strong stochastic nature and dynamic uncertainty of wind speed. In this study, unscented Kalman filter (UKF) is integrated with support vector regression (SVR) based state-space model in order to precisely update the short-term estimation of wind speed sequence. In the proposed SVR–UKF approach, support vector regression is first employed to formulate a nonlinear state-space model and then unscented Kalman filter is adopted to perform dynamic state estimation recursively on wind sequence with stochastic uncertainty. The novel SVR–UKF method is compared with artificial neural networks (ANNs), SVR, autoregressive (AR) and autoregressive integrated with Kalman filter (AR-Kalman) approaches for predicting short-term wind speed sequences collected from three sites in Massachusetts, USA. The forecasting results indicate that the proposed method has much better performance in both one-step-ahead and multi-step-ahead wind speed predictions than the other approaches across all the locations
Statistical x-ray computed tomography imaging from photon-starved measurements
Chang, Zhiqian; Zhang, Ruoqiao; Thibault, Jean-Baptiste; Sauer, Ken; Bouman, Charles
2013-03-01
Dose reduction in clinical X-ray computed tomography (CT) causes low signal-to-noise ratio (SNR) in photonsparse situations. Statistical iterative reconstruction algorithms have the advantage of retaining image quality while reducing input dosage, but they meet their limits of practicality when significant portions of the sinogram near photon starvation. The corruption of electronic noise leads to measured photon counts taking on negative values, posing a problem for the log() operation in preprocessing of data. In this paper, we propose two categories of projection correction methods: an adaptive denoising filter and Bayesian inference. The denoising filter is easy to implement and preserves local statistics, but it introduces correlation between channels and may affect image resolution. Bayesian inference is a point-wise estimation based on measurements and prior information. Both approaches help improve diagnostic image quality at dramatically reduced dosage.
Statistical analysis of questionnaires a unified approach based on R and Stata
Bartolucci, Francesco; Gnaldi, Michela
2015-01-01
Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing.The book covers the foundations of classical test theory (CTT), test reliability, va
INNOVATIVE APPROACH TO EDUCATION AND TEACHING OF STATISTICS
Directory of Open Access Journals (Sweden)
Andrea Jindrová
2010-06-01
Full Text Available Educational and tutorial programs are being developed together, with the changing world of information technology it is a necessary course to adapt to and accept new possibilities and needs. Use of online learning tools can amplify our teaching resources and create new types of learning opportunities that did not exist in the pre-Internet age. The world is full of information, which needs to be constantly updated. Virtualisation of studying materials enables us to update and manage them quickly and easily. As an advantage, we see an asynchronous approach towards learning materials that can be tailored for the students´ needs and adjusted according to their time and availability. The specificness of statistical learning lies in various statistical programs. The high technical demands of these programs require tutorials (instructional presentations, which can help students to learn how to use them efficiently. Instructional presentation may be understood as a demonstration of how the statistical software program works. This is one of the options that students may use to simplify the utilization of control and navigation through the statistical system. Thanks to instructional presentations, students will be able to transfer their theoretical statistical knowledge into practical situation and real life and, therefore, improve their personal development process. The goal of this tutorial is to show an innovative approach for learning of statistics in the Czech University of Life Sciences. The use of presentations and their benefits for students was evaluated according to results obtained from a questionnaire survey completed by students of the 4th grade of the Faculty of Economics and Management. The aim of this pilot survey was to evaluate the benefits of these instructional presentations, and the students interest in using them. The information obtained was used as essential data for the evaluation of the efficiency of this new approach. Firstly
On a nonlinear Kalman filter with simplified divided difference approximation
Luo, Xiaodong; Hoteit, Ibrahim; Moroz, Irene M.
2012-01-01
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling's interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling's interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling's interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
On a nonlinear Kalman filter with simplified divided difference approximation
Luo, Xiaodong
2012-03-01
We present a new ensemble-based approach that handles nonlinearity based on a simplified divided difference approximation through Stirling\\'s interpolation formula, which is hence called the simplified divided difference filter (sDDF). The sDDF uses Stirling\\'s interpolation formula to evaluate the statistics of the background ensemble during the prediction step, while at the filtering step the sDDF employs the formulae in an ensemble square root filter (EnSRF) to update the background to the analysis. In this sense, the sDDF is a hybrid of Stirling\\'s interpolation formula and the EnSRF method, while the computational cost of the sDDF is less than that of the EnSRF. Numerical comparison between the sDDF and the EnSRF, with the ensemble transform Kalman filter (ETKF) as the representative, is conducted. The experiment results suggest that the sDDF outperforms the ETKF with a relatively large ensemble size, and thus is a good candidate for data assimilation in systems with moderate dimensions. © 2011 Elsevier B.V. All rights reserved.
Knowing How Good Our Searches Are: An Approach Derived from Search Filter Development Methodology
Directory of Open Access Journals (Sweden)
Sarah Hayman
2015-12-01
Full Text Available Objective – Effective literature searching is of paramount importance in supporting evidence based practice, research, and policy. Missed references can have adverse effects on outcomes. This paper reports on the development and evaluation of an online learning resource, designed for librarians and other interested searchers, presenting an evidence based approach to enhancing and testing literature searches. Methods – We developed and evaluated the set of free online learning modules for librarians called Smart Searching, suggesting the use of techniques derived from search filter development undertaken by the CareSearch Palliative Care Knowledge Network and its associated project Flinders Filters. The searching module content has been informed by the processes and principles used in search filter development. The self-paced modules are intended to help librarians and other interested searchers test the effectiveness of their literature searches, provide evidence of search performance that can be used to improve searches, as well as to evaluate and promote searching expertise. Each module covers one of four techniques, or core principles, employed in search filter development: (1 collaboration with subject experts; (2 use of a reference sample set; (3 term identification through frequency analysis; and (4 iterative testing. Evaluation of the resource comprised ongoing monitoring of web analytics to determine factors such as numbers of users and geographic origin; a user survey conducted online elicited qualitative information about the usefulness of the resource. Results – The resource was launched in May 2014. Web analytics show over 6,000 unique users from 101 countries (at 9 August 2015. Responses to the survey (n=50 indicated that 80% would recommend the resource to a colleague. Conclusions – An evidence based approach to searching, derived from search filter development methodology, has been shown to have value as an online learning
Variational Bayesian labeled multi-Bernoulli filter with unknown sensor noise statistics
Directory of Open Access Journals (Sweden)
Qiu Hao
2016-10-01
Full Text Available It is difficult to build accurate model for measurement noise covariance in complex backgrounds. For the scenarios of unknown sensor noise variances, an adaptive multi-target tracking algorithm based on labeled random finite set and variational Bayesian (VB approximation is proposed. The variational approximation technique is introduced to the labeled multi-Bernoulli (LMB filter to jointly estimate the states of targets and sensor noise variances. Simulation results show that the proposed method can give unbiased estimation of cardinality and has better performance than the VB probability hypothesis density (VB-PHD filter and the VB cardinality balanced multi-target multi-Bernoulli (VB-CBMeMBer filter in harsh situations. The simulations also confirm the robustness of the proposed method against the time-varying noise variances. The computational complexity of proposed method is higher than the VB-PHD and VB-CBMeMBer in extreme cases, while the mean execution times of the three methods are close when targets are well separated.
Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
Modified temporal approach to meta-optimizing an extended Kalman filter's parameters
CSIR Research Space (South Africa)
Salmon
2014-07-01
Full Text Available stream_source_info Salmon_2014.pdf.txt stream_content_type text/plain stream_size 1233 Content-Encoding UTF-8 stream_name Salmon_2014.pdf.txt Content-Type text/plain; charset=UTF-8 2014 IEEE International Geoscience... and Remote Sensing Symposium, Québec, Canada, 13-18 July 2014 A modified temporal approach to meta-optimizing an Extended Kalman Filter's parameters B. P. Salmon ; W. Kleynhans ; J. C. Olivier ; W. C. Olding ; K. J. Wessels ; F. van den Bergh...
Directory of Open Access Journals (Sweden)
Martin P Schilling
Full Text Available Continuing advances in nucleotide sequencing technology are inspiring a suite of genomic approaches in studies of natural populations. Researchers are faced with data management and analytical scales that are increasing by orders of magnitude. With such dramatic advances comes a need to understand biases and error rates, which can be propagated and magnified in large-scale data acquisition and processing. Here we assess genomic sampling biases and the effects of various population-level data filtering strategies in a genotyping-by-sequencing (GBS protocol. We focus on data from two species of Populus, because this genus has a relatively small genome and is emerging as a target for population genomic studies. We estimate the proportions and patterns of genomic sampling by examining the Populus trichocarpa genome (Nisqually-1, and demonstrate a pronounced bias towards coding regions when using the methylation-sensitive ApeKI restriction enzyme in this species. Using population-level data from a closely related species (P. tremuloides, we also investigate various approaches for filtering GBS data to retain high-depth, informative SNPs that can be used for population genetic analyses. We find a data filter that includes the designation of ambiguous alleles resulted in metrics of population structure and Hardy-Weinberg equilibrium that were most consistent with previous studies of the same populations based on other genetic markers. Analyses of the filtered data (27,910 SNPs also resulted in patterns of heterozygosity and population structure similar to a previous study using microsatellites. Our application demonstrates that technically and analytically simple approaches can readily be developed for population genomics of natural populations.
Kalman filter for statistical monitoring of forest cover across sub-continental regions
Raymond L. Czaplewski
1991-01-01
The Kalman filter is a multivariate generalization of the composite estimator which recursively combines a current direct estimate with a past estimate that is updated for expected change over time with a prediction model. The Kalman filter can estimate proportions of different cover types for sub-continental regions each year. A random sample of high-resolution...
Inverse statistical approach in heartbeat time series
International Nuclear Information System (INIS)
Ebadi, H; Shirazi, A H; Mani, Ali R; Jafari, G R
2011-01-01
We present an investigation on heart cycle time series, using inverse statistical analysis, a concept borrowed from studying turbulence. Using this approach, we studied the distribution of the exit times needed to achieve a predefined level of heart rate alteration. Such analysis uncovers the most likely waiting time needed to reach a certain change in the rate of heart beat. This analysis showed a significant difference between the raw data and shuffled data, when the heart rate accelerates or decelerates to a rare event. We also report that inverse statistical analysis can distinguish between the electrocardiograms taken from healthy volunteers and patients with heart failure
Statistical filtering in fluorescence microscopy and fluorescence correlation spectroscopy
Czech Academy of Sciences Publication Activity Database
Macháň, Radek; Kapusta, Peter; Hof, Martin
Roč. 406 , č. 20 (2014), s. 4797-4813 ISSN 1618-2642 R&D Projects: GA ČR GBP208/12/G016 Institutional support: RVO:61388955 Keywords : Filtered fluorescence correlation spectroscopy * Fluorescence lifetime correlation spectroscopy * Fluorescence spectral correlation spectroscopy Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 3.436, year: 2014
Boettiger, Alistair N; Wittemyer, George; Starfield, Richard; Volrath, Fritz; Douglas-Hamilton, Iain; Getz, Wayne M
2011-08-01
Understanding the environmental factors influencing animal movements is fundamental to theoretical and applied research in the field of movement ecology. Studies relating fine-scale movement paths to spatiotemporally structured landscape data, such as vegetation productivity or human activity, are particularly lacking despite the obvious importance of such information to understanding drivers of animal movement. In part, this may be because few approaches provide the sophistication to characterize the complexity of movement behavior and relate it to diverse, varying environmental stimuli. We overcame this hurdle by applying, for the first time to an ecological question, a finite impulse-response signal-filtering approach to identify human and natural environmental drivers of movements of 13 free-ranging African elephants (Loxodonta africana) from distinct social groups collected over seven years. A minimum mean-square error (MMSE) estimation criterion allowed comparison of the predictive power of landscape and ecological model inputs. We showed that a filter combining vegetation dynamics, human and physical landscape features, and previous movement outperformed simpler filter structures, indicating the importance of both dynamic and static landscape features, as well as habit, on movement decisions taken by elephants. Elephant responses to vegetation productivity indices were not uniform in time or space, indicating that elephant foraging strategies are more complex than simply gravitation toward areas of high productivity. Predictions were most frequently inaccurate outside protected area boundaries near human settlements, suggesting that human activity disrupts typical elephant movement behavior. Successful management strategies at the human-elephant interface, therefore, are likely to be context specific and dynamic. Signal processing provides a promising approach for elucidating environmental factors that drive animal movements over large time and spatial
Paradigms and pragmatism: approaches to medical statistics.
Healy, M J
2000-01-01
Until recently, the dominant philosophy of science was that due to Karl Popper, with its doctrine that the proper task of science was the formulation of hypotheses followed by attempts at refuting them. In spite of the close analogy with significance testing, these ideas do not fit well with the practice of medical statistics. The same can be said of the later philosophy of Thomas Kuhn, who maintains that science proceeds by way of revolutionary upheavals separated by periods of relatively pedestrian research which are governed by what Kuhn refers to as paradigms. Through there have been paradigm shifts in the history of statistics, a degree of continuity can also be discerned. A current paradigm shift is embodied in the spread of Bayesian ideas. It may be that a future paradigm will emphasise the pragmatic approach to statistics that is associated with the name of Daniel Schwartz.
Simplified design of filter circuits
Lenk, John
1999-01-01
Simplified Design of Filter Circuits, the eighth book in this popular series, is a step-by-step guide to designing filters using off-the-shelf ICs. The book starts with the basic operating principles of filters and common applications, then moves on to describe how to design circuits by using and modifying chips available on the market today. Lenk's emphasis is on practical, simplified approaches to solving design problems.Contains practical designs using off-the-shelf ICsStraightforward, no-nonsense approachHighly illustrated with manufacturer's data sheets
Students' Attitudes toward Statistics across the Disciplines: A Mixed-Methods Approach
Griffith, James D.; Adams, Lea T.; Gu, Lucy L.; Hart, Christian L.; Nichols-Whitehead, Penney
2012-01-01
Students' attitudes toward statistics were investigated using a mixed-methods approach including a discovery-oriented qualitative methodology among 684 undergraduate students across business, criminal justice, and psychology majors where at least one course in statistics was required. Students were asked about their attitudes toward statistics and…
A novel optimization design approach for Contourlet directional filter banks
Zhang, Songjun; Yang, Guoan; Cheng, Zhengxing; van de Wetering, H.M.M.; Ikuta, Chihiro; Nishio, Yoshifumi
2014-01-01
A Contourlet transform, an expansion of a wavelet transform, is a double filter bank structure composed of Laplacian Pyramid and directional filter banks. Several wavelet filters of preferable performance have been developed for wavelet transforms, e.g. CDF (Cohen, Daubechies and Feauveau) 9/7
Behavior of Filters and Smoothers for Strongly Nonlinear Dynamics
Zhu, Yanqui; Cohn, Stephen E.; Todling, Ricardo
1999-01-01
The Kalman filter is the optimal filter in the presence of known gaussian error statistics and linear dynamics. Filter extension to nonlinear dynamics is non trivial in the sense of appropriately representing high order moments of the statistics. Monte Carlo, ensemble-based, methods have been advocated as the methodology for representing high order moments without any questionable closure assumptions. Investigation along these lines has been conducted for highly idealized dynamics such as the strongly nonlinear Lorenz model as well as more realistic models of the means and atmosphere. A few relevant issues in this context are related to the necessary number of ensemble members to properly represent the error statistics and, the necessary modifications in the usual filter situations to allow for correct update of the ensemble members. The ensemble technique has also been applied to the problem of smoothing for which similar questions apply. Ensemble smoother examples, however, seem to be quite puzzling in that results state estimates are worse than for their filter analogue. In this study, we use concepts in probability theory to revisit the ensemble methodology for filtering and smoothing in data assimilation. We use the Lorenz model to test and compare the behavior of a variety of implementations of ensemble filters. We also implement ensemble smoothers that are able to perform better than their filter counterparts. A discussion of feasibility of these techniques to large data assimilation problems will be given at the time of the conference.
A Tool for Kalman Filter Tuning
DEFF Research Database (Denmark)
Åkesson, Bernt Magnus; Jørgensen, John Bagterp; Poulsen, Niels Kjølstad
2007-01-01
The Kalman filter requires knowledge about the noise statistics. In practical applications, however, the noise covariances are generally not known. A method for estimating noise covariances from process data has been investigated. The method gives a least-squares estimate of the noise covariances......, which can be used to compute the Kalman filter gain....
Clay Ceramic Filter for Water Treatment
Directory of Open Access Journals (Sweden)
Zereffa Enyew Amare
2017-05-01
Full Text Available Ceramic water filters were prepared from different proportions of kaolin and soft wood and sintered at 900 °C, 950 °C, and 1000 °C. The flow rate, conductivity, pH of filtered water and removal efficiency (microbial, water hardness agent’s, nitrite and turbidity were analysed. The ceramic filter with 15 % saw dust, 80 % clay and 5 % grog that was fired at temperature of 950 °C or 1000 °C showed the best removal efficiency. Statistical ANOVA tests showed a significant difference between ceramic filters with various compositions in their removal efficiencies.
DEFF Research Database (Denmark)
Drecourt, J.-P.; Madsen, H.; Rosbjerg, Dan
2006-01-01
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state....... The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques...... are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 2005 Elsevier Ltd. All rights reserved....
Input filter compensation for switching regulators
Lee, F. C.; Kelkar, S. S.
1982-01-01
The problems caused by the interaction between the input filter, output filter, and the control loop are discussed. The input filter design is made more complicated because of the need to avoid performance degradation and also stay within the weight and loss limitations. Conventional input filter design techniques are then dicussed. The concept of pole zero cancellation is reviewed; this concept is the basis for an approach to control the peaking of the output impedance of the input filter and thus mitigate some of the problems caused by the input filter. The proposed approach for control of the peaking of the output impedance of the input filter is to use a feedforward loop working in conjunction with feedback loops, thus forming a total state control scheme. The design of the feedforward loop for a buck regulator is described. A possible implementation of the feedforward loop design is suggested.
Deviation-based spam-filtering method via stochastic approach
Lee, Daekyung; Lee, Mi Jin; Kim, Beom Jun
2018-03-01
In the presence of a huge number of possible purchase choices, ranks or ratings of items by others often play very important roles for a buyer to make a final purchase decision. Perfectly objective rating is an impossible task to achieve, and we often use an average rating built on how previous buyers estimated the quality of the product. The problem of using a simple average rating is that it can easily be polluted by careless users whose evaluation of products cannot be trusted, and by malicious spammers who try to bias the rating result on purpose. In this letter we suggest how trustworthiness of individual users can be systematically and quantitatively reflected to build a more reliable rating system. We compute the suitably defined reliability of each user based on the user's rating pattern for all products she evaluated. We call our proposed method as the deviation-based ranking, since the statistical significance of each user's rating pattern with respect to the average rating pattern is the key ingredient. We find that our deviation-based ranking method outperforms existing methods in filtering out careless random evaluators as well as malicious spammers.
Speckle Filtering of GF-3 Polarimetric SAR Data with Joint Restriction Principle.
Xie, Jinwei; Li, Zhenfang; Zhou, Chaowei; Fang, Yuyuan; Zhang, Qingjun
2018-05-12
Polarimetric SAR (PolSAR) scattering characteristics of imagery are always obtained from the second order moments estimation of multi-polarization data, that is, the estimation of covariance or coherency matrices. Due to the extra-paths that signal reflected from separate scatterers within the resolution cell has to travel, speckle noise always exists in SAR images and has a severe impact on the scattering performance, especially on single look complex images. In order to achieve high accuracy in estimating covariance or coherency matrices, three aspects are taken into consideration: (1) the edges and texture of the scene are distinct after speckle filtering; (2) the statistical characteristic should be similar to the object pixel; and (3) the polarimetric scattering signature should be preserved, in addition to speckle reduction. In this paper, a joint restriction principle is proposed to meet the requirement. Three different restriction principles are introduced to the processing of speckle filtering. First, a new template, which is more suitable for the point or line targets, is designed to ensure the morphological consistency. Then, the extent sigma filter is used to restrict the pixels in the template aforementioned to have an identical statistic characteristic. At last, a polarimetric similarity factor is applied to the same pixels above, to guarantee the similar polarimetric features amongst the optional pixels. This processing procedure is named as speckle filtering with joint restriction principle and the approach is applied to GF-3 polarimetric SAR data acquired in San Francisco, CA, USA. Its effectiveness of keeping the image sharpness and preserving the scattering mechanism as well as speckle reduction is validated by the comparison with boxcar filters and refined Lee filter.
Box-particle probability hypothesis density filtering
Schikora, M.; Gning, A.; Mihaylova, L.; Cremers, D.; Koch, W.
2014-01-01
This paper develops a novel approach for multitarget tracking, called box-particle probability hypothesis density filter (box-PHD filter). The approach is able to track multiple targets and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic, and data association uncertainty. The box-PHD filter reduces the number of particles significantly, which improves the runtime considerably. The small number of box-p...
Numerical and experimental approaches to study soil transport and clogging in granular filters
Kanarska, Y.; Smith, J. J.; Ezzedine, S. M.; Lomov, I.; Glascoe, L. G.
2012-12-01
Failure of a dam by erosion ranks among the most serious accidents in civil engineering. The best way to prevent internal erosion is using adequate granular filters in the transition areas where important hydraulic gradients can appear. In case of cracking and erosion, if the filter is capable of retaining the eroded particles, the crack will seal and the dam safety will be ensured. Numerical modeling has proved to be a cost-effective tool for improving our understanding of physical processes. Traditionally, the consideration of flow and particle transport in porous media has focused on treating the media as continuum. Practical models typically address flow and transport based on the Darcy's law as a function of a pressure gradient and a medium-dependent permeability parameter. Additional macroscopic constitutes describe porosity, and permeability changes during the migration of a suspension through porous media. However, most of them rely on empirical correlations, which often need to be recalibrated for each application. Grain-scale modeling can be used to gain insight into scale dependence of continuum macroscale parameters. A finite element numerical solution of the Navier-Stokes equations for fluid flow together with Lagrange multiplier technique for solid particles was applied to the simulation of soil filtration in the filter layers of gravity dam. The numerical approach was validated through comparison of numerical simulations with the experimental results of base soil particle clogging in the filter layers performed at ERDC. The numerical simulation correctly predicted flow and pressure decay due to particle clogging. The base soil particle distribution was almost identical to those measured in the laboratory experiment. It is believed that the agreement between simulations and experimental data demonstrates the applicability of the proposed approach for prediction of the soil transport and clogging in embankment dams. To get more precise understanding of
Truncation correction for oblique filtering lines
International Nuclear Information System (INIS)
Hoppe, Stefan; Hornegger, Joachim; Lauritsch, Guenter; Dennerlein, Frank; Noo, Frederic
2008-01-01
State-of-the-art filtered backprojection (FBP) algorithms often define the filtering operation to be performed along oblique filtering lines in the detector. A limited scan field of view leads to the truncation of those filtering lines, which causes artifacts in the final reconstructed volume. In contrast to the case where filtering is performed solely along the detector rows, no methods are available for the case of oblique filtering lines. In this work, the authors present two novel truncation correction methods which effectively handle data truncation in this case. Method 1 (basic approach) handles data truncation in two successive preprocessing steps by applying a hybrid data extrapolation method, which is a combination of a water cylinder extrapolation and a Gaussian extrapolation. It is independent of any specific reconstruction algorithm. Method 2 (kink approach) uses similar concepts for data extrapolation as the basic approach but needs to be integrated into the reconstruction algorithm. Experiments are presented from simulated data of the FORBILD head phantom, acquired along a partial-circle-plus-arc trajectory. The theoretically exact M-line algorithm is used for reconstruction. Although the discussion is focused on theoretically exact algorithms, the proposed truncation correction methods can be applied to any FBP algorithm that exposes oblique filtering lines.
On a multiscale approach for filter efficiency simulations
Iliev, Oleg
2014-07-01
Filtration in general, and the dead end depth filtration of solid particles out of fluid in particular, is intrinsic multiscale problem. The deposition (capturing of particles) essentially depends on local velocity, on microgeometry (pore scale geometry) of the filtering medium and on the diameter distribution of the particles. The deposited (captured) particles change the microstructure of the porous media what leads to change of permeability. The changed permeability directly influences the velocity field and pressure distribution inside the filter element. To close the loop, we mention that the velocity influences the transport and deposition of particles. In certain cases one can evaluate the filtration efficiency considering only microscale or only macroscale models, but in general an accurate prediction of the filtration efficiency requires multiscale models and algorithms. This paper discusses the single scale and the multiscale models, and presents a fractional time step discretization algorithm for the multiscale problem. The velocity within the filter element is computed at macroscale, and is used as input for the solution of microscale problems at selected locations of the porous medium. The microscale problem is solved with respect to transport and capturing of individual particles, and its solution is postprocessed to provide permeability values for macroscale computations. Results from computational experiments with an oil filter are presented and discussed.
Six sigma for organizational excellence a statistical approach
Muralidharan, K
2015-01-01
This book discusses the integrated concepts of statistical quality engineering and management tools. It will help readers to understand and apply the concepts of quality through project management and technical analysis, using statistical methods. Prepared in a ready-to-use form, the text will equip practitioners to implement the Six Sigma principles in projects. The concepts discussed are all critically assessed and explained, allowing them to be practically applied in managerial decision-making, and in each chapter, the objectives and connections to the rest of the work are clearly illustrated. To aid in understanding, the book includes a wealth of tables, graphs, descriptions and checklists, as well as charts and plots, worked-out examples and exercises. Perhaps the most unique feature of the book is its approach, using statistical tools, to explain the science behind Six Sigma project management and integrated in engineering concepts. The material on quality engineering and statistical management tools of...
Selected annotated bibliographies for adaptive filtering of digital image data
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
Image Denoising Using Interquartile Range Filter with Local Averaging
Jassim, Firas Ajil
2013-01-01
Image denoising is one of the fundamental problems in image processing. In this paper, a novel approach to suppress noise from the image is conducted by applying the interquartile range (IQR) which is one of the statistical methods used to detect outlier effect from a dataset. A window of size kXk was implemented to support IQR filter. Each pixel outside the IQR range of the kXk window is treated as noisy pixel. The estimation of the noisy pixels was obtained by local averaging. The essential...
Statistical algebraic approach to quantum mechanics
International Nuclear Information System (INIS)
Slavnov, D.A.
2001-01-01
The scheme for plotting the quantum theory with application of the statistical algebraic approach is proposed. The noncommutative algebra elements (observed ones) and nonlinear functionals on this algebra (physical state) are used as the primary constituents. The latter ones are associated with the single-unit measurement results. Certain physical state groups are proposed to consider as quantum states of the standard quantum mechanics. It is shown that the mathematical apparatus of the standard quantum mechanics may be reproduced in such a scheme in full volume [ru
Data assimilation using Bayesian filters and B-spline geological models
Duan, Lian
2011-04-01
This paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir.
Data assimilation using Bayesian filters and B-spline geological models
International Nuclear Information System (INIS)
Duan Lian; Farmer, Chris; Hoteit, Ibrahim; Luo Xiaodong; Moroz, Irene
2011-01-01
This paper proposes a new approach to problems of data assimilation, also known as history matching, of oilfield production data by adjustment of the location and sharpness of patterns of geological facies. Traditionally, this problem has been addressed using gradient based approaches with a level set parameterization of the geology. Gradient-based methods are robust, but computationally demanding with real-world reservoir problems and insufficient for reservoir management uncertainty assessment. Recently, the ensemble filter approach has been used to tackle this problem because of its high efficiency from the standpoint of implementation, computational cost, and performance. Incorporation of level set parameterization in this approach could further deal with the lack of differentiability with respect to facies type, but its practical implementation is based on some assumptions that are not easily satisfied in real problems. In this work, we propose to describe the geometry of the permeability field using B-spline curves. This transforms history matching of the discrete facies type to the estimation of continuous B-spline control points. As filtering scheme, we use the ensemble square-root filter (EnSRF). The efficacy of the EnSRF with the B-spline parameterization is investigated through three numerical experiments, in which the reservoir contains a curved channel, a disconnected channel or a 2-dimensional closed feature. It is found that the application of the proposed method to the problem of adjusting facies edges to match production data is relatively straightforward and provides statistical estimates of the distribution of geological facies and of the state of the reservoir.
Experiential Approach to Teaching Statistics and Research Methods ...
African Journals Online (AJOL)
Statistics and research methods are among the more demanding topics for students of education to master at both the undergraduate and postgraduate levels. It is our conviction that teaching these topics should be combined with real practical experiences. We discuss an experiential teaching/ learning approach that ...
Statistical Approaches to Assess Biosimilarity from Analytical Data.
Burdick, Richard; Coffey, Todd; Gutka, Hiten; Gratzl, Gyöngyi; Conlon, Hugh D; Huang, Chi-Ting; Boyne, Michael; Kuehne, Henriette
2017-01-01
Protein therapeutics have unique critical quality attributes (CQAs) that define their purity, potency, and safety. The analytical methods used to assess CQAs must be able to distinguish clinically meaningful differences in comparator products, and the most important CQAs should be evaluated with the most statistical rigor. High-risk CQA measurements assess the most important attributes that directly impact the clinical mechanism of action or have known implications for safety, while the moderate- to low-risk characteristics may have a lower direct impact and thereby may have a broader range to establish similarity. Statistical equivalence testing is applied for high-risk CQA measurements to establish the degree of similarity (e.g., highly similar fingerprint, highly similar, or similar) of selected attributes. Notably, some high-risk CQAs (e.g., primary sequence or disulfide bonding) are qualitative (e.g., the same as the originator or not the same) and therefore not amenable to equivalence testing. For biosimilars, an important step is the acquisition of a sufficient number of unique originator drug product lots to measure the variability in the originator drug manufacturing process and provide sufficient statistical power for the analytical data comparisons. Together, these analytical evaluations, along with PK/PD and safety data (immunogenicity), provide the data necessary to determine if the totality of the evidence warrants a designation of biosimilarity and subsequent licensure for marketing in the USA. In this paper, a case study approach is used to provide examples of analytical similarity exercises and the appropriateness of statistical approaches for the example data.
Submerged Pond Sand Filter-A Novel Approach to Rural Water Supply
DEFF Research Database (Denmark)
Øhlenschlæger, Mia; Christensen, Sarah Christine Boesgaard; Bregnhøj, Henrik
2016-01-01
This study describes the new design and function of a modified version of a traditional slow sand filter. The Submerged Pond Sand Filter is built inside a pond and has a vertical as well as a horizontal flow of water through a sloped filter opening. The filter provides treated drinking water...... to a rural Indian village. The filter has functioned with minimal maintenance for five years without being subject to the typical scraping off and changing of sand as needed in traditional slow sand filters every few months. This five-year study showed bacterial removal efficiency of 97% on average...... to 10 CFU/100 mL on average compared to shorter pumping intervals (5 min). Though the treated water did not comply with the World Health Organization standards of 0 CFU/100 mL, the filter significantly improved water quality and provided one of the best sources of drinkable water in a water...
Two-stage nonrecursive filter/decimator
International Nuclear Information System (INIS)
Yoder, J.R.; Richard, B.D.
1980-08-01
A two-stage digital filter/decimator has been designed and implemented to reduce the sampling rate associated with the long-term computer storage of certain digital waveforms. This report describes the design selection and implementation process and serves as documentation for the system actually installed. A filter design with finite-impulse response (nonrecursive) was chosen for implementation via direct convolution. A newly-developed system-test statistic validates the system under different computer-operating environments
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Hoteit, Ibrahim
2010-09-19
Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.
Global Kalman filter approaches to estimate absolute angles of lower limb segments.
Nogueira, Samuel L; Lambrecht, Stefan; Inoue, Roberto S; Bortole, Magdo; Montagnoli, Arlindo N; Moreno, Juan C; Rocon, Eduardo; Terra, Marco H; Siqueira, Adriano A G; Pons, Jose L
2017-05-16
In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF. The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance. The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.
Optimization of the reconstruction and anti-aliasing filter in a Wiener filter system
Wesselink, J.M.; Berkhoff, Arthur P.
2006-01-01
This paper discusses the influence of the reconstruction and anti-aliasing filters on the performance of a digital implementation of a Wiener filter for active noise control. The overall impact will be studied in combination with a multi-rate system approach. A reconstruction and anti-aliasing
A statistical approach for water movement in the unsaturated zone
International Nuclear Information System (INIS)
Tielin Zang.
1991-01-01
This thesis presents a statistical approach for estimating and analyzing the downward transport pattern and distribution of soil water by the use of pattern analysis of space-time correlation structures. This approach, called the Space-time-Correlation Field, is mainly based on the analyses of correlation functions simultaneously in the space and time domain. The overall purpose of this work is to derive an alternative statistical procedure in soil moisture analysis without involving detailed information on hydraulic parameters and to visualize the dynamics of soil water variability in the space and time domains. A numerical model using method of characteristics is employed to provide hypothetical time series to use in the statistical method, which is, after the verification and calibration, applied to the field measured time series. The results of the application show that the space-time correlation fields reveal effects of soil layers with different hydraulic properties and boundaries between them. It is concluded that the approach poses special advantages when visualizing time and space dependent properties simultaneously. It can be used to investigate the hydrological response of soil water dynamics and characteristics in different dimensions (space and time) and scales. This approach can be used to identify the dominant component in unsaturated flow systems. It is possible to estimate the pattern and the propagation rate downwards of moisture movement in the soil profile. Small-scale soil heterogeneities can be identified by the correlation field. Since the correlation field technique give a statistical measure of the dependent property that varies within the space-time field, it is possible to interpolate the fields to points where observations are not available, estimating spatial or temporal averages from discrete observations. (au)
Rigatos, Gerasimos G; Rigatou, Efthymia G; Djida, Jean Daniel
2015-10-01
A method for early diagnosis of parametric changes in intracellular protein synthesis models (e.g. the p53 protein - mdm2 inhibitor model) is developed with the use of a nonlinear Kalman Filtering approach (Derivative-free nonlinear Kalman Filter) and of statistical change detection methods. The intracellular protein synthesis dynamic model is described by a set of coupled nonlinear differential equations. It is shown that such a dynamical system satisfies differential flatness properties and this allows to transform it, through a change of variables (diffeomorphism), to the so-called linear canonical form. For the linearized equivalent of the dynamical system, state estimation can be performed using the Kalman Filter recursion. Moreover, by applying an inverse transformation based on the previous diffeomorphism it becomes also possible to obtain estimates of the state variables of the initial nonlinear model. By comparing the output of the Kalman Filter (which is assumed to correspond to the undistorted dynamical model) with measurements obtained from the monitored protein synthesis system, a sequence of differences (residuals) is obtained. The statistical processing of the residuals with the use of x2 change detection tests, can provide indication within specific confidence intervals about parametric changes in the considered biological system and consequently indications about the appearance of specific diseases (e.g. malignancies).
Propensity Score Analysis: An Alternative Statistical Approach for HRD Researchers
Keiffer, Greggory L.; Lane, Forrest C.
2016-01-01
Purpose: This paper aims to introduce matching in propensity score analysis (PSA) as an alternative statistical approach for researchers looking to make causal inferences using intact groups. Design/methodology/approach: An illustrative example demonstrated the varying results of analysis of variance, analysis of covariance and PSA on a heuristic…
Measuring University Students' Approaches to Learning Statistics: An Invariance Study
Chiesi, Francesca; Primi, Caterina; Bilgin, Ayse Aysin; Lopez, Maria Virginia; del Carmen Fabrizio, Maria; Gozlu, Sitki; Tuan, Nguyen Minh
2016-01-01
The aim of the current study was to provide evidence that an abbreviated version of the Approaches and Study Skills Inventory for Students (ASSIST) was invariant across different languages and educational contexts in measuring university students' learning approaches to statistics. Data were collected on samples of university students attending…
Multiplier-free filters for wideband SAR
DEFF Research Database (Denmark)
Dall, Jørgen; Christensen, Erik Lintz
2001-01-01
This paper derives a set of parameters to be optimized when designing filters for digital demodulation and range prefiltering in SAR systems. Aiming at an implementation in field programmable gate arrays (FPGAs), an approach for the design of multiplier-free filters is outlined. Design results...... are presented in terms of filter complexity and performance. One filter has been coded in VHDL and preliminary results indicate that the filter can meet a 2 GHz input sample rate....
Statistical data filtration in neutron coincidence counting
International Nuclear Information System (INIS)
Beddingfield, D.H.; Menlove, H.O.
1992-11-01
We assessed the effectiveness of statistical data filtration to minimize the contribution of matrix materials in 200-ell drums to the nondestructive assay of plutonium. Those matrices were examined: polyethylene, concrete, aluminum, iron, cadmium, and lead. Statistical filtration of neutron coincidence data improved the low-end sensitivity of coincidence counters. Spurious data arising from electrical noise, matrix spallation, and geometric effects were smoothed in a predictable fashion by the statistical filter. The filter effectively lowers the minimum detectable mass limit that can be achieved for plutonium assay using passive neutron coincidence counting
PSpice for filters and transmission lines
Tobin, Paul
2007-01-01
In this book, PSpice for Filters and Transmission Lines, we examine a range of active and passive filters where each design is simulated using the latest Cadence Orcad V10.5 PSpice capture software. These filters cannot match the very high order digital signal processing (DSP) filters considered in PSpice for Digital Signal Processing, but nevertheless these filters have many uses. The active filters considered were designed using Butterworth and Chebychev approximation loss functions rather than using the 'cookbook approach' so that the final design will meet a given specification in an exact
The application of particle filters in single trial event-related potential estimation
International Nuclear Information System (INIS)
Mohseni, Hamid R; Nazarpour, Kianoush; Sanei, Saeid; Wilding, Edward L
2009-01-01
In this paper, an approach for the estimation of single trial event-related potentials (ST-ERPs) using particle filters (PFs) is presented. The method is based on recursive Bayesian mean square estimation of ERP wavelet coefficients using their previous estimates as prior information. To enable a performance evaluation of the approach in the Gaussian and non-Gaussian distributed noise conditions, we added Gaussian white noise (GWN) and real electroencephalogram (EEG) signals recorded during rest to the simulated ERPs. The results were compared to that of the Kalman filtering (KF) approach demonstrating the robustness of the PF over the KF to the added GWN noise. The proposed method also outperforms the KF when the assumption about the Gaussianity of the noise is violated. We also applied this technique to real EEG potentials recorded in an odd-ball paradigm and investigated the correlation between the amplitude and the latency of the estimated ERP components. Unlike the KF method, for the PF there was a statistically significant negative correlation between amplitude and latency of the estimated ERPs, matching previous neurophysiological findings
Quantum neural network-based EEG filtering for a brain-computer interface.
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.
Cosseddu, Salvatore M.; Khovanov, Igor A.; Allen, Michael P.; Rodger, P. M.; Luchinsky, Dmitry G.; McClintock, Peter V. E.
2013-01-01
The statistical and dynamical properties of ions in the selectivity filter of the KcsA ion channel are considered on the basis of molecular dynamics (MD) simulations of the KcsA protein embedded in a lipid membrane surrounded by an ionic solution. A new approach to the derivation of a Brownian dynamics (BD) model of ion permeation through the filter is discussed, based on unbiased MD simulations. It is shown that depending on additional assumptions, ion’s dynamics can be described either by u...
Comparing geological and statistical approaches for element selection in sediment tracing research
Laceby, J. Patrick; McMahon, Joe; Evrard, Olivier; Olley, Jon
2015-04-01
Elevated suspended sediment loads reduce reservoir capacity and significantly increase the cost of operating water treatment infrastructure, making the management of sediment supply to reservoirs of increasingly importance. Sediment fingerprinting techniques can be used to determine the relative contributions of different sources of sediment accumulating in reservoirs. The objective of this research is to compare geological and statistical approaches to element selection for sediment fingerprinting modelling. Time-integrated samplers (n=45) were used to obtain source samples from four major subcatchments flowing into the Baroon Pocket Dam in South East Queensland, Australia. The geochemistry of potential sources were compared to the geochemistry of sediment cores (n=12) sampled in the reservoir. The geochemical approach selected elements for modelling that provided expected, observed and statistical discrimination between sediment sources. Two statistical approaches selected elements for modelling with the Kruskal-Wallis H-test and Discriminatory Function Analysis (DFA). In particular, two different significance levels (0.05 & 0.35) for the DFA were included to investigate the importance of element selection on modelling results. A distribution model determined the relative contributions of different sources to sediment sampled in the Baroon Pocket Dam. Elemental discrimination was expected between one subcatchment (Obi Obi Creek) and the remaining subcatchments (Lexys, Falls and Bridge Creek). Six major elements were expected to provide discrimination. Of these six, only Fe2O3 and SiO2 provided expected, observed and statistical discrimination. Modelling results with this geological approach indicated 36% (+/- 9%) of sediment sampled in the reservoir cores were from mafic-derived sources and 64% (+/- 9%) were from felsic-derived sources. The geological and the first statistical approach (DFA0.05) differed by only 1% (σ 5%) for 5 out of 6 model groupings with only
International Nuclear Information System (INIS)
Bird, William F.; Chaney, Edward L.; Coggins, James M.
1995-01-01
Purpose / Objective: Manual labeling of individual 192 Ir seeds in localization images for dosimetry of multi-strand low-dose-rate (LDR) implants is labor intensive, tedious and prone to error. The objective of this investigation is to develop computer-based methods that analyze digitized localization images, improve dosimetric efficiency, and reduce labeling errors. Materials and Methods: 192 Ir localization films were digitized with a scanned-laser system and analyzed using Multiscale, Geometric, Statistical Pattern Recognition (MGSPR), a technique that recognizes and classifies pixels in gray-scale images based on their surrounding, neighborhood geometry. To 'teach' MGSPR how to recognize specific objects, a Gaussian-based mathematical filter set is applied to training images containing user-labeled examples of the desired objects. The filters capture a broad range of descriptive geometric information at multiple spatial scales. Principled mathematical analysis is used to determine the linear combination of filters from a large base set that yields the best discrimination between object types. Thus the sensitivity of the filters can be 'tuned' to detect specific objects such as 192 Ir seeds. For a given pixel, the output of the filter is a multi-component feature vector that uniquely describes the pixel's geometric characteristics. Pixels with similar geometric attributes have feature vectors that naturally 'cluster', or group, in the multidimensional space called 'feature space'. After statistically quantifying the training-set clusters in feature space, pixels found in new images are automatically labeled by correlation with the nearest cluster, e.g., the cluster representing 192 Ir seeds. One of the greatest challenges in statistical pattern recognition is to determine which filters result in the best labeling. Good discrimination is achieved when clusters are compact and well isolated from one another in feature space. The filters used in this study are
Non-linear and signal energy optimal asymptotic filter design
Directory of Open Access Journals (Sweden)
Josef Hrusak
2003-10-01
Full Text Available The paper studies some connections between the main results of the well known Wiener-Kalman-Bucy stochastic approach to filtering problems based mainly on the linear stochastic estimation theory and emphasizing the optimality aspects of the achieved results and the classical deterministic frequency domain linear filters such as Chebyshev, Butterworth, Bessel, etc. A new non-stochastic but not necessarily deterministic (possibly non-linear alternative approach called asymptotic filtering based mainly on the concepts of signal power, signal energy and a system equivalence relation plays an important role in the presentation. Filtering error invariance and convergence aspects are emphasized in the approach. It is shown that introducing the signal power as the quantitative measure of energy dissipation makes it possible to achieve reasonable results from the optimality point of view as well. The property of structural energy dissipativeness is one of the most important and fundamental features of resulting filters. Therefore, it is natural to call them asymptotic filters. The notion of the asymptotic filter is carried in the paper as a proper tool in order to unify stochastic and non-stochastic, linear and nonlinear approaches to signal filtering.
A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics
Directory of Open Access Journals (Sweden)
Joaquín Míguez
2004-11-01
Full Text Available In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.
[Investigation of fast filter of ECG signals with lifting wavelet and smooth filter].
Li, Xuefei; Mao, Yuxing; He, Wei; Yang, Fan; Zhou, Liang
2008-02-01
The lifting wavelet is used to decompose the original ECG signals and separate them into the approach signals with low frequency and the detail signals with high frequency, based on frequency characteristic. Parts of the detail signals are ignored according to the frequency characteristic. To avoid the distortion of QRS Complexes, the approach signals are filtered by an adaptive smooth filter with a proper threshold value. Through the inverse transform of the lifting wavelet, the reserved approach signals are reconstructed, and the three primary kinds of noise are limited effectively. In addition, the method is fast and there is no time delay between input and output.
The Behavior of Filters and Smoothers for Strongly Nonlinear Dynamics
Zhu, Yanqiu; Cohn, Stephen E.; Todling, Ricardo
1999-01-01
The Kalman filter is the optimal filter in the presence of known Gaussian error statistics and linear dynamics. Filter extension to nonlinear dynamics is non trivial in the sense of appropriately representing high order moments of the statistics. Monte Carlo, ensemble-based, methods have been advocated as the methodology for representing high order moments without any questionable closure assumptions (e.g., Miller 1994). Investigation along these lines has been conducted for highly idealized dynamics such as the strongly nonlinear Lorenz (1963) model as well as more realistic models of the oceans (Evensen and van Leeuwen 1996) and atmosphere (Houtekamer and Mitchell 1998). A few relevant issues in this context are related to the necessary number of ensemble members to properly represent the error statistics and, the necessary modifications in the usual filter equations to allow for correct update of the ensemble members (Burgers 1998). The ensemble technique has also been applied to the problem of smoothing for which similar questions apply. Ensemble smoother examples, however, seem to quite puzzling in that results of state estimate are worse than for their filter analogue (Evensen 1997). In this study, we use concepts in probability theory to revisit the ensemble methodology for filtering and smoothing in data assimilation. We use Lorenz (1963) model to test and compare the behavior of a variety implementations of ensemble filters. We also implement ensemble smoothers that are able to perform better than their filter counterparts. A discussion of feasibility of these techniques to large data assimilation problems will be given at the time of the conference.
Elementary statistical thermodynamics a problems approach
Smith, Norman O
1982-01-01
This book is a sequel to my Chemical Thermodynamics: A Prob lems Approach published in 1967, which concerned classical thermodynamics almost exclusively. Most books on statistical thermodynamics now available are written either for the superior general chemistry student or for the specialist. The author has felt the need for a text which would bring the intermediate reader to the point where he could not only appreciate the roots of the subject but also have some facility in calculating thermodynamic quantities. Although statistical thermodynamics comprises an essential part of the college training of a chemist, its treatment in general physical chem istry texts is, of necessity, compressed to the point where the less competent student is unable to appreciate or comprehend its logic and beauty, and is reduced to memorizing a series of formulas. It has been my aim to fill this need by writing a logical account of the foundations and applications of the sub ject at a level which can be grasped by an under...
Adaptive Maneuvering Frequency Method of Current Statistical Model
Institute of Scientific and Technical Information of China (English)
Wei Sun; Yongjian Yang
2017-01-01
Current statistical model(CSM) has a good performance in maneuvering target tracking. However, the fixed maneuvering frequency will deteriorate the tracking results, such as a serious dynamic delay, a slowly converging speedy and a limited precision when using Kalman filter(KF) algorithm. In this study, a new current statistical model and a new Kalman filter are proposed to improve the performance of maneuvering target tracking. The new model which employs innovation dominated subjection function to adaptively adjust maneuvering frequency has a better performance in step maneuvering target tracking, while a fluctuant phenomenon appears. As far as this problem is concerned, a new adaptive fading Kalman filter is proposed as well. In the new Kalman filter, the prediction values are amended in time by setting judgment and amendment rules,so that tracking precision and fluctuant phenomenon of the new current statistical model are improved. The results of simulation indicate the effectiveness of the new algorithm and the practical guiding significance.
DSP Control of Line Hybrid Active Filter
DEFF Research Database (Denmark)
Dan, Stan George; Benjamin, Doniga Daniel; Magureanu, R.
2005-01-01
Active Power Filters have been intensively explored in the past decade. Hybrid active filters inherit the efficiency of passive filters and the improved performance of active filters, and thus constitute a viable improved approach for harmonic compensation. In this paper a parallel hybrid filter...... is studied for current harmonic compensation. The hybrid filter is formed by a single tuned Le filter and a small-rated power active filter, which are directly connected in series without any matching transformer. Thus the required rating of the active filter is much smaller than a conventional standalone...... active filter. Simulation and experimental results obtained in laboratory confirmed the validity and effectiveness of the control....
Ayam, Rufus Tekoh
2011-01-01
PURPOSE: The two approaches to audit sampling; statistical and nonstatistical have been examined in this study. The overall purpose of the study is to explore the current extent at which statistical and nonstatistical sampling approaches are utilized by independent auditors during auditing practices. Moreover, the study also seeks to achieve two additional purposes; the first is to find out whether auditors utilize different sampling techniques when auditing SME´s (Small and Medium-Sized Ente...
Directory of Open Access Journals (Sweden)
Geeta Hanji
2016-11-01
Full Text Available Noise reduction is an important area of research in image processing applications. The performance of the digital image noise filtering method primarily depends upon the accuracy of noise detection scheme. This paper presents an effective detector based, adaptive mask, median filtration of heavily noised digital images affected with fixed value (or salt and pepper impulse noise. The proposed filter presents a novel approach; an ameliorated Rank Ordered Absolute Deviation (ROAD statistics to judge whether the input pixel is noised or noise free. If a pixel is detected as corrupted, it is subjected to adaptive mask median filtration; otherwise, it is kept unchanged. Extensive experimental results and comparative performance evaluations demonstrate that the proposed filter outperforms the existing decision type, median based filters with powerful noise detectors in terms of objective performance measures and visual retrieviation accuracy.
Marginalized Particle Filtering Framework for Tuning of Ensemble Filters
Czech Academy of Sciences Publication Activity Database
Šmídl, Václav; Hofman, Radek
2011-01-01
Roč. 139, č. 11 (2011), s. 3589-3599 ISSN 0027-0644 R&D Projects: GA MV VG20102013018; GA ČR GP102/08/P250 Institutional research plan: CEZ:AV0Z10750506 Keywords : ensemble finter * marginalized particle filter * data assimilation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.688, year: 2011 http://library.utia.cas.cz/separaty/2011/AS/smidl-0367533.pdf
A Statistical Mechanics Approach to Approximate Analytical Bootstrap Averages
DEFF Research Database (Denmark)
Malzahn, Dorthe; Opper, Manfred
2003-01-01
We apply the replica method of Statistical Physics combined with a variational method to the approximate analytical computation of bootstrap averages for estimating the generalization error. We demonstrate our approach on regression with Gaussian processes and compare our results with averages...
Non-specific filtering of beta-distributed data.
Wang, Xinhui; Laird, Peter W; Hinoue, Toshinori; Groshen, Susan; Siegmund, Kimberly D
2014-06-19
Non-specific feature selection is a dimension reduction procedure performed prior to cluster analysis of high dimensional molecular data. Not all measured features are expected to show biological variation, so only the most varying are selected for analysis. In DNA methylation studies, DNA methylation is measured as a proportion, bounded between 0 and 1, with variance a function of the mean. Filtering on standard deviation biases the selection of probes to those with mean values near 0.5. We explore the effect this has on clustering, and develop alternate filter methods that utilize a variance stabilizing transformation for Beta distributed data and do not share this bias. We compared results for 11 different non-specific filters on eight Infinium HumanMethylation data sets, selected to span a variety of biological conditions. We found that for data sets having a small fraction of samples showing abnormal methylation of a subset of normally unmethylated CpGs, a characteristic of the CpG island methylator phenotype in cancer, a novel filter statistic that utilized a variance-stabilizing transformation for Beta distributed data outperformed the common filter of using standard deviation of the DNA methylation proportion, or its log-transformed M-value, in its ability to detect the cancer subtype in a cluster analysis. However, the standard deviation filter always performed among the best for distinguishing subgroups of normal tissue. The novel filter and standard deviation filter tended to favour features in different genome contexts; for the same data set, the novel filter always selected more features from CpG island promoters and the standard deviation filter always selected more features from non-CpG island intergenic regions. Interestingly, despite selecting largely non-overlapping sets of features, the two filters did find sample subsets that overlapped for some real data sets. We found two different filter statistics that tended to prioritize features with
Low-sensitivity active filter realization using a complex all-pass filter
Regalia, Phillip A.; Mitra, Sanjit K.
1987-04-01
A wide class of continuous-time transfer functions may be implemented as the parallel combination of two all-pass filters, including Butterworth, Chebyshev, and elliptic low-pass approximations of odd order. Here, the realization of even-order low-pass classical approximations is considered, and it is shown that they may be decomposed in terms of complex all-pass functions. A systematic realization approach, based on scattering domain simulation (i.e., wave active filters), allows for a low-sensitivity active filter implementation. Further insight into the low-sensitivity property is gained by connecting the insertion loss of doubly terminated antimetric networks with the imaginary return loss of complex lossless networks.
A statistical mechanics approach to mixing in stratified fluids
Venaille , Antoine; Gostiaux , Louis; Sommeria , Joël
2016-01-01
Accepted for the Journal of Fluid Mechanics; Predicting how much mixing occurs when a given amount of energy is injected into a Boussinesq fluid is a longstanding problem in stratified turbulence. The huge number of degrees of freedom involved in these processes renders extremely difficult a deterministic approach to the problem. Here we present a statistical mechanics approach yielding a prediction for a cumulative, global mixing efficiency as a function of a global Richard-son number and th...
B Plant exhaust filter inventory analysis
International Nuclear Information System (INIS)
Lan, J.S.; Wootan, D.W.; Carter, L.L.; Bunch, W.L.; Covey, L.I.; Greenborg, J.
1994-10-01
This paper describes a method for determining radionuclide inventories in filters using measurements of radiation fields that determine photon dose rates and photon-plus-electron dose rates between filter banks. The mathematical approach quantifies the curie inventories of filter banks by using the measured dose rates and the calculated Green's functions involving detector responses per unit source
Carlson, Kieth A.; Winquist, Jennifer R.
2011-01-01
The study evaluates a semester-long workbook curriculum approach to teaching a college level introductory statistics course. The workbook curriculum required students to read content before and during class and then work in groups to complete problems and answer conceptual questions pertaining to the material they read. Instructors spent class…
Directory of Open Access Journals (Sweden)
Jay Krishna Thakur
2015-08-01
Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.
A statistical approach to traditional Vietnamese medical diagnoses standardization
International Nuclear Information System (INIS)
Nguyen Hoang Phuong; Nguyen Quang Hoa; Le Dinh Long
1990-12-01
In this paper the first results of the statistical approach for Cold-Heat diagnosis standardization as a first work in the ''eight rules diagnoses'' standardization of Traditional Vietnamese Medicine are briefly described. Some conclusions and suggestions for further work are given. 3 refs, 2 tabs
Particle filters for random set models
Ristic, Branko
2013-01-01
“Particle Filters for Random Set Models” presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. While particle filters have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book...
Energy Technology Data Exchange (ETDEWEB)
Bird, William F; Chaney, Edward L; Coggins, James M
1995-07-01
Purpose / Objective: Manual labeling of individual {sup 192}Ir seeds in localization images for dosimetry of multi-strand low-dose-rate (LDR) implants is labor intensive, tedious and prone to error. The objective of this investigation is to develop computer-based methods that analyze digitized localization images, improve dosimetric efficiency, and reduce labeling errors. Materials and Methods: {sup 192}Ir localization films were digitized with a scanned-laser system and analyzed using Multiscale, Geometric, Statistical Pattern Recognition (MGSPR), a technique that recognizes and classifies pixels in gray-scale images based on their surrounding, neighborhood geometry. To 'teach' MGSPR how to recognize specific objects, a Gaussian-based mathematical filter set is applied to training images containing user-labeled examples of the desired objects. The filters capture a broad range of descriptive geometric information at multiple spatial scales. Principled mathematical analysis is used to determine the linear combination of filters from a large base set that yields the best discrimination between object types. Thus the sensitivity of the filters can be 'tuned' to detect specific objects such as{sup 192} Ir seeds. For a given pixel, the output of the filter is a multi-component feature vector that uniquely describes the pixel's geometric characteristics. Pixels with similar geometric attributes have feature vectors that naturally 'cluster', or group, in the multidimensional space called 'feature space'. After statistically quantifying the training-set clusters in feature space, pixels found in new images are automatically labeled by correlation with the nearest cluster, e.g., the cluster representing {sup 192}Ir seeds. One of the greatest challenges in statistical pattern recognition is to determine which filters result in the best labeling. Good discrimination is achieved when clusters are compact and well isolated from one another in feature space. The filters used in
Improved air ventilation rate estimation based on a statistical model
International Nuclear Information System (INIS)
Brabec, M.; Jilek, K.
2004-01-01
A new approach to air ventilation rate estimation from CO measurement data is presented. The approach is based on a state-space dynamic statistical model, allowing for quick and efficient estimation. Underlying computations are based on Kalman filtering, whose practical software implementation is rather easy. The key property is the flexibility of the model, allowing various artificial regimens of CO level manipulation to be treated. The model is semi-parametric in nature and can efficiently handle time-varying ventilation rate. This is a major advantage, compared to some of the methods which are currently in practical use. After a formal introduction of the statistical model, its performance is demonstrated on real data from routine measurements. It is shown how the approach can be utilized in a more complex situation of major practical relevance, when time-varying air ventilation rate and radon entry rate are to be estimated simultaneously from concurrent radon and CO measurements
Kalman Filtering with Real-Time Applications
Chui, Charles K
2009-01-01
Kalman Filtering with Real-Time Applications presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering.
International Nuclear Information System (INIS)
Kachelriess, Marc; Watzke, Oliver; Kalender, Willi A.
2001-01-01
In modern computed tomography (CT) there is a strong desire to reduce patient dose and/or to improve image quality by increasing spatial resolution and decreasing image noise. These are conflicting demands since increasing resolution at a constant noise level or decreasing noise at a constant resolution level implies a higher demand on x-ray power and an increase of patient dose. X-ray tube power is limited due to technical reasons. We therefore developed a generalized multi-dimensional adaptive filtering approach that applies nonlinear filters in up to three dimensions in the raw data domain. This new method differs from approaches in the literature since our nonlinear filters are applied not only in the detector row direction but also in the view and in the z-direction. This true three-dimensional filtering improves the quantum statistics of a measured projection value proportional to the third power of the filter size. Resolution tradeoffs are shared among these three dimensions and thus are considerably smaller as compared to one-dimensional smoothing approaches. Patient data of spiral and sequential single- and multi-slice CT scans as well as simulated spiral cone-beam data were processed to evaluate these new approaches. Image quality was assessed by evaluation of difference images, by measuring the image noise and the noise reduction, and by calculating the image resolution using point spread functions. The use of generalized adaptive filters helps to reduce image noise or, alternatively, patient dose. Image noise structures, typically along the direction of the highest attenuation, are effectively reduced. Noise reduction values of typically 30%-60% can be achieved in noncylindrical body regions like the shoulder. The loss in image resolution remains below 5% for all cases. In addition, the new method has a great potential to reduce metal artifacts, e.g., in the hip region
Wiener discrete cosine transform-based image filtering
Pogrebnyak, Oleksiy; Lukin, Vladimir V.
2012-10-01
A classical problem of additive white (spatially uncorrelated) Gaussian noise suppression in grayscale images is considered. The main attention is paid to discrete cosine transform (DCT)-based denoising, in particular, to image processing in blocks of a limited size. The efficiency of DCT-based image filtering with hard thresholding is studied for different sizes of overlapped blocks. A multiscale approach that aggregates the outputs of DCT filters having different overlapped block sizes is proposed. Later, a two-stage denoising procedure that presumes the use of the multiscale DCT-based filtering with hard thresholding at the first stage and a multiscale Wiener DCT-based filtering at the second stage is proposed and tested. The efficiency of the proposed multiscale DCT-based filtering is compared to the state-of-the-art block-matching and three-dimensional filter. Next, the potentially reachable multiscale filtering efficiency in terms of output mean square error (MSE) is studied. The obtained results are of the same order as those obtained by Chatterjee's approach based on nonlocal patch processing. It is shown that the ideal Wiener DCT-based filter potential is usually higher when noise variance is high.
Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters
Directory of Open Access Journals (Sweden)
Sicuranza Giovanni L
2004-01-01
Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.
A κ-generalized statistical mechanics approach to income analysis
Clementi, F.; Gallegati, M.; Kaniadakis, G.
2009-02-01
This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low-middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful.
A κ-generalized statistical mechanics approach to income analysis
International Nuclear Information System (INIS)
Clementi, F; Gallegati, M; Kaniadakis, G
2009-01-01
This paper proposes a statistical mechanics approach to the analysis of income distribution and inequality. A new distribution function, having its roots in the framework of κ-generalized statistics, is derived that is particularly suitable for describing the whole spectrum of incomes, from the low–middle income region up to the high income Pareto power-law regime. Analytical expressions for the shape, moments and some other basic statistical properties are given. Furthermore, several well-known econometric tools for measuring inequality, which all exist in a closed form, are considered. A method for parameter estimation is also discussed. The model is shown to fit remarkably well the data on personal income for the United States, and the analysis of inequality performed in terms of its parameters is revealed as very powerful
Fast bilateral filtering of CT-images
Energy Technology Data Exchange (ETDEWEB)
Steckmann, Sven; Baer, Matthias; Kachelriess, Marc [Erlangen-Nuernberg Univ., Erlangen (Germany). Inst. of Medical Physics (IMP)
2011-07-01
The Bilateral filter is able to get a lower noise level while retaining the edges in images. The downside of a bilateral filter is the high order of the problem itself. While having a Volume size of N with a dimension of d and a filter window of r the problem is of size N{sup d} . r{sup d}. In the literature there are some proposals for speeding up by reducing this order by approximating a component of the filter. This leads to inaccurate results which often implies non acceptable artifacts for medical imaging. A better way for medical imaging is to speed up the filter itself while leaving the basic structure intact. This is the way our implementation uses. We solve the problem of calculating the function of e{sup -x} in an efficient way on modern architectures, and the problem of vectorizing the filtering process. As result we implemented a filter which is 2.5 times faster than the highly optimized basic approach. By comparing the basic analytical approach with the final algorithm, the differences in quality of the computing process is negligible to the human eye. We are able to process a volume with 512{sup 3} voxels with a filter of 25 x 25 x 1 in 21 s on a modern Intel Xeon platform with two X5590 processors running at 3.33 GHz. (orig.)
A statistical and distributed packet filter against DDoS attacks in ...
Indian Academy of Sciences (India)
VIKASH C PANDEY
2018-03-14
Mar 14, 2018 ... Distributed Denial of Service (DDoS) attacks are a serious threat to Cloud. These attacks ... packet filtering model is proposed against DDoS attacks in Cloud. The key idea of this .... generates alerts or logs. If a deviation from ...
International Nuclear Information System (INIS)
Xiao, Liang; Shen, Jing; Tong, Jia Jie; Huang, De Sheng
2012-01-01
To determine whether the introducer curving technique is useful in decreasing the degree of tilting of transfemoral Tulip filters. The study sample group consisted of 108 patients with deep vein thrombosis who were enrolled and planned to undergo thrombolysis, and who accepted transfemoral Tulip filter insertion procedure. The patients were randomly divided into Group C and Group T. The introducer curving technique was Adopted in Group T. The post-implantation filter tilting angle (ACF) was measured in an anteroposterior projection. The retrieval hook adhering to the vascular wall was measured via tangential cavogram during retrieval. The overall average ACF was 5.8 ± 4.14 degrees. In Group C, the average ACF was 7.1 ± 4.52 degrees. In Group T, the average ACF was 4.4 ± 3.20 degrees. The groups displayed a statistically significant difference (t = 3.573, p = 0.001) in ACF. Additionally, the difference of ACF between the left and right approaches turned out to be statistically significant (7.1 ± 4.59 vs. 5.1 ± 3.82, t = 2.301, p = 0.023). The proportion of severe tilt (ACF ≥ 10 degree) in Group T was significantly lower than that in Group C (9.3% vs. 24.1%, X 2 = 4.267, p = 0.039). Between the groups, the difference in the rate of the retrieval hook adhering to the vascular wall was also statistically significant (2.9% vs. 24.2%, X 2 = 5.030, p = 0.025). The introducer curving technique appears to minimize the incidence and extent of transfemoral Tulip filter tilting.
Energy Technology Data Exchange (ETDEWEB)
Xiao, Liang; Shen, Jing; Tong, Jia Jie [The First Hospital of China Medical University, Shenyang (China); Huang, De Sheng [College of Basic Medical Science, China Medical University, Shenyang (China)
2012-07-15
To determine whether the introducer curving technique is useful in decreasing the degree of tilting of transfemoral Tulip filters. The study sample group consisted of 108 patients with deep vein thrombosis who were enrolled and planned to undergo thrombolysis, and who accepted transfemoral Tulip filter insertion procedure. The patients were randomly divided into Group C and Group T. The introducer curving technique was Adopted in Group T. The post-implantation filter tilting angle (ACF) was measured in an anteroposterior projection. The retrieval hook adhering to the vascular wall was measured via tangential cavogram during retrieval. The overall average ACF was 5.8 {+-} 4.14 degrees. In Group C, the average ACF was 7.1 {+-} 4.52 degrees. In Group T, the average ACF was 4.4 {+-} 3.20 degrees. The groups displayed a statistically significant difference (t = 3.573, p = 0.001) in ACF. Additionally, the difference of ACF between the left and right approaches turned out to be statistically significant (7.1 {+-} 4.59 vs. 5.1 {+-} 3.82, t = 2.301, p = 0.023). The proportion of severe tilt (ACF {>=} 10 degree) in Group T was significantly lower than that in Group C (9.3% vs. 24.1%, X{sup 2} = 4.267, p = 0.039). Between the groups, the difference in the rate of the retrieval hook adhering to the vascular wall was also statistically significant (2.9% vs. 24.2%, X{sup 2} = 5.030, p = 0.025). The introducer curving technique appears to minimize the incidence and extent of transfemoral Tulip filter tilting.
Kalman filter-based gap conductance modeling
International Nuclear Information System (INIS)
Tylee, J.L.
1983-01-01
Geometric and thermal property uncertainties contribute greatly to the problem of determining conductance within the fuel-clad gas gap of a nuclear fuel pin. Accurate conductance values are needed for power plant licensing transient analysis and for test analyses at research facilities. Recent work by Meek, Doerner, and Adams has shown that use of Kalman filters to estimate gap conductance is a promising approach. A Kalman filter is simply a mathematical algorithm that employs available system measurements and assumed dynamic models to generate optimal system state vector estimates. This summary addresses another Kalman filter approach to gap conductance estimation and subsequent identification of an empirical conductance model
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.
Topology for statistical modeling of petascale data.
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio (University of Utah, Salt Lake City, UT); Mascarenhas, Ajith Arthur; Rusek, Korben (Texas A& M University, College Station, TX); Bennett, Janine Camille; Levine, Joshua (University of Utah, Salt Lake City, UT); Pebay, Philippe Pierre; Gyulassy, Attila (University of Utah, Salt Lake City, UT); Thompson, David C.; Rojas, Joseph Maurice (Texas A& M University, College Station, TX)
2011-07-01
This document presents current technical progress and dissemination of results for the Mathematics for Analysis of Petascale Data (MAPD) project titled 'Topology for Statistical Modeling of Petascale Data', funded by the Office of Science Advanced Scientific Computing Research (ASCR) Applied Math program. Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is thus to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, our approach is based on the complementary techniques of combinatorial topology and statistical modeling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modeling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. This document summarizes the technical advances we have made to date that were made possible in whole or in part by MAPD funding. These technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modeling, and (3) new integrated topological and statistical methods.
Van Delden, Jay S
2003-07-15
A novel, interferometric, polarization-interrogating filter assembly and method for the simultaneous measurement of all four Stokes parameters across a partially polarized irradiance image in a no-moving-parts, instantaneous, highly sensitive manner is described. In the reported embodiment of the filter, two spatially varying linear retarders and a linear polarizer comprise an ortho-Babinet, polarization-interrogating (OBPI) filter. The OBPI filter uniquely encodes the incident ensemble of electromagnetic wave fronts comprising a partially polarized irradiance image in a controlled, deterministic, spatially varying manner to map the complete state of polarization across the image to local variations in a superposed interference pattern. Experimental interferograms are reported along with a numerical simulation of the method.
Correlation Filter Learning Toward Peak Strength for Visual Tracking.
Sui, Yao; Wang, Guanghui; Zhang, Li
2018-04-01
This paper presents a novel visual tracking approach to correlation filter learning toward peak strength of correlation response. Previous methods leverage all features of the target and the immediate background to learn a correlation filter. Some features, however, may be distractive to tracking, like those from occlusion and local deformation, resulting in unstable tracking performance. This paper aims at solving this issue and proposes a novel algorithm to learn the correlation filter. The proposed approach, by imposing an elastic net constraint on the filter, can adaptively eliminate those distractive features in the correlation filtering. A new peak strength metric is proposed to measure the discriminative capability of the learned correlation filter. It is demonstrated that the proposed approach effectively strengthens the peak of the correlation response, leading to more discriminative performance than previous methods. Extensive experiments on a challenging visual tracking benchmark demonstrate that the proposed tracker outperforms most state-of-the-art methods.
Statistical approach to partial equilibrium analysis
Wang, Yougui; Stanley, H. E.
2009-04-01
A statistical approach to market equilibrium and efficiency analysis is proposed in this paper. One factor that governs the exchange decisions of traders in a market, named willingness price, is highlighted and constitutes the whole theory. The supply and demand functions are formulated as the distributions of corresponding willing exchange over the willingness price. The laws of supply and demand can be derived directly from these distributions. The characteristics of excess demand function are analyzed and the necessary conditions for the existence and uniqueness of equilibrium point of the market are specified. The rationing rates of buyers and sellers are introduced to describe the ratio of realized exchange to willing exchange, and their dependence on the market price is studied in the cases of shortage and surplus. The realized market surplus, which is the criterion of market efficiency, can be written as a function of the distributions of willing exchange and the rationing rates. With this approach we can strictly prove that a market is efficient in the state of equilibrium.
Durantin, Gautier; Scannella, Sébastien; Gateau, Thibault; Delorme, Arnaud; Dehais, Frédéric
2015-01-01
Working memory (WM) is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor WM as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering, and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces (BCI). We used functional near infrared spectroscopy as it has been already successfully tested to measure WM capacity in complex environment with air traffic controllers (ATC), pilots, or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with nine participants involving a basic WM task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with ATC instructions (two levels of difficulty). The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence (MACD) filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in WM. In addition, the use of a Kalman filter increased
Directory of Open Access Journals (Sweden)
Gautier eDurantin
2016-01-01
Full Text Available Working memory is a key executive function for operating aircraft, especially when pilots have to recall series of air traffic control instructions. There is a need to implement tools to monitor working memory as its limitation may jeopardize flight safety. An innovative way to address this issue is to adopt a Neuroergonomics approach that merges knowledge and methods from Human Factors, System Engineering and Neuroscience. A challenge of great importance for Neuroergonomics is to implement efficient brain imaging techniques to measure the brain at work and to design Brain Computer Interfaces. We used functional near infrared spectroscopy as it has been already successfully tested to measure working memory capacity in complex environment with air traffic controllers, pilots or unmanned vehicle operators. However, the extraction of relevant features from the raw signal in ecological environment is still a critical issue due to the complexity of implementing real-time signal processing techniques without a priori knowledge. We proposed to implement the Kalman filtering approach, a signal processing technique that is efficient when the dynamics of the signal can be modeled. We based our approach on the Boynton model of hemodynamic response. We conducted a first experiment with 9 participants involving a basic working memory task to estimate the noise covariances of the Kalman filter. We then conducted a more ecological experiment in our flight simulator with 18 pilots who interacted with air traffic controller instructions (two levels of difficulty. The data was processed with the same Kalman filter settings implemented in the first experiment. This filter was benchmarked with a classical pass-band IIR filter and a Moving Average Convergence Divergence filter. Statistical analysis revealed that the Kalman filter was the most efficient to separate the two levels of load, by increasing the observed effect size in prefrontal areas involved in working
The use of the Kalman filter in the automated segmentation of EIT lung images
International Nuclear Information System (INIS)
Zifan, A; Chapman, B E; Liatsis, P
2013-01-01
In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging. (paper)
The use of the Kalman filter in the automated segmentation of EIT lung images.
Zifan, A; Liatsis, P; Chapman, B E
2013-06-01
In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging.
Xu, Zheyao; Qi, Naiming; Chen, Yukun
2015-12-01
Spacecraft simulators are widely used to study the dynamics, guidance, navigation, and control of a spacecraft on the ground. A spacecraft simulator can have three rotational degrees of freedom by using a spherical air-bearing to simulate a frictionless and micro-gravity space environment. The moment of inertia and center of mass are essential for control system design of ground-based three-axis spacecraft simulators. Unfortunately, they cannot be known precisely. This paper presents two approaches, i.e. a recursive least-squares (RLS) approach with tracking differentiator (TD) and Extended Kalman Filter (EKF) method, to estimate inertia parameters. The tracking differentiator (TD) filter the noise coupled with the measured signals and generate derivate of the measured signals. Combination of two TD filters in series obtains the angular accelerations that are required in RLS (TD-TD-RLS). Another method that does not need to estimate the angular accelerations is using the integrated form of dynamics equation. An extended TD (ETD) filter which can also generate the integration of the function of signals is presented for RLS (denoted as ETD-RLS). States and inertia parameters are estimated simultaneously using EKF. The observability is analyzed. All proposed methods are illustrated by simulations and experiments.
Hydrodynamics of microbial filter feeding
DEFF Research Database (Denmark)
Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia
2017-01-01
Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate......-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude......; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet...
A hybrid filtering approach for storage optimization in main-memory cloud database
Directory of Open Access Journals (Sweden)
Ghada M. Afify
2015-11-01
Full Text Available Enterprises and cloud service providers face dramatic increase in the amount of data stored in private and public clouds. Thus, data storage costs are growing hastily because they use only one single high-performance storage tier for storing all cloud data. There’s considerable potential to reduce cloud costs by classifying data into active (hot and inactive (cold. In the main-memory databases research, recent works focus on approaches to identify hot/cold data. Most of these approaches track tuple accesses to identify hot/cold tuples. In contrast, we introduce a novel Hybrid Filtering Approach (HFA that tracks both tuples and columns accesses in main-memory databases. Our objective is to enhance the performance in terms of three dimensions: storage space, query elapsed time and CPU time. In order to validate the effectiveness of our approach, we realized its concrete implementation on Hekaton, a SQL’s server memory-optimized engine using the well-known TPC-H benchmark. Experimental results show that the proposed HFA outperforms Hekaton approach in respect of all performance dimensions. In specific, HFA reduces the storage space by average of 44–96%, reduces the query elapsed time by average of 25–93% and reduces the CPU time by average of 31–97% compared to the traditional database approach.
Ceramic fiber reinforced filter
Stinton, David P.; McLaughlin, Jerry C.; Lowden, Richard A.
1991-01-01
A filter for removing particulate matter from high temperature flowing fluids, and in particular gases, that is reinforced with ceramic fibers. The filter has a ceramic base fiber material in the form of a fabric, felt, paper of the like, with the refractory fibers thereof coated with a thin layer of a protective and bonding refractory applied by chemical vapor deposition techniques. This coating causes each fiber to be physically joined to adjoining fibers so as to prevent movement of the fibers during use and to increase the strength and toughness of the composite filter. Further, the coating can be selected to minimize any reactions between the constituents of the fluids and the fibers. A description is given of the formation of a composite filter using a felt preform of commercial silicon carbide fibers together with the coating of these fibers with pure silicon carbide. Filter efficiency approaching 100% has been demonstrated with these filters. The fiber base material is alternately made from aluminosilicate fibers, zirconia fibers and alumina fibers. Coating with Al.sub.2 O.sub.3 is also described. Advanced configurations for the composite filter are suggested.
Variable Span Filters for Speech Enhancement
DEFF Research Database (Denmark)
Jensen, Jesper Rindom; Benesty, Jacob; Christensen, Mads Græsbøll
2016-01-01
In this work, we consider enhancement of multichannel speech recordings. Linear filtering and subspace approaches have been considered previously for solving the problem. The current linear filtering methods, although many variants exist, have limited control of noise reduction and speech...
Alarm filtering and presentation
International Nuclear Information System (INIS)
Bray, M.A.
1989-01-01
This paper discusses alarm filtering and presentation in the control room of nuclear and other process control plants. Alarm generation and presentation is widely recognized as a general process control problem. Alarm systems often fail to provide meaningful alarms to operators. Alarm generation and presentation is an area in which computer aiding is feasible and provides clear benefits. Therefore, researchers have developed several computerized alarm filtering and presentation approaches. This paper discusses problems associated with alarm generation and presentation. Approaches to improving the alarm situation and installation issues of alarm system improvements are discussed. The impact of artificial intelligence (AI) technology on alarm system improvements is assessed. (orig.)
An analytical statistical approach to the 3D reconstruction problem
Energy Technology Data Exchange (ETDEWEB)
Cierniak, Robert [Czestochowa Univ. of Technology (Poland). Inst. of Computer Engineering
2011-07-01
The presented here approach is concerned with the reconstruction problem for 3D spiral X-ray tomography. The reconstruction problem is formulated taking into considerations the statistical properties of signals obtained in X-ray CT. Additinally, image processing performed in our approach is involved in analytical methodology. This conception significantly improves quality of the obtained after reconstruction images and decreases the complexity of the reconstruction problem in comparison with other approaches. Computer simulations proved that schematically described here reconstruction algorithm outperforms conventional analytical methods in obtained image quality. (orig.)
General renormalized statistical approach with finite cross-field correlations
International Nuclear Information System (INIS)
Vakulenko, M.O.
1992-01-01
The renormalized statistical approach is proposed, accounting for finite correlations of potential and magnetic fluctuations. It may be used for analysis of a wide class of nonlinear model equations describing the cross-correlated plasma states. The influence of a cross spectrum on stationary potential and magnetic ones is investigated. 10 refs. (author)
Statistical approaches for evaluating body composition markers in clinical cancer research.
Bayar, Mohamed Amine; Antoun, Sami; Lanoy, Emilie
2017-04-01
The term 'morphomics' stands for the markers of body composition in muscle and adipose tissues. in recent years, as part of clinical cancer research, several associations between morphomics and outcome or toxicity were found in different treatment settings leading to a growing interest. we aim to review statistical approaches used to evaluate these markers and suggest practical statistical recommendations. Area covered: We identified statistical methods used recently to take into account properties of morphomics measurements. We also reviewed adjustment methods on major confounding factors such as gender and approaches to model morphomic data, especially mixed models for repeated measures. Finally, we focused on methods for determining a cut-off for a morphomic marker that could be used in clinical practice and how to assess its robustness. Expert commentary: From our review, we proposed 13 key points to strengthen analyses and reporting of clinical research assessing associations between morphomics and outcome or toxicity.
Topology optimization of microwave waveguide filters
DEFF Research Database (Denmark)
Aage, Niels; Johansen, Villads Egede
2017-01-01
We present a density based topology optimization approach for the design of metallic microwave insert filters. A two-phase optimization procedure is proposed in which we, starting from a uniform design, first optimize to obtain a set of spectral varying resonators followed by a band gap...... optimization for the desired filter characteristics. This is illustrated through numerical experiments and comparison to a standard band pass filter design. It is seen that the carefully optimized topologies can sharpen the filter characteristics and improve performance. Furthermore, the obtained designs share...... little resemblance to standard filter layouts and hence the proposed design method offers a new design tool in microwave engineering....
Kalman filtering with real-time applications
Chui, Charles K
2017-01-01
This new edition presents a thorough discussion of the mathematical theory and computational schemes of Kalman filtering. The filtering algorithms are derived via different approaches, including a direct method consisting of a series of elementary steps, and an indirect method based on innovation projection. Other topics include Kalman filtering for systems with correlated noise or colored noise, limiting Kalman filtering for time-invariant systems, extended Kalman filtering for nonlinear systems, interval Kalman filtering for uncertain systems, and wavelet Kalman filtering for multiresolution analysis of random signals. Most filtering algorithms are illustrated by using simplified radar tracking examples. The style of the book is informal, and the mathematics is elementary but rigorous. The text is self-contained, suitable for self-study, and accessible to all readers with a minimum knowledge of linear algebra, probability theory, and system engineering. Over 100 exercises and problems with solutions help de...
Xiao, Liang; Huang, De-sheng; Shen, Jing; Tong, Jia-jie
2012-01-01
To determine whether the introducer curving technique is useful in decreasing the degree of tilting of transfemoral Tulip filters. The study sample group consisted of 108 patients with deep vein thrombosis who were enrolled and planned to undergo thrombolysis, and who accepted transfemoral Tulip filter insertion procedure. The patients were randomly divided into Group C and Group T. The introducer curving technique was Adopted in Group T. The post-implantation filter tilting angle (ACF) was measured in an anteroposterior projection. The retrieval hook adhering to the vascular wall was measured via tangential cavogram during retrieval. The overall average ACF was 5.8 ± 4.14 degrees. In Group C, the average ACF was 7.1 ± 4.52 degrees. In Group T, the average ACF was 4.4 ± 3.20 degrees. The groups displayed a statistically significant difference (t = 3.573, p = 0.001) in ACF. Additionally, the difference of ACF between the left and right approaches turned out to be statistically significant (7.1 ± 4.59 vs. 5.1 ± 3.82, t = 2.301, p = 0.023). The proportion of severe tilt (ACF ≥ 10°) in Group T was significantly lower than that in Group C (9.3% vs. 24.1%, χ(2) = 4.267, p = 0.039). Between the groups, the difference in the rate of the retrieval hook adhering to the vascular wall was also statistically significant (2.9% vs. 24.2%, χ(2) = 5.030, p = 0.025). The introducer curving technique appears to minimize the incidence and extent of transfemoral Tulip filter tilting.
Topology optimization of microwave waveguide filters
DEFF Research Database (Denmark)
Aage, Niels; Johansen, Villads Egede
2017-01-01
We present a density based topology optimization approach for the design of metallic microwave insert filters. A two-phase optimization procedure is proposed in which we, starting from a uniform design, first optimize to obtain a set of spectral varying resonators followed by a band gap optimizat......We present a density based topology optimization approach for the design of metallic microwave insert filters. A two-phase optimization procedure is proposed in which we, starting from a uniform design, first optimize to obtain a set of spectral varying resonators followed by a band gap...... little resemblance to standard filter layouts and hence the proposed design method offers a new design tool in microwave engineering....
Filter Paper: Solution to High Self-Attenuation Corrections in HEPA Filter Measurements
International Nuclear Information System (INIS)
Oberer, R.B.; Harold, N.B.; Gunn, C.A.; Brummett, M.; Chaing, L.G.
2005-01-01
An 8 by 8 by 6 inch High Efficiency Particulate Air (HEPA) filter was measured as part of a uranium holdup survey in June of 2005 as it has been routinely measured every two months since 1998. Although the survey relies on gross gamma count measurements, this was one of a few measurements that had been converted to a quantitative measurement in 1998. The measurement was analyzed using the traditional Generalized Geometry Holdup (GGH) approach, using HMS3 software, with an area calibration and self-attenuation corrected with an empirical correction factor of 1.06. A result of 172 grams of 235 U was reported. The actual quantity of 235 U in the filter was approximately 1700g. Because of this unusually large discrepancy, the measurement of HEPA filters will be discussed. Various techniques for measuring HEPA filters will be described using the measurement of a 24 by 24 by 12 inch HEPA filter as an example. A new method to correct for self attenuation will be proposed for this measurement Following the discussion of the 24 by 24 by 12 inch HEPA filter, the measurement of the 8 by 8 by 6 inch will be discussed in detail
Respiratory-Induced Haemodynamic Changes: A Contributing Factor to IVC Filter Penetration
International Nuclear Information System (INIS)
Laborda, Alicia; Kuo, William T.; Ioakeim, Ignatios; De Blas, Ignacio; Malvè, Mauro; Lahuerta, Celia; De Gregorio, Miguel A.
2015-01-01
PurposeThe purpose of the study is to evaluate the influence of respiratory-induced vena caval hemodynamic changes on filter migration/penetration.Materials and MethodsAfter placement of either a Gunther Tulip or Celect IVC filter, 101 consecutive patients scheduled for filter retrieval were prospectively enrolled in this study. Pre-retrieval CT scans were used to assess filter complications and to calculate cross-sectional area in three locations: at level of filter strut fixation, 3 cm above and 3 cm below. A 3D finite element simulation was constructed on these data and direct IVC pressure was recorded during filter retrieval. Cross-sectional areas and pressures of the vena cava were measured during neutral breathing and in Valsalva maneuver and identified filter complications were recorded. A statistical analysis of these variables was then performed.ResultsDuring Valsalva maneuvers, a 60 % decrease of the IVC cross-sectional area and a fivefold increase in the IVC pressure were identified (p < 0.001). There was a statistically significant difference in the reduction of the cross-sectional area at the filter strut level (p < 0.001) in patient with filter penetration. Difficulty in filter retrieval was higher in penetrated or tilted filters (p < 0.001; p = 0.005). 3D computational models showed significant IVC deformation around the filter during Valsalva maneuver.ConclusionCaval morphology and hemodynamics are clearly affected by Valsalva maneuvers. A physiological reduction of IVC cross-sectional area is associated with higher risk of filter penetration, despite short dwell times. Physiologic data should be used to improve future filter designs to remain safely implanted over longer dwell times
Respiratory-Induced Haemodynamic Changes: A Contributing Factor to IVC Filter Penetration
Energy Technology Data Exchange (ETDEWEB)
Laborda, Alicia, E-mail: alaborda@unizar.es [Universidad de Zaragoza, Minimally Invasive Techniques Research Group (GITMI) (Spain); Kuo, William T., E-mail: wkuo@stanford.edu [Stanford University Medical Center, Division of Vascular and Interventional Radiology (United States); Ioakeim, Ignatios, E-mail: ignacio.ioakim@hotmail.es [Universidad de Zaragoza, Minimally Invasive Techniques Research Group (GITMI) (Spain); De Blas, Ignacio, E-mail: deblas@unizar.es [Universidad de Zaragoza, Unit of Infectious Diseases and Epidemiology, Department of Animal Pathology (Spain); Malvè, Mauro, E-mail: mauro.malve@unavarra.es [Universidad Pública de Navarra, Department of Mechanical, Energy and Materials Engineering (Spain); Lahuerta, Celia, E-mail: celialahuerta@gmail.com; De Gregorio, Miguel A., E-mail: mgregori@unizar.es [Universidad de Zaragoza, Minimally Invasive Techniques Research Group (GITMI) (Spain)
2015-10-15
PurposeThe purpose of the study is to evaluate the influence of respiratory-induced vena caval hemodynamic changes on filter migration/penetration.Materials and MethodsAfter placement of either a Gunther Tulip or Celect IVC filter, 101 consecutive patients scheduled for filter retrieval were prospectively enrolled in this study. Pre-retrieval CT scans were used to assess filter complications and to calculate cross-sectional area in three locations: at level of filter strut fixation, 3 cm above and 3 cm below. A 3D finite element simulation was constructed on these data and direct IVC pressure was recorded during filter retrieval. Cross-sectional areas and pressures of the vena cava were measured during neutral breathing and in Valsalva maneuver and identified filter complications were recorded. A statistical analysis of these variables was then performed.ResultsDuring Valsalva maneuvers, a 60 % decrease of the IVC cross-sectional area and a fivefold increase in the IVC pressure were identified (p < 0.001). There was a statistically significant difference in the reduction of the cross-sectional area at the filter strut level (p < 0.001) in patient with filter penetration. Difficulty in filter retrieval was higher in penetrated or tilted filters (p < 0.001; p = 0.005). 3D computational models showed significant IVC deformation around the filter during Valsalva maneuver.ConclusionCaval morphology and hemodynamics are clearly affected by Valsalva maneuvers. A physiological reduction of IVC cross-sectional area is associated with higher risk of filter penetration, despite short dwell times. Physiologic data should be used to improve future filter designs to remain safely implanted over longer dwell times.
An approach of point cloud denoising based on improved bilateral filtering
Zheng, Zeling; Jia, Songmin; Zhang, Guoliang; Li, Xiuzhi; Zhang, Xiangyin
2018-04-01
An omnidirectional mobile platform is designed for building point cloud based on an improved filtering algorithm which is employed to handle the depth image. First, the mobile platform can move flexibly and the control interface is convenient to control. Then, because the traditional bilateral filtering algorithm is time-consuming and inefficient, a novel method is proposed which called local bilateral filtering (LBF). LBF is applied to process depth image obtained by the Kinect sensor. The results show that the effect of removing noise is improved comparing with the bilateral filtering. In the condition of off-line, the color images and processed images are used to build point clouds. Finally, experimental results demonstrate that our method improves the speed of processing time of depth image and the effect of point cloud which has been built.
The Practicality of Statistical Physics Handout Based on KKNI and the Constructivist Approach
Sari, S. Y.; Afrizon, R.
2018-04-01
Statistical physics lecture shows that: 1) the performance of lecturers, social climate, students’ competence and soft skills needed at work are in enough category, 2) students feel difficulties in following the lectures of statistical physics because it is abstract, 3) 40.72% of students needs more understanding in the form of repetition, practice questions and structured tasks, and 4) the depth of statistical physics material needs to be improved gradually and structured. This indicates that learning materials in accordance of The Indonesian National Qualification Framework or Kerangka Kualifikasi Nasional Indonesia (KKNI) with the appropriate learning approach are needed to help lecturers and students in lectures. The author has designed statistical physics handouts which have very valid criteria (90.89%) according to expert judgment. In addition, the practical level of handouts designed also needs to be considered in order to be easy to use, interesting and efficient in lectures. The purpose of this research is to know the practical level of statistical physics handout based on KKNI and a constructivist approach. This research is a part of research and development with 4-D model developed by Thiagarajan. This research activity has reached part of development test at Development stage. Data collection took place by using a questionnaire distributed to lecturers and students. Data analysis using descriptive data analysis techniques in the form of percentage. The analysis of the questionnaire shows that the handout of statistical physics has very practical criteria. The conclusion of this study is statistical physics handouts based on the KKNI and constructivist approach have been practically used in lectures.
Challenges and Approaches to Statistical Design and Inference in High Dimensional Investigations
Garrett, Karen A.; Allison, David B.
2015-01-01
Summary Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other “omic” data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology, and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. PMID:19588106
Challenges and approaches to statistical design and inference in high-dimensional investigations.
Gadbury, Gary L; Garrett, Karen A; Allison, David B
2009-01-01
Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other "omic" data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative.
Linear theory for filtering nonlinear multiscale systems with model error.
Berry, Tyrus; Harlim, John
2014-07-08
In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering
A statistical-based approach for acoustic tomography of the atmosphere.
Kolouri, Soheil; Azimi-Sadjadi, Mahmood R; Ziemann, Astrid
2014-01-01
Acoustic travel-time tomography of the atmosphere is a nonlinear inverse problem which attempts to reconstruct temperature and wind velocity fields in the atmospheric surface layer using the dependence of sound speed on temperature and wind velocity fields along the propagation path. This paper presents a statistical-based acoustic travel-time tomography algorithm based on dual state-parameter unscented Kalman filter (UKF) which is capable of reconstructing and tracking, in time, temperature, and wind velocity fields (state variables) as well as the dynamic model parameters within a specified investigation area. An adaptive 3-D spatial-temporal autoregressive model is used to capture the state evolution in the UKF. The observations used in the dual state-parameter UKF process consist of the acoustic time of arrivals measured for every pair of transmitter/receiver nodes deployed in the investigation area. The proposed method is then applied to the data set collected at the Meteorological Observatory Lindenberg, Germany, as part of the STINHO experiment, and the reconstruction results are presented.
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer
2015-12-07
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.
Multi-template Scale-Adaptive Kernelized Correlation Filters
Bibi, Adel Aamer; Ghanem, Bernard
2015-01-01
This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.
An Analysis of the Effectiveness of the Constructivist Approach in Teaching Business Statistics
Directory of Open Access Journals (Sweden)
Greeni Maheshwari
2017-05-01
Full Text Available Aim/Purpose: The main aim of the research is to examine the performance of second language English speaking students enrolled in the Business Statistics course and to investigate the academic performance of students when taught under the constructivist and non-constructivist approaches in a classroom environment. Background: There are different learning theories that are established based on how students learn. Each of these theories has its own benefits based on the different type of learners and context of the environment. The students in this research are new to the University environment and to a challenging technical course like Business Statistics. This research has been carried out to see the effectiveness of the constructivist approach in motivating and increasing the student engagement and their academic performance. Methodology\t: A total of 1373 students were involved in the quasi-experiment method using Stratified Sampling Method from the year 2015 until 2016. Contribution: To consider curriculum adjustments for first year programs and implications for teacher education. Findings: The t-test for unequal variances was used to understand the mean score. Results indicate students have high motivation level and achieve higher mean scores when they are taught using the constructivist teaching approach compared to the non-constructivist teaching approach. Recommendations for Practitioners: To consider the challenges faced by first year students and create a teaching approach that fits their needs. Recommendation for Researchers: To explore in depth other teaching approaches of the Business Statistics course in improving students’ academic performance. Impact on Society\t: The constructivist approach will enable learning to be enjoyable and students to be more confident. Future Research: The research will assist other lectures teaching Business Statistics in creating a more conducive environment to encourage second language English
MR image reconstruction via guided filter.
Huang, Heyan; Yang, Hang; Wang, Kang
2018-04-01
Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.
Medina, H.; Romano, N.; Chirico, G. B.
2012-12-01
We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.
Modeling, simulation, and design of SAW grating filters
Schwelb, Otto; Adler, E. L.; Slaboszewicz, J. K.
1990-05-01
A systematic procedure for modeling, simulating, and designing SAW (surface acoustic wave) grating filters, taking losses into account, is described. Grating structures and IDTs (interdigital transducers) coupling to SAWs are defined by cascadable transmission-matrix building blocks. Driving point and transfer characteristics (immittances) of complex architectures consisting of gratings, transducers, and coupling networks are obtained by chain-multiplying building-block matrices. This modular approach to resonator filter analysis and design combines the elements of lossy filter synthesis with the transmission-matrix description of SAW components. A multipole filter design procedure based on a lumped-element-model approximation of one-pole two-port resonator building blocks is given and the range of validity of this model examined. The software for simulating the performance of SAW grating devices based on this matrix approach is described, and its performance, when linked to the design procedure to form a CAD/CAA (computer-aided design and analysis) multiple-filter design package, is illustrated with a resonator filter design example.
Unscented Kalman filter for SINS alignment
Institute of Scientific and Technical Information of China (English)
Zhou Zhanxin; Gao Yanan; Chen Jiabin
2007-01-01
In order to improve the filter accuracy for the nonlinear error model of strapdown inertial navigation system (SINS) alignment, Unscented Kalman Filter (UKF) is presented for simulation with stationary base and moving base of SINS alignment.Simulation results show the superior performance of this approach when compared with classical suboptimal techniques such as extended Kalman filter in cases of large initial misalignment.The UKF has good performance in case of small initial misalignment.
Modelling diversity in building occupant behaviour: a novel statistical approach
DEFF Research Database (Denmark)
Haldi, Frédéric; Calì, Davide; Andersen, Rune Korsholm
2016-01-01
We propose an advanced modelling framework to predict the scope and effects of behavioural diversity regarding building occupant actions on window openings, shading devices and lighting. We develop a statistical approach based on generalised linear mixed models to account for the longitudinal nat...
Nonlinear filtering for LIDAR signal processing
Directory of Open Access Journals (Sweden)
D. G. Lainiotis
1996-01-01
Full Text Available LIDAR (Laser Integrated Radar is an engineering problem of great practical importance in environmental monitoring sciences. Signal processing for LIDAR applications involves highly nonlinear models and consequently nonlinear filtering. Optimal nonlinear filters, however, are practically unrealizable. In this paper, the Lainiotis's multi-model partitioning methodology and the related approximate but effective nonlinear filtering algorithms are reviewed and applied to LIDAR signal processing. Extensive simulation and performance evaluation of the multi-model partitioning approach and its application to LIDAR signal processing shows that the nonlinear partitioning methods are very effective and significantly superior to the nonlinear extended Kalman filter (EKF, which has been the standard nonlinear filter in past engineering applications.
On a multiscale approach for filter efficiency simulations
Iliev, Oleg; Lakdawala, Zahra; Printsypar, Galina
2014-01-01
geometry) of the filtering medium and on the diameter distribution of the particles. The deposited (captured) particles change the microstructure of the porous media what leads to change of permeability. The changed permeability directly influences
Experimental design techniques in statistical practice a practical software-based approach
Gardiner, W P
1998-01-01
Provides an introduction to the diverse subject area of experimental design, with many practical and applicable exercises to help the reader understand, present and analyse the data. The pragmatic approach offers technical training for use of designs and teaches statistical and non-statistical skills in design and analysis of project studies throughout science and industry. Provides an introduction to the diverse subject area of experimental design and includes practical and applicable exercises to help understand, present and analyse the data Offers technical training for use of designs and teaches statistical and non-statistical skills in design and analysis of project studies throughout science and industry Discusses one-factor designs and blocking designs, factorial experimental designs, Taguchi methods and response surface methods, among other topics.
Li, Jingyi; Liu, Qian; Alsamarri, Hussein; Lounsbury, Jenny A; Haversitick, Doris M; Landers, James P
2013-03-07
Reliable measurement of DNA concentration is essential for a broad range of applications in biology and molecular biology, and for many of these, quantifying the nucleic acid content is inextricably linked to obtaining optimal results. In its most simplistic form, quantitative analysis of nucleic acids can be accomplished by UV-Vis absorbance and, in more sophisticated format, by fluorimetry. A recently reported new concept, the 'pinwheel assay', involves a label-free approach for quantifying DNA through aggregation of paramagnetic beads in a rotating magnetic field. Here, we describe a simplified version of that assay adapted for execution using only a pipet and filter paper. The 'pipette, aggregate, and blot' (PAB) approach allows DNA to induce bead aggregation in a pipette tip through exposure to a magnetic field, followed by dispensing (blotting) onto filter paper. The filter paper immortalises the extent of aggregation, and digital images of the immortalized bead conformation, acquired with either a document scanner or a cell phone camera, allows for DNA quantification using a noncomplex algorithm. Human genomic DNA samples extracted from blood are quantified with the PAB approach and the results utilized to define the volume of sample used in a PCR reaction that is sensitive to input mass of template DNA. Integrating the PAB assay with paper-based DNA extraction and detection modalities has the potential to yield 'DNA quant-on-paper' devices that may be useful for point-of-care testing.
Statistical mechanics of learning: A variational approach for real data
International Nuclear Information System (INIS)
Malzahn, Doerthe; Opper, Manfred
2002-01-01
Using a variational technique, we generalize the statistical physics approach of learning from random examples to make it applicable to real data. We demonstrate the validity and relevance of our method by computing approximate estimators for generalization errors that are based on training data alone
Selection of noise parameters for Kalman filter
Institute of Scientific and Technical Information of China (English)
Ka-Veng Yuen; Ka-In Hoi; Kai-Meng Mok
2007-01-01
The Bayesian probabilistic approach is proposed to estimate the process noise and measurement noise parameters for a Kalman filter. With state vectors and covariance matrices estimated by the Kalman filter, the likehood of the measurements can be constructed as a function of the process noise and measurement noise parameters. By maximizing the likklihood function with respect to these noise parameters, the optimal values can be obtained. Furthermore, the Bayesian probabilistic approach allows the associated uncertainty to be quantified. Examples using a single-degree-of-freedom system and a ten-story building illustrate the proposed method. The effect on the performance of the Kalman filter due to the selection of the process noise and measurement noise parameters was demonstrated. The optimal values of the noise parameters were found to be close to the actual values in the sense that the actual parameters were in the region with significant probability density. Through these examples, the Bayesian approach was shown to have the capability to provide accurate estimates of the noise parameters of the Kalman filter, and hence for state estimation.
GENERALIZATION OF RAYLEIGH MAXIMUM LIKELIHOOD DESPECKLING FILTER USING QUADRILATERAL KERNELS
Directory of Open Access Journals (Sweden)
S. Sridevi
2013-02-01
Full Text Available Speckle noise is the most prevalent noise in clinical ultrasound images. It visibly looks like light and dark spots and deduce the pixel intensity as murkiest. Gazing at fetal ultrasound images, the impact of edge and local fine details are more palpable for obstetricians and gynecologists to carry out prenatal diagnosis of congenital heart disease. A robust despeckling filter has to be contrived to proficiently suppress speckle noise and simultaneously preserve the features. The proposed filter is the generalization of Rayleigh maximum likelihood filter by the exploitation of statistical tools as tuning parameters and use different shapes of quadrilateral kernels to estimate the noise free pixel from neighborhood. The performance of various filters namely Median, Kuwahura, Frost, Homogenous mask filter and Rayleigh maximum likelihood filter are compared with the proposed filter in terms PSNR and image profile. Comparatively the proposed filters surpass the conventional filters.
Combination of Wiener filtering and singular value decomposition filtering for volume imaging PET
International Nuclear Information System (INIS)
Shao, L.; Lewitt, R.M.; Karp, J.S.
1995-01-01
Although the three-dimensional (3D) multi-slice rebinning (MSRB) algorithm in PET is fast and practical, and provides an accurate reconstruction, the MSRB image, in general, suffers from the noise amplified by its singular value decomposition (SVD) filtering operation in the axial direction. Their aim in this study is to combine the use of the Wiener filter (WF) with the SVD to decrease the noise and improve the image quality. The SVD filtering ''deconvolves'' the spatially variant axial response function while the WF suppresses the noise and reduces the blurring not modeled by the axial SVD filter but included in the system modulation transfer function. Therefore, the synthesis of these two techniques combines the advantages of both filters. The authors applied this approach to the volume imaging HEAD PENN-PET brain scanner with an axial extent of 256 mm. This combined filter was evaluated in terms of spatial resolution, image contrast, and signal-to-noise ratio with several phantoms, such as a cold sphere phantom and 3D brain phantom. Specifically, the authors studied both the SVD filter with an axial Wiener filter and the SVD filter with a 3D Wiener filter, and compared the filtered images to those from the 3D reprojection (3DRP) reconstruction algorithm. Their results indicate that the Wiener filter increases the signal-to-noise ratio and also improves the contrast. For the MSRB images of the 3D brain phantom, after 3D WF, both the Gray/White and Gray/Ventricle ratios were improved from 1.8 to 2.8 and 2.1 to 4.1, respectively. In addition, the image quality with the MSRB algorithm is close to that of the 3DRP algorithm with 3D WF applied to both image reconstructions
A robust nonlinear filter for image restoration.
Koivunen, V
1995-01-01
A class of nonlinear regression filters based on robust estimation theory is introduced. The goal of the filtering is to recover a high-quality image from degraded observations. Models for desired image structures and contaminating processes are employed, but deviations from strict assumptions are allowed since the assumptions on signal and noise are typically only approximately true. The robustness of filters is usually addressed only in a distributional sense, i.e., the actual error distribution deviates from the nominal one. In this paper, the robustness is considered in a broad sense since the outliers may also be due to inappropriate signal model, or there may be more than one statistical population present in the processing window, causing biased estimates. Two filtering algorithms minimizing a least trimmed squares criterion are provided. The design of the filters is simple since no scale parameters or context-dependent threshold values are required. Experimental results using both real and simulated data are presented. The filters effectively attenuate both impulsive and nonimpulsive noise while recovering the signal structure and preserving interesting details.
A Series-LC-Filtered Active Trap Filter for High Power Voltage Source Inverter
DEFF Research Database (Denmark)
Bai, Haofeng; Wang, Xiongfei; Loh, Poh Chiang
2016-01-01
Passive trap filters are widely used in high power Voltage Source Inverters (VSI) for the switching harmonic attenuation. The usage of the passive trap filters requires clustered and fixed switching harmonic spectrum, which is not the case for low pulse-ratio or Variable Switching Frequency (VSF...... current control of the auxiliary converter, which can be challenging considering that the switching harmonics have very high orders. In this paper, an Active Trap Filter (ATF) based on output impedance shaping is proposed. It is able to bypass the switching harmonics by providing nearly zero output...... impedance. A series-LC-filter is used to reduce the power rating and synthesize the desired output impedance of the ATF. Compared with the existing approaches, the compensated frequency range is greatly enlarged. Also, the current reference is simply set to zero, which reduces the complexity of the control...
Folmsbee, Martha; Lentine, Kerry Roche; Wright, Christine; Haake, Gerhard; Mcburnie, Leesa; Ashtekar, Dilip; Beck, Brian; Hutchison, Nick; Okhio-Seaman, Laura; Potts, Barbara; Pawar, Vinayak; Windsor, Helena
2014-01-01
Mycoplasma are bacteria that can penetrate 0.2 and 0.22 μm rated sterilizing-grade filters and even some 0.1 μm rated filters. Primary applications for mycoplasma filtration include large scale mammalian and bacterial cell culture media and serum filtration. The Parenteral Drug Association recognized the absence of standard industry test parameters for testing and classifying 0.1 μm rated filters for mycoplasma clearance and formed a task force to formulate consensus test parameters. The task force established some test parameters by common agreement, based upon general industry practices, without the need for additional testing. However, the culture medium and incubation conditions, for generating test mycoplasma cells, varied from filter company to filter company and was recognized as a serious gap by the task force. Standardization of the culture medium and incubation conditions required collaborative testing in both commercial filter company laboratories and in an Independent laboratory (Table I). The use of consensus test parameters will facilitate the ultimate cross-industry goal of standardization of 0.1 μm filter claims for mycoplasma clearance. However, it is still important to recognize filter performance will depend on the actual conditions of use. Therefore end users should consider, using a risk-based approach, whether process-specific evaluation of filter performance may be warranted for their application. Mycoplasma are small bacteria that have the ability to penetrate sterilizing-grade filters. Filtration of large-scale mammalian and bacterial cell culture media is an example of an industry process where effective filtration of mycoplasma is required. The Parenteral Drug Association recognized the absence of industry standard test parameters for evaluating mycoplasma clearance filters by filter manufacturers and formed a task force to formulate such a consensus among manufacturers. The use of standardized test parameters by filter manufacturers
CF4CF: Recommending Collaborative Filtering algorithms using Collaborative Filtering
Cunha, Tiago; Soares, Carlos; de Carvalho, André C. P. L. F.
2018-01-01
Automatic solutions which enable the selection of the best algorithms for a new problem are commonly found in the literature. One research area which has recently received considerable efforts is Collaborative Filtering. Existing work includes several approaches using Metalearning, which relate the characteristics of datasets with the performance of the algorithms. This work explores an alternative approach to tackle this problem. Since, in essence, both are recommendation problems, this work...
Introduction to the Box Particle Filtering
Gning, Amadou; Ristic, B; Mihaylova, Lyudmila; Abdallah, F.
2013-01-01
This paper presents a novel method for solving nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relies on the concept of a box particle, which occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are in its reduced c...
Nonlinear Kalman filtering in affine term structure models
DEFF Research Database (Denmark)
Christoffersen, Peter; Dorion, Christian; Jacobs, Kris
2014-01-01
The extended Kalman filter, which linearizes the relationship between security prices and state variables, is widely used in fixed-income applications. We investigate whether the unscented Kalman filter should be used to capture nonlinearities and compare the performance of the Kalman filter...... with that of the particle filter. We analyze the cross section of swap rates, which are mildly nonlinear in the states, and cap prices, which are highly nonlinear. When caps are used to filter the states, the unscented Kalman filter significantly outperforms its extended counterpart. The unscented Kalman filter also...... performs well when compared with the much more computationally intensive particle filter. These findings suggest that the unscented Kalman filter may be a good approach for a variety of problems in fixed-income pricing....
Deraemaeker, A.; Worden, K.
2018-05-01
This paper discusses the possibility of using the Mahalanobis squared-distance to perform robust novelty detection in the presence of important environmental variability in a multivariate feature vector. By performing an eigenvalue decomposition of the covariance matrix used to compute that distance, it is shown that the Mahalanobis squared-distance can be written as the sum of independent terms which result from a transformation from the feature vector space to a space of independent variables. In general, especially when the size of the features vector is large, there are dominant eigenvalues and eigenvectors associated with the covariance matrix, so that a set of principal components can be defined. Because the associated eigenvalues are high, their contribution to the Mahalanobis squared-distance is low, while the contribution of the other components is high due to the low value of the associated eigenvalues. This analysis shows that the Mahalanobis distance naturally filters out the variability in the training data. This property can be used to remove the effect of the environment in damage detection, in much the same way as two other established techniques, principal component analysis and factor analysis. The three techniques are compared here using real experimental data from a wooden bridge for which the feature vector consists in eigenfrequencies and modeshapes collected under changing environmental conditions, as well as damaged conditions simulated with an added mass. The results confirm the similarity between the three techniques and the ability to filter out environmental effects, while keeping a high sensitivity to structural changes. The results also show that even after filtering out the environmental effects, the normality assumption cannot be made for the residual feature vector. An alternative is demonstrated here based on extreme value statistics which results in a much better threshold which avoids false positives in the training data, while
COMPARATIVE EVALUATION OF FILTERS USED IN TRACKING AIR TARGETS
Directory of Open Access Journals (Sweden)
Y. I. Strekalovskaya
2015-01-01
Full Text Available Using an imitation model for a flow of heterogeneous air targets the comparative assessment of the αβ, αβγ and the Kalman filters efficiency is evaluated. In the case of slightly maneuvering target the difference in filters’ efficiency is statistically insignificant; in the case of sharp maneuvering the Kalman filter is significantly more precise.
A collaborative filtering approach for protein-protein docking scoring functions.
Bourquard, Thomas; Bernauer, Julie; Azé, Jérôme; Poupon, Anne
2011-04-22
A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions. In this work, we introduce a new procedure for conformation ranking. We previously developed a set of scoring functions where learning was performed using a genetic algorithm. These functions were used to assign a rank to each possible conformation. We now have a refined rank using different classifiers (decision trees, rules and support vector machines) in a collaborative filtering scheme. The scoring function newly obtained is evaluated using 10 fold cross-validation, and compared to the functions obtained using either genetic algorithms or collaborative filtering taken separately. This new approach was successfully applied to the CAPRI scoring ensembles. We show that for 10 targets out of 12, we are able to find a near-native conformation in the 10 best ranked solutions. Moreover, for 6 of them, the near-native conformation selected is of high accuracy. Finally, we show that this function dramatically enriches the 100 best-ranking conformations in near-native structures.
Quantum Image Filtering in the Frequency Domain
Directory of Open Access Journals (Sweden)
MANTA, V. I.
2013-08-01
Full Text Available In this paper we address the emerging field of Quantum Image Processing. We investigate the use of quantum computing systems to represent and manipulate images. In particular, we consider the basic task of image filtering. We prove that a quantum version for this operation can be achieved, even though the quantum convolution of two sequences is physically impossible. In our approach we use the principle of the quantum oracle to implement the filter function. We provide the quantum circuit that implements the filtering task and present the results of several simulation experiments on grayscale images. There are important differences between the classical and the quantum implementations for image filtering. We analyze these differences and show that the major advantage of the quantum approach lies in the exploitation of the efficient implementation of the quantum Fourier transform.
Performance Comparison of Various Filters Media in
Directory of Open Access Journals (Sweden)
Lilyan Yaqup Matti
2013-05-01
Full Text Available In this research, a bench-scale filter is designed and constructed in order to compare the performance of different media namely, sand, crushed marble stone and crushed red brick. The filters are operated under various operating conditions such as filter depth, raw water turbidity, pretreatment, effective size and uniformity coefficient. These filters are operated under conventional and direct filtration modes with different doses of alum. Statistical methods had been used to determine the best media using Duncan multiple range test. The result showed the superiority of crushed red brick media in the removal of turbidity and total bacteria. The results also indicated that filters operated under direct filtration mode show better performance than that operated under conventional filtration mode. The pH of treated water show slight increase for the two modes of filtration.
A robust spatial filtering technique for multisource localization and geoacoustic inversion.
Stotts, S A
2005-07-01
Geoacoustic inversion and source localization using beamformed data from a ship of opportunity has been demonstrated with a bottom-mounted array. An alternative approach, which lies within a class referred to as spatial filtering, transforms element level data into beam data, applies a bearing filter, and transforms back to element level data prior to performing inversions. Automation of this filtering approach is facilitated for broadband applications by restricting the inverse transform to the degrees of freedom of the array, i.e., the effective number of elements, for frequencies near or below the design frequency. A procedure is described for nonuniformly spaced elements that guarantees filter stability well above the design frequency. Monitoring energy conservation with respect to filter output confirms filter stability. Filter performance with both uniformly spaced and nonuniformly spaced array elements is discussed. Vertical (range and depth) and horizontal (range and bearing) ambiguity surfaces are constructed to examine filter performance. Examples that demonstrate this filtering technique with both synthetic data and real data are presented along with comparisons to inversion results using beamformed data. Examinations of cost functions calculated within a simulated annealing algorithm reveal the efficacy of the approach.
International Nuclear Information System (INIS)
Glaze, J.
1974-01-01
It is known that small scale beam instabilities limit the focusable energy that can be achieved from a terawatt laser chain. Spatial filters are currently being used on CYCLOPS to ameliorate this problem. Realizing the full advantage of such a filter, however, may require certain staging modifications. A staging methodology is discussed that should be applicable to the CYCLOPS, 381, and SHIVA systems. Experiments are in progress on CYCLOPS that will address directly the utility of the proposed approach
DEFF Research Database (Denmark)
D'Agostino, Maria-Antonietta; Boers, Maarten; Kirwan, John
2014-01-01
OBJECTIVE: The Outcome Measures in Rheumatology (OMERACT) Filter provides a framework for the validation of outcome measures for use in rheumatology clinical research. However, imaging and biochemical measures may face additional validation challenges because of their technical nature. The Imaging...... using the original OMERACT Filter and the newly proposed structure. Breakout groups critically reviewed the extent to which the candidate biomarkers complied with the proposed stepwise approach, as a way of examining the utility of the proposed 3-dimensional structure. RESULTS: Although...... was obtained for a proposed tri-axis structure to assess validation of imaging and soluble biomarkers; nevertheless, additional work is required to better evaluate its place within the OMERACT Filter 2.0....
A novel spatiotemporal muscle activity imaging approach based on the Extended Kalman Filter.
Wang, Jing; Zhang, Yingchun; Zhu, Xiangjun; Zhou, Ping; Liu, Chenguang; Rymer, William Z
2012-01-01
A novel spatiotemporal muscle activity imaging (sMAI) approach has been developed using the Extended Kalman Filter (EKF) to reconstruct internal muscle activities from non-invasive multi-channel surface electromyogram (sEMG) recordings. A distributed bioelectric dipole source model is employed to describe the internal muscle activity space, and a linear relationship between the muscle activity space and the sEMG measurement space is then established. The EKF is employed to recursively solve the ill-posed inverse problem in the sMAI approach, in which the weighted minimum norm (WMN) method is utilized to calculate the initial state and a new nonlinear method is developed based on the propagating features of muscle activities to predict the recursive state. A series of computer simulations was conducted to test the performance of the proposed sMAI approach. Results show that the localization error rapidly decreases over 35% and the overlap ratio rapidly increases over 45% compared to the results achieved using the WMN method only. The present promising results demonstrate the feasibility of utilizing the proposed EKF-based sMAI approach to accurately reconstruct internal muscle activities from non-invasive sEMG recordings.
Luo, Xin; You, Zhuhong; Zhou, Mengchu; Li, Shuai; Leung, Hareton; Xia, Yunni; Zhu, Qingsheng
2015-01-09
The comprehensive mapping of protein-protein interactions (PPIs) is highly desired for one to gain deep insights into both fundamental cell biology processes and the pathology of diseases. Finely-set small-scale experiments are not only very expensive but also inefficient to identify numerous interactomes despite their high accuracy. High-throughput screening techniques enable efficient identification of PPIs; yet the desire to further extract useful knowledge from these data leads to the problem of binary interactome mapping. Network topology-based approaches prove to be highly efficient in addressing this problem; however, their performance deteriorates significantly on sparse putative PPI networks. Motivated by the success of collaborative filtering (CF)-based approaches to the problem of personalized-recommendation on large, sparse rating matrices, this work aims at implementing a highly efficient CF-based approach to binary interactome mapping. To achieve this, we first propose a CF framework for it. Under this framework, we model the given data into an interactome weight matrix, where the feature-vectors of involved proteins are extracted. With them, we design the rescaled cosine coefficient to model the inter-neighborhood similarity among involved proteins, for taking the mapping process. Experimental results on three large, sparse datasets demonstrate that the proposed approach outperforms several sophisticated topology-based approaches significantly.
Gyftakis, Konstantinos N.; Marques Cardoso, Antonio J.; Antonino-Daviu, Jose A.
2017-09-01
The Park's Vector Approach (PVA), together with its variations, has been one of the most widespread diagnostic methods for electrical machines and drives. Regarding the broken rotor bars fault diagnosis in induction motors, the common practice is to rely on the width increase of the Park's Vector (PV) ring and then apply some more sophisticated signal processing methods. It is shown in this paper that this method can be unreliable and is strongly dependent on the magnetic poles and rotor slot numbers. To overcome this constraint, the novel Filtered Park's/Extended Park's Vector Approach (FPVA/FEPVA) is introduced. The investigation is carried out with FEM simulations and experimental testing. The results prove to satisfyingly coincide, whereas the proposed advanced FPVA method is desirably reliable.
Hydrodynamics of microbial filter feeding.
Nielsen, Lasse Tor; Asadzadeh, Seyed Saeed; Dölger, Julia; Walther, Jens H; Kiørboe, Thomas; Andersen, Anders
2017-08-29
Microbial filter feeders are an important group of grazers, significant to the microbial loop, aquatic food webs, and biogeochemical cycling. Our understanding of microbial filter feeding is poor, and, importantly, it is unknown what force microbial filter feeders must generate to process adequate amounts of water. Also, the trade-off in the filter spacing remains unexplored, despite its simple formulation: A filter too coarse will allow suitably sized prey to pass unintercepted, whereas a filter too fine will cause strong flow resistance. We quantify the feeding flow of the filter-feeding choanoflagellate Diaphanoeca grandis using particle tracking, and demonstrate that the current understanding of microbial filter feeding is inconsistent with computational fluid dynamics (CFD) and analytical estimates. Both approaches underestimate observed filtration rates by more than an order of magnitude; the beating flagellum is simply unable to draw enough water through the fine filter. We find similar discrepancies for other choanoflagellate species, highlighting an apparent paradox. Our observations motivate us to suggest a radically different filtration mechanism that requires a flagellar vane (sheet), something notoriously difficult to visualize but sporadically observed in the related choanocytes (sponges). A CFD model with a flagellar vane correctly predicts the filtration rate of D. grandis , and using a simple model we can account for the filtration rates of other microbial filter feeders. We finally predict how optimum filter mesh size increases with cell size in microbial filter feeders, a prediction that accords very well with observations. We expect our results to be of significance for small-scale biophysics and trait-based ecological modeling.
New advances in the statistical parton distributions approach*
Directory of Open Access Journals (Sweden)
Soffer Jacques
2016-01-01
Full Text Available The quantum statistical parton distributions approach proposed more than one decade ago is revisited by considering a larger set of recent and accurate Deep Inelastic Scattering experimental results. It enables us to improve the description of the data by means of a new determination of the parton distributions. This global next-to-leading order QCD analysis leads to a good description of several structure functions, involving unpolarized parton distributions and helicity distributions, in terms of a rather small number of free parameters. There are many serious challenging issues. The predictions of this theoretical approach will be tested for single-jet production and charge asymmetry in W± production in p̄p and pp collisions up to LHC energies, using recent data and also for forthcoming experimental results.
A Low Cost Structurally Optimized Design for Diverse Filter Types
Kazmi, Majida; Aziz, Arshad; Akhtar, Pervez; Ikram, Nassar
2016-01-01
A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to 75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image
Introducing linear functions: an alternative statistical approach
Nolan, Caroline; Herbert, Sandra
2015-12-01
The introduction of linear functions is the turning point where many students decide if mathematics is useful or not. This means the role of parameters and variables in linear functions could be considered to be `threshold concepts'. There is recognition that linear functions can be taught in context through the exploration of linear modelling examples, but this has its limitations. Currently, statistical data is easily attainable, and graphics or computer algebra system (CAS) calculators are common in many classrooms. The use of this technology provides ease of access to different representations of linear functions as well as the ability to fit a least-squares line for real-life data. This means these calculators could support a possible alternative approach to the introduction of linear functions. This study compares the results of an end-of-topic test for two classes of Australian middle secondary students at a regional school to determine if such an alternative approach is feasible. In this study, test questions were grouped by concept and subjected to concept by concept analysis of the means of test results of the two classes. This analysis revealed that the students following the alternative approach demonstrated greater competence with non-standard questions.
A novel approach for choosing summary statistics in approximate Bayesian computation.
Aeschbacher, Simon; Beaumont, Mark A; Futschik, Andreas
2012-11-01
The choice of summary statistics is a crucial step in approximate Bayesian computation (ABC). Since statistics are often not sufficient, this choice involves a trade-off between loss of information and reduction of dimensionality. The latter may increase the efficiency of ABC. Here, we propose an approach for choosing summary statistics based on boosting, a technique from the machine-learning literature. We consider different types of boosting and compare them to partial least-squares regression as an alternative. To mitigate the lack of sufficiency, we also propose an approach for choosing summary statistics locally, in the putative neighborhood of the true parameter value. We study a demographic model motivated by the reintroduction of Alpine ibex (Capra ibex) into the Swiss Alps. The parameters of interest are the mean and standard deviation across microsatellites of the scaled ancestral mutation rate (θ(anc) = 4N(e)u) and the proportion of males obtaining access to matings per breeding season (ω). By simulation, we assess the properties of the posterior distribution obtained with the various methods. According to our criteria, ABC with summary statistics chosen locally via boosting with the L(2)-loss performs best. Applying that method to the ibex data, we estimate θ(anc)≈ 1.288 and find that most of the variation across loci of the ancestral mutation rate u is between 7.7 × 10(-4) and 3.5 × 10(-3) per locus per generation. The proportion of males with access to matings is estimated as ω≈ 0.21, which is in good agreement with recent independent estimates.
Filter Bank Approach to the Estimation of Flexible Modes in Dynamic Systems
National Research Council Canada - National Science Library
Tzellos, Konstantinos
2007-01-01
.... In this thesis the problem of identifying frequencies of disturbances in flexible systems using advanced Digital Signal Processing techniques such as filter banks and Quadrature Mirror Filters is addressed...
Performance of multiple HEPA filters against plutonium aerosols
International Nuclear Information System (INIS)
Gonzales, M.; Elder, J.; Ettinger, H.
1975-01-01
Performance of multiple stages of High Efficiency Particulate Air (HEPA) filters against aerosols similar to those produced by plutonium processing facilities has been verified as part of an experimental program. A system of three HEPA filters in series was tested against 238 PuO 2 aerosol concentrations as high as 3.3 x 10 10 d/s-m 3 . An air nebulization aerosol generation system, using ball milled plutonium oxide suspended in water, provided test aerosols with size characteristics similar to those defined by a field sampling program at several different AEC plutonium processing facilities. Aerosols have been produced ranging from 0.22 μm activity median aerodynamic diameter (amad) to 1.6 μm amad. The smaller size distributions yield 10 to 30 percent of the total activity in the less than 0.22 μm size range allowing efficiency measurement as a function of size for the first two HEPA filters in series. The low level of activity on the sampler downstream of the third HEPA filter (approximately 0.01 c/s) precludes aerosol size characterization downstream of this filter. For the first two HEPA filters, overall efficiency, and efficiency as a function of size, exceeds 99.98 percent including the <0.12 μm and the 0.12 to 0.22 μm size intervals. Efficiency of the third HEPA filter is somewhat lower with an overall average efficiency of 99.8 percent and an apparent minimum efficiency of 99.5 percent. This apparently lower efficiency is an artifact due to the low level of activity on the sampler downstream of HEPA No. 3 and the variations due to counting statistics. Recent runs with higher concentrations, thereby improving statistical variations, show efficiencies well within minimum requirements. (U.S.)
Current-State Constrained Filter Bank for Wald Testing of Spacecraft Conjunctions
Carpenter, J. Russell; Markley, F. Landis
2012-01-01
We propose a filter bank consisting of an ordinary current-state extended Kalman filter, and two similar but constrained filters: one is constrained by a null hypothesis that the miss distance between two conjuncting spacecraft is inside their combined hard body radius at the predicted time of closest approach, and one is constrained by an alternative complementary hypothesis. The unconstrained filter is the basis of an initial screening for close approaches of interest. Once the initial screening detects a possibly risky conjunction, the unconstrained filter also governs measurement editing for all three filters, and predicts the time of closest approach. The constrained filters operate only when conjunctions of interest occur. The computed likelihoods of the innovations of the two constrained filters form a ratio for a Wald sequential probability ratio test. The Wald test guides risk mitigation maneuver decisions based on explicit false alarm and missed detection criteria. Since only current-state Kalman filtering is required to compute the innovations for the likelihood ratio, the present approach does not require the mapping of probability density forward to the time of closest approach. Instead, the hard-body constraint manifold is mapped to the filter update time by applying a sigma-point transformation to a projection function. Although many projectors are available, we choose one based on Lambert-style differential correction of the current-state velocity. We have tested our method using a scenario based on the Magnetospheric Multi-Scale mission, scheduled for launch in late 2014. This mission involves formation flight in highly elliptical orbits of four spinning spacecraft equipped with antennas extending 120 meters tip-to-tip. Eccentricities range from 0.82 to 0.91, and close approaches generally occur in the vicinity of perigee, where rapid changes in geometry may occur. Testing the method using two 12,000-case Monte Carlo simulations, we found the
Papa, Lesther A; Litson, Kaylee; Lockhart, Ginger; Chassin, Laurie; Geiser, Christian
2015-01-01
Testing mediation models is critical for identifying potential variables that need to be targeted to effectively change one or more outcome variables. In addition, it is now common practice for clinicians to use multiple informant (MI) data in studies of statistical mediation. By coupling the use of MI data with statistical mediation analysis, clinical researchers can combine the benefits of both techniques. Integrating the information from MIs into a statistical mediation model creates various methodological and practical challenges. The authors review prior methodological approaches to MI mediation analysis in clinical research and propose a new latent variable approach that overcomes some limitations of prior approaches. An application of the new approach to mother, father, and child reports of impulsivity, frustration tolerance, and externalizing problems (N = 454) is presented. The results showed that frustration tolerance mediated the relationship between impulsivity and externalizing problems. The new approach allows for a more comprehensive and effective use of MI data when testing mediation models.
Stain Deconvolution Using Statistical Analysis of Multi-Resolution Stain Colour Representation.
Directory of Open Access Journals (Sweden)
Najah Alsubaie
Full Text Available Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners.
Sensitivity filtering from a continuum mechanics perspective
DEFF Research Database (Denmark)
Sigmund, Ole; Maute, Kurt
2012-01-01
In topology optimization filtering is a popular approach for preventing numerical instabilities. This short note shows that the well-known sensitivity filtering technique, that prevents checkerboards and ensures mesh-independent designs in density-based topology optimization, is equivalent to min...... to minimizing compliance for nonlocal elasticity problems known from continuum mechanics. Hence, the note resolves the long-standing quest for finding an explanation and physical motivation for the sensitivity filter....
Biagini, Francesca
2016-01-01
This book provides an introduction to elementary probability and to Bayesian statistics using de Finetti's subjectivist approach. One of the features of this approach is that it does not require the introduction of sample space – a non-intrinsic concept that makes the treatment of elementary probability unnecessarily complicate – but introduces as fundamental the concept of random numbers directly related to their interpretation in applications. Events become a particular case of random numbers and probability a particular case of expectation when it is applied to events. The subjective evaluation of expectation and of conditional expectation is based on an economic choice of an acceptable bet or penalty. The properties of expectation and conditional expectation are derived by applying a coherence criterion that the evaluation has to follow. The book is suitable for all introductory courses in probability and statistics for students in Mathematics, Informatics, Engineering, and Physics.
In-place testing of off-gas iodine filters
International Nuclear Information System (INIS)
Duce, S.W.; Tkachyk, J.W.; Motes, B.G.
1980-01-01
At the Idaho National Engineering Laboratory, both charcoal and silver zeolite (AgX) filters are used for radioactive iodine off-gas cleanup of reactor systems. These filters are used in facilities which are conducting research in the areas of reactor fuel failure, reactor fuel inspection, and loss of fluids from reactor vessels. Iodine retention efficiency testing of these filters is dictated by prudent safety practices and regulatory guidelines. A procedure for determining iodine off-gas filter efficiency in-place has been developed and tested on both AgX and charcoal filters. The procedure involves establishing sample points upstream and downstream of the filter to be tested. A step-by-step approach for filter efficiency testing is presented
MEDOF - MINIMUM EUCLIDEAN DISTANCE OPTIMAL FILTER
Barton, R. S.
1994-01-01
The Minimum Euclidean Distance Optimal Filter program, MEDOF, generates filters for use in optical correlators. The algorithm implemented in MEDOF follows theory put forth by Richard D. Juday of NASA/JSC. This program analytically optimizes filters on arbitrary spatial light modulators such as coupled, binary, full complex, and fractional 2pi phase. MEDOF optimizes these modulators on a number of metrics including: correlation peak intensity at the origin for the centered appearance of the reference image in the input plane, signal to noise ratio including the correlation detector noise as well as the colored additive input noise, peak to correlation energy defined as the fraction of the signal energy passed by the filter that shows up in the correlation spot, and the peak to total energy which is a generalization of PCE that adds the passed colored input noise to the input image's passed energy. The user of MEDOF supplies the functions that describe the following quantities: 1) the reference signal, 2) the realizable complex encodings of both the input and filter SLM, 3) the noise model, possibly colored, as it adds at the reference image and at the correlation detection plane, and 4) the metric to analyze, here taken to be one of the analytical ones like SNR (signal to noise ratio) or PCE (peak to correlation energy) rather than peak to secondary ratio. MEDOF calculates filters for arbitrary modulators and a wide range of metrics as described above. MEDOF examines the statistics of the encoded input image's noise (if SNR or PCE is selected) and the filter SLM's (Spatial Light Modulator) available values. These statistics are used as the basis of a range for searching for the magnitude and phase of k, a pragmatically based complex constant for computing the filter transmittance from the electric field. The filter is produced for the mesh points in those ranges and the value of the metric that results from these points is computed. When the search is concluded, the
A comparison of nonlinear filtering approaches in the context of an HIV model.
Banks, H Thomas; Hu, Shuhua; Kenz, Zackary R; Tran, Hien T
2010-04-01
In this paper three different filtering methods, the Extended Kalman Filter (EKF), the Gauss-Hermite Filter (GHF), and the Unscented Kalman Filter (UKF), are compared for state-only and coupled state and parameter estimation when used with log state variables of a model of the immunologic response to the human immunodeficiency virus (HIV) in individuals. The filters are implemented to estimate model states as well as model parameters from simulated noisy data, and are compared in terms of estimation accuracy and computational time. Numerical experiments reveal that the GHF is the most computationally expensive algorithm, while the EKF is the least expensive one. In addition, computational experiments suggest that there is little difference in the estimation accuracy between the UKF and GHF. When measurements are taken as frequently as every week to two weeks, the EKF is the superior filter. When measurements are further apart, the UKF is the best choice in the problem under investigation.
Investigation of Alternative Approaches for Cleaning Mott Porous Metal Filters
International Nuclear Information System (INIS)
Poirier, M.R.
2003-01-01
The Department of Energy selected Caustic Side Solvent Extraction (CSSX) as the preferred cesium removal technology for Savannah River Site (SRS) waste. As a pretreatment step for the CSSX flowsheet, the incoming salt solution that contains entrained sludge is contacted with monosodium titanate (MST) to adsorb strontium and select actinides. The resulting slurry is filtered to remove the sludge and MST. Filter fouling occurs during this process. At times, personnel can increase the filtrate rate by backpulsing or scouring. At other times, the filtrate rate drops significantly and only chemical cleaning will restore filter performance. The current baseline technology for filter cleaning uses 0.5 M oxalic acid. The Salt Processing Project (SPP) at SRS, through the Tanks Focus Area, requested an evaluation of other cleaning agents to determine their effectiveness at removing trapped sludge and MST solids compared with the baseline oxalic acid method. A review of the technical literature identified compounds that appear effective at dissolving solid compounds. Consultation with the SPP management team, engineering personnel, and researchers led to a selection of oxalic acid, nitric acid, citric acid, and ascorbic acid for testing. Tests used simulated waste and actual waste as follows. Personnel placed simulated or actual SRS High Level Waste sludge and MST in a beaker. They added the selected cleaning agents, stirred the beakers, and collected supernate samples periodically analyzing for dissolved metals
International Nuclear Information System (INIS)
Huang Kun; Zhao Yi; Xu Ke; Feng Bo; Liang Songnian
2009-01-01
Objective: To investigate the clinical value of domestic ZQL-type inferior vena caval filter through comparing its complications with that of imported inferior vena caval filter. Methods: Domestic ZQL-type inferior vena caval filter was placed in 62 patients (study group) and imported inferior vena caval filter in 58 patients (control group) for the treatment of deep venous thrombosis of the lower limb. Abdominal plain film, lower limb phlebography and/or pulmonary arteriography, color Doppler ultrasonography were performed after the procedure. 3D-CT scanning was carried out when pulmonary embolism was suspected. The complications were documented and analyzed. Results In all patients the inferior vena caval filter was successfully implanted in planned site. In study group, the complications included filter migration over 10 cm (n = 1), thrombus in the filter (n = 1), inferior vena cava obstruction (n = 2) and pulmonary embolism (n = 1). In control group, the complications included thrombus in the filter (n = 2), deep venous thrombus of the lower extremities (n = 2), inferior vena cava obstruction (n = 9), venous perforation by filter (n = 1) and pulmonary embolism (n = 1). The data were compared between two groups and statistic analysis showed no significant difference between two groups (P > 0.05). Conclusion: No significant difference in the occurrence of complications exists between domestic ZQL-type inferior vena caval filter and imported inferior vena caval filter, therefore, domestic ZQL-type inferior vena caval filter can safely substitute for the imported filter. (authors)
Wave-filter-based approach for generation of a quiet space in a rectangular cavity
Iwamoto, Hiroyuki; Tanaka, Nobuo; Sanada, Akira
2018-02-01
This paper is concerned with the generation of a quiet space in a rectangular cavity using active wave control methodology. It is the purpose of this paper to present the wave filtering method for a rectangular cavity using multiple microphones and its application to an adaptive feedforward control system. Firstly, the transfer matrix method is introduced for describing the wave dynamics of the sound field, and then feedforward control laws for eliminating transmitted waves is derived. Furthermore, some numerical simulations are conducted that show the best possible result of active wave control. This is followed by the derivation of the wave filtering equations that indicates the structure of the wave filter. It is clarified that the wave filter consists of three portions; modal group filter, rearrangement filter and wave decomposition filter. Next, from a numerical point of view, the accuracy of the wave decomposition filter which is expressed as a function of frequency is investigated using condition numbers. Finally, an experiment on the adaptive feedforward control system using the wave filter is carried out, demonstrating that a quiet space is generated in the target space by the proposed method.
International Nuclear Information System (INIS)
Angland, P.; Haberberger, D.; Ivancic, S. T.; Froula, D. H.
2017-01-01
Here, a new method of analysis for angular filter refractometry images was developed to characterize laser-produced, long-scale-length plasmas using an annealing algorithm to iterative converge upon a solution. Angular filter refractometry (AFR) is a novel technique used to characterize the density pro files of laser-produced, long-scale-length plasmas. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on a minimization of the χ2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in average uncertainty in the density profile of 5-10% in the region of interest.
Directory of Open Access Journals (Sweden)
Lesther ePapa
2015-11-01
Full Text Available Testing mediation models is critical for identifying potential variables that need to be targeted to effectively change one or more outcome variables. In addition, it is now common practice for clinicians to use multiple informant (MI data in studies of statistical mediation. By coupling the use of MI data with statistical mediation analysis, clinical researchers can combine the benefits of both techniques. Integrating the information from MIs into a statistical mediation model creates various methodological and practical challenges. The authors review prior methodological approaches to MI mediation analysis in clinical research and propose a new latent variable approach that overcomes some limitations of prior approaches. An application of the new approach to mother, father, and child reports of impulsivity, frustration tolerance, and externalizing problems (N = 454 is presented. The results showed that frustration tolerance mediated the relationship between impulsivity and externalizing problems. Advantages and limitations of the new approach are discussed. The new approach can help clinical researchers overcome limitations of prior techniques. It allows for a more comprehensive and effective use of MI data when testing mediation models.
Saito, Masatoshi
2007-11-01
Dual-energy contrast agent-enhanced mammography is a technique of demonstrating breast cancers obscured by a cluttered background resulting from the contrast between soft tissues in the breast. The technique has usually been implemented by exploiting two exposures to different x-ray tube voltages. In this article, another dual-energy approach using the balanced filter method without switching the tube voltages is described. For the spectral optimization of dual-energy mammography using the balanced filters, we applied a theoretical framework reported by Lemacks et al. [Med. Phys. 29, 1739-1751 (2002)] to calculate the signal-to-noise ratio (SNR) in an iodinated contrast agent subtraction image. This permits the selection of beam parameters such as tube voltage and balanced filter material, and the optimization of the latter's thickness with respect to some critical quantity-in this case, mean glandular dose. For an imaging system with a 0.1 mm thick CsI:T1 scintillator, we predict that the optimal tube voltage would be 45 kVp for a tungsten anode using zirconium, iodine, and neodymium balanced filters. A mean glandular dose of 1.0 mGy is required to obtain an SNR of 5 in order to detect 1.0 mg/cm2 iodine in the resulting clutter-free image of a 5 cm thick breast composed of 50% adipose and 50% glandular tissue. In addition to spectral optimization, we carried out phantom measurements to demonstrate the present dual-energy approach for obtaining a clutter-free image, which preferentially shows iodine, of a breast phantom comprising three major components-acrylic spheres, olive oil, and an iodinated contrast agent. The detection of iodine details on the cluttered background originating from the contrast between acrylic spheres and olive oil is analogous to the task of distinguishing contrast agents in a mixture of glandular and adipose tissues.
International Nuclear Information System (INIS)
Saito, Masatoshi
2007-01-01
Dual-energy contrast agent-enhanced mammography is a technique of demonstrating breast cancers obscured by a cluttered background resulting from the contrast between soft tissues in the breast. The technique has usually been implemented by exploiting two exposures to different x-ray tube voltages. In this article, another dual-energy approach using the balanced filter method without switching the tube voltages is described. For the spectral optimization of dual-energy mammography using the balanced filters, we applied a theoretical framework reported by Lemacks et al. [Med. Phys. 29, 1739-1751 (2002)] to calculate the signal-to-noise ratio (SNR) in an iodinated contrast agent subtraction image. This permits the selection of beam parameters such as tube voltage and balanced filter material, and the optimization of the latter's thickness with respect to some critical quantity--in this case, mean glandular dose. For an imaging system with a 0.1 mm thick CsI:Tl scintillator, we predict that the optimal tube voltage would be 45 kVp for a tungsten anode using zirconium, iodine, and neodymium balanced filters. A mean glandular dose of 1.0 mGy is required to obtain an SNR of 5 in order to detect 1.0 mg/cm 2 iodine in the resulting clutter-free image of a 5 cm thick breast composed of 50% adipose and 50% glandular tissue. In addition to spectral optimization, we carried out phantom measurements to demonstrate the present dual-energy approach for obtaining a clutter-free image, which preferentially shows iodine, of a breast phantom comprising three major components - acrylic spheres, olive oil, and an iodinated contrast agent. The detection of iodine details on the cluttered background originating from the contrast between acrylic spheres and olive oil is analogous to the task of distinguishing contrast agents in a mixture of glandular and adipose tissues
Monochromatic filter with multiple manipulation approaches by the layered all-dielectric patch array
International Nuclear Information System (INIS)
Liu, Xiaoshan; Liu, Guiqiang; Fu, Guolan; Liu, Mulin; Liu, Zhengqi
2016-01-01
Monochromatic filtering with ultra-narrowband and high spectral contrast is desirable for wide applications in display, image, and other optoelectronics. However, owing to the inherent omhic losses in the metallic materials, a broadband spectrum with a low Q-factor down to 10 inevitably limits the device performance. Herein, we for the first time theoretically propose and demonstrate an ultra-narrowband color-filtering platform based on the layered all-dielectric meta-material (LADM), which consists of a triple-layer high/low/high-index dielectrics cavity structure. Owing to the lossless dielectric materials used, sharp resonances with the bandwidth down to sub-10 nm are observed in the sub-wavelength LADM-based filters. A spectral Q-factor of 361.6 is achieved, which is orders of magnitude larger than that of the plasmonic resonators. Moreover, for the other significant factor for evaluation of filtering performance, the spectral contrast reaches 94.5%. These optical properties are the main results of the excitation of the resonant modes in the LADMs. Furthermore, polarization-manipulated light filtering is realized in this LADM. The classical Malus law is also confirmed in the reflective spectrum by tuning the polarization state. More interestingly and importantly, the filtering phenomenon shows novel features of the wavelength-independent and tunable resonant intensity for the reflective spectrum when the LADM-based filter is illuminated under an oblique state. High scalability of the sharp reflective spectrum is obtained by tuning the structural parameters. A single-wavelength reflective filtering window is also achieved in the visible frequencies. These features hold promise for the LADM-based filter with wide applications in color engineering, displaying, imaging, etc. (paper)
Robot Trajectories Comparison: A Statistical Approach
Directory of Open Access Journals (Sweden)
A. Ansuategui
2014-01-01
Full Text Available The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners.
Robot Trajectories Comparison: A Statistical Approach
Ansuategui, A.; Arruti, A.; Susperregi, L.; Yurramendi, Y.; Jauregi, E.; Lazkano, E.; Sierra, B.
2014-01-01
The task of planning a collision-free trajectory from a start to a goal position is fundamental for an autonomous mobile robot. Although path planning has been extensively investigated since the beginning of robotics, there is no agreement on how to measure the performance of a motion algorithm. This paper presents a new approach to perform robot trajectories comparison that could be applied to any kind of trajectories and in both simulated and real environments. Given an initial set of features, it automatically selects the most significant ones and performs a statistical comparison using them. Additionally, a graphical data visualization named polygraph which helps to better understand the obtained results is provided. The proposed method has been applied, as an example, to compare two different motion planners, FM2 and WaveFront, using different environments, robots, and local planners. PMID:25525618
Implementation of a nonlinear filter for online nuclear counting
International Nuclear Information System (INIS)
Coulon, R.; Dumazert, J.; Kondrasovs, V.; Normand, S.
2016-01-01
Nuclear counting is a challenging task for nuclear instrumentation because of the stochastic nature of radioactivity. Event counting has to be processed and filtered to determine a stable count rate value and perform variation monitoring of the measured event. An innovative approach for nuclear counting is presented in this study, improving response time and maintaining count rate stability. Some nonlinear filters providing a local maximum likelihood estimation of the signal have been recently developed, which have been tested and compared with conventional linear filters. A nonlinear filter thus developed shows significant performance in terms of response time and measurement precision. The filter also presents the specificity of easy embedment into digital signal processor (DSP) electronics based on field-programmable gate arrays (FPGA) or microcontrollers, compatible with real-time requirements. © 2001 Elsevier Science. All rights reserved. - Highlights: • An efficient approach based on nonlinear filtering has been implemented. • The hypothesis test provides a local maximum likelihood estimation of the count rate. • The filter ensures an optimal compromise between precision and response time.
Directory of Open Access Journals (Sweden)
S. L. Heck
2012-02-01
Full Text Available There is a widely recognized need to improve our understanding of biosphere-atmosphere carbon exchanges in areas of complex terrain including the United States Mountain West. CO2 fluxes over mountainous terrain are often difficult to measure due to unusual and complicated influences associated with atmospheric transport. Consequently, deriving regional fluxes in mountain regions with carbon cycle inversion of atmospheric CO2 mole fraction is sensitive to filtering of observations to those that can be represented at the transport model resolution. Using five years of CO2 mole fraction observations from the Regional Atmospheric Continuous CO2 Network in the Rocky Mountains (Rocky RACCOON, five statistical filters are used to investigate a range of approaches for identifying regionally representative CO2 mole fractions. Test results from three filters indicate that subsets based on short-term variance and local CO2 gradients across tower inlet heights retain nine-tenths of the total observations and are able to define representative diel variability and seasonal cycles even for difficult-to-model sites where the influence of local fluxes is much larger than regional mole fraction variations. Test results from two other filters that consider measurements from previous and following days using spline fitting or sliding windows are overly selective. Case study examples showed that these windowing-filters rejected measurements representing synoptic changes in CO2, which suggests that they are not well suited to filtering continental CO2 measurements. We present a novel CO2 lapse rate filter that uses CO2 differences between levels in the model atmosphere to select subsets of site measurements that are representative on model scales. Our new filtering techniques provide guidance for novel approaches to assimilating mountain-top CO2 mole fractions in carbon cycle inverse models.
International Nuclear Information System (INIS)
Paul, Sabyasachi; Sarkar, P.K.
2012-05-01
The characterization of radionuclide in the in-vivo monitoring analysis using gamma spectrometry poses difficulty due to very low activity level in biological systems. The large statistical fluctuations often make 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 while analyzing noisy spectrometric data. This sequential, iterative filtering method uses the wavelet multi-resolution approach for the noise rejection and inverse transform after soft thresholding over the generated coefficients. Analyses of in-vivo monitoring data of 235 U and 238 U have been carried out using this method without disturbing the peak position and amplitude while achieving a threefold improvement in the signal to noise ratio, compared to the original measured spectrum. When compared with other data filtering techniques, the wavelet based method shows better results. (author)
Image statistics and nonlinear artifacts in composed transmission x-ray tomography
International Nuclear Information System (INIS)
Duerinckx, A.J.G.
1979-01-01
Knowledge of the image quality and image statistics in Computed Tomography (CT) images obtained with transmission x-ray CT scanners can increase the amount of clinically useful information that can be retrieved. Artifacts caused by nonlinear shadows are strongly object-dependent and are visible over larger areas of the image. No simple technique exists for their complete elimination. One source of artifacts in the first order statistics is the nonlinearities in the measured shadow or projection data used to reconstruct the image. One of the leading causes is the polychromaticity of the x-ray beam used in transmission CT scanners. Ways to improve the resulting image quality and techniques to extract additional information using dual energy scanning are discussed. A unique formalism consisting of a vector representation of the material dependence of the photon-tissue interactions is generalized to allow an in depth analysis. Poly-correction algorithms are compared using this analytic approach. Both quantum and detector electronic noise decrease the quality or information content of first order statistics. Preliminary results are presented using an heuristic adaptive nonlinear noise filter system for projection data. This filter system can be improved and/or modified to remove artifacts in both first and second order image statistics. Artifacts in the second order image statistics arise from the contribution of quantum noise. This can be described with a nonlinear detection equivalent model, similar to the model used to study artifacts in first order statistics. When analyzing these artifacts in second order statistics, one can divide them into linear artifacts, which do not present any problem of interpretation, and nonlinear artifacts, referred to as noise artifacts. A study of noise artifacts is presented together with a discussion of their relative importance in diagnostic radiology
Kovačević, Branko; Milosavljević, Milan
2013-01-01
“Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms. The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...
Speech enhancement via Mel-scale Wiener filtering with a frequency-wise voice activity detector
International Nuclear Information System (INIS)
Kim, Han Jun; Kim, Hwa Soo; Cho, Young Man
2007-01-01
This paper presents a speech enhancement system that enables a comfortable communication inside an automobile. A couple of novel concepts are proposed in an effort to improve two major building blocks in the existing speech enhancement systems: a voice activity detector (VAD) and a noise filtering algorithm. The proposed VAD classifies a given data frame as speech or noise at each frequency, enabling the frequency-wise updates of noise statistics and thereby improving the effectiveness of the noise filtering algorithms by providing more up-to-date noise statistics. The celebrated Wiener filter is adopted in this paper as the accompanying noise filtering algorithm, which results in significant noise suppression. Yet, the musical noise present in most Wiener filter-based systems prompts the idea of applying the Wiener filter in the Mel-scale in which the human auditory system responds to the external stimulation. It turns out that the Mel-scale Wiener filter creates some masking effects and thereby reduces musical noise significantly, leading to smooth transition between data frames
Costa, Marco; A. Manuela Gonçalves
2012-01-01
In this work are discussed some statistical approaches that combine multivariate statistical techniques and time series analysis in order to describe and model spatial patterns and temporal evolution by observing hydrological series of water quality variables recorded in time and space. These approaches are illustrated with a data set collected in the River Ave hydrological basin located in the Northwest region of Portugal.
Statistical distance and the approach to KNO scaling
International Nuclear Information System (INIS)
Diosi, L.; Hegyi, S.; Krasznovszky, S.
1990-05-01
A new method is proposed for characterizing the approach to KNO scaling. The essence of our method lies in the concept of statistical distance between nearby KNO distributions which reflects their distinguishability in spite of multiplicity fluctuations. It is shown that the geometry induced by the distance function defines a natural metric on the parameter space of a certain family of KNO distributions. Some examples are given in which the energy dependences of distinguishability of neighbouring KNO distributions are compared in nondiffractive hadron-hadron collisions and electron-positron annihilation. (author) 19 refs.; 4 figs
The Precautionary Principle and statistical approaches to uncertainty
DEFF Research Database (Denmark)
Keiding, Niels; Budtz-Jørgensen, Esben
2004-01-01
is unhelpful, because lack of significance can be due either to uninformative data or to genuine lack of effect (the Type II error problem). Its inversion, bioequivalence testing, might sometimes be a model for the Precautionary Principle in its ability to "prove the null hypothesis". Current procedures...... for setting safe exposure levels are essentially derived from these classical statistical ideas, and we outline how uncertainties in the exposure and response measurements affect the no observed adverse effect level, the Benchmark approach and the "Hockey Stick" model. A particular problem concerns model...
Adaptive Filtering Using Recurrent Neural Networks
Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.
2005-01-01
A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.
Passive target tracking using marginalized particle filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A marginalized particle filtering(MPF)approach is proposed for target tracking under the background of passive measurement.Essentially,the MPF is a combination of particle filtering technique and Kalman filter.By making full use of marginalization,the distributions of the tractable linear part of the total state variables are updated analytically using Kalman filter,and only the lower-dimensional nonlinear state variable needs to be dealt with using particle filter.Simulation studies are performed on an illustrative example,and the results show that the MPF method leads to a significant reduction of the tracking errors when compared with the direct particle implementation.Real data test results also validate the effectiveness of the presented method.
Statistical physics approach to earthquake occurrence and forecasting
Energy Technology Data Exchange (ETDEWEB)
Arcangelis, Lucilla de [Department of Industrial and Information Engineering, Second University of Naples, Aversa (CE) (Italy); Godano, Cataldo [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy); Grasso, Jean Robert [ISTerre, IRD-CNRS-OSUG, University of Grenoble, Saint Martin d’Héres (France); Lippiello, Eugenio, E-mail: eugenio.lippiello@unina2.it [Department of Mathematics and Physics, Second University of Naples, Caserta (Italy)
2016-04-25
There is striking evidence that the dynamics of the Earth crust is controlled by a wide variety of mutually dependent mechanisms acting at different spatial and temporal scales. The interplay of these mechanisms produces instabilities in the stress field, leading to abrupt energy releases, i.e., earthquakes. As a consequence, the evolution towards instability before a single event is very difficult to monitor. On the other hand, collective behavior in stress transfer and relaxation within the Earth crust leads to emergent properties described by stable phenomenological laws for a population of many earthquakes in size, time and space domains. This observation has stimulated a statistical mechanics approach to earthquake occurrence, applying ideas and methods as scaling laws, universality, fractal dimension, renormalization group, to characterize the physics of earthquakes. In this review we first present a description of the phenomenological laws of earthquake occurrence which represent the frame of reference for a variety of statistical mechanical models, ranging from the spring-block to more complex fault models. Next, we discuss the problem of seismic forecasting in the general framework of stochastic processes, where seismic occurrence can be described as a branching process implementing space–time-energy correlations between earthquakes. In this context we show how correlations originate from dynamical scaling relations between time and energy, able to account for universality and provide a unifying description for the phenomenological power laws. Then we discuss how branching models can be implemented to forecast the temporal evolution of the earthquake occurrence probability and allow to discriminate among different physical mechanisms responsible for earthquake triggering. In particular, the forecasting problem will be presented in a rigorous mathematical framework, discussing the relevance of the processes acting at different temporal scales for
Ran, J.; Ditmar, P.; Klees, R.; Farahani, H. H.
2018-03-01
We present an improved mascon approach to transform monthly spherical harmonic solutions based on GRACE satellite data into mass anomaly estimates in Greenland. The GRACE-based spherical harmonic coefficients are used to synthesize gravity anomalies at satellite altitude, which are then inverted into mass anomalies per mascon. The limited spectral content of the gravity anomalies is properly accounted for by applying a low-pass filter as part of the inversion procedure to make the functional model spectrally consistent with the data. The full error covariance matrices of the monthly GRACE solutions are properly propagated using the law of covariance propagation. Using numerical experiments, we demonstrate the importance of a proper data weighting and of the spectral consistency between functional model and data. The developed methodology is applied to process real GRACE level-2 data (CSR RL05). The obtained mass anomaly estimates are integrated over five drainage systems, as well as over entire Greenland. We find that the statistically optimal data weighting reduces random noise by 35-69%, depending on the drainage system. The obtained mass anomaly time-series are de-trended to eliminate the contribution of ice discharge and are compared with de-trended surface mass balance (SMB) time-series computed with the Regional Atmospheric Climate Model (RACMO 2.3). We show that when using a statistically optimal data weighting in GRACE data processing, the discrepancies between GRACE-based estimates of SMB and modelled SMB are reduced by 24-47%.
Neural network training by Kalman filtering in process system monitoring
International Nuclear Information System (INIS)
Ciftcioglu, Oe.
1996-03-01
Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)
The large break LOCA evaluation method with the simplified statistic approach
International Nuclear Information System (INIS)
Kamata, Shinya; Kubo, Kazuo
2004-01-01
USNRC published the Code Scaling, Applicability and Uncertainty (CSAU) evaluation methodology to large break LOCA which supported the revised rule for Emergency Core Cooling System performance in 1989. In USNRC regulatory guide 1.157, it is required that the peak cladding temperature (PCT) cannot exceed 2200deg F with high probability 95th percentile. In recent years, overseas countries have developed statistical methodology and best estimate code with the model which can provide more realistic simulation for the phenomena based on the CSAU evaluation methodology. In order to calculate PCT probability distribution by Monte Carlo trials, there are approaches such as the response surface technique using polynomials, the order statistics method, etc. For the purpose of performing rational statistic analysis, Mitsubishi Heavy Industries, LTD (MHI) tried to develop the statistic LOCA method using the best estimate LOCA code MCOBRA/TRAC and the simplified code HOTSPOT. HOTSPOT is a Monte Carlo heat conduction solver to evaluate the uncertainties of the significant fuel parameters at the PCT positions of the hot rod. The direct uncertainty sensitivity studies can be performed without the response surface because the Monte Carlo simulation for key parameters can be performed in short time using HOTSPOT. With regard to the parameter uncertainties, MHI established the treatment that the bounding conditions are given for LOCA boundary and plant initial conditions, the Monte Carlo simulation using HOTSPOT is applied to the significant fuel parameters. The paper describes the large break LOCA evaluation method with the simplified statistic approach and the results of the application of the method to the representative four-loop nuclear power plant. (author)
Fujita, Hideaki; Akagi, Hirofumi
1991-01-01
The authors present a combined system with a passive filter and a small-rated active filter, both connected in series with each other. The passive filter removes load produced harmonics just as a conventional filter does. The active filter plays a role in improving the filtering characteristics of the passive filter. This results in a great reduction of the required rating of the active filter and in eliminating all the limitations faced by using only the passive filter, leading to a practica...
A Simple Ripple Filter for FLUKA
DEFF Research Database (Denmark)
Bassler, Niels; Herrmann, Rochus
In heavy ion radiotherapy, pristine C-12 beams are usually widened a few mm (FWHM) along the beam axis, before the actual spread out Bragg peak (SOBP) is build. The pristine beam widening is commonly performed with a ripple filter, known from the facility at GSI (Darmstadt) and at HIT (Heidelberg......). The ripple filter at GSI and HIT consists of several wedge like structures, which widens the Bragg-peak up to e.g. 3 mm. For Monte Carlo simulations of C-12 therapy, the exact setup, including the ripple filter needs to be simulated. In the Monte Carlo particle transport program FLUKA, the ripple filter can....... Since the ripple filter is a periodic geometry, one could use the LATTIC card with advantage, but here we shall take a Monte Carlo based approach istead. The advantage of this method is that our input file merely contains one body as the ripple filter, which can be a flat slab (or any other arbitrary...
A Hybrid Approach using Collaborative filtering and Content based Filtering for Recommender System
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.
Fault detection and identification in missile system guidance and control: a filtering approach
Padgett, Mary Lou; Evers, Johnny; Karplus, Walter J.
1996-03-01
Real-world applications of computational intelligence can enhance the fault detection and identification capabilities of a missile guidance and control system. A simulation of a bank-to- turn missile demonstrates that actuator failure may cause the missile to roll and miss the target. Failure of one fin actuator can be detected using a filter and depicting the filter output as fuzzy numbers. The properties and limitations of artificial neural networks fed by these fuzzy numbers are explored. A suite of networks is constructed to (1) detect a fault and (2) determine which fin (if any) failed. Both the zero order moment term and the fin rate term show changes during actuator failure. Simulations address the following questions: (1) How bad does the actuator failure have to be for detection to occur, (2) How bad does the actuator failure have to be for fault detection and isolation to occur, (3) are both zero order moment and fine rate terms needed. A suite of target trajectories are simulated, and properties and limitations of the approach reported. In some cases, detection of the failed actuator occurs within 0.1 second, and isolation of the failure occurs 0.1 after that. Suggestions for further research are offered.
Eulerian Time-Domain Filtering for Spatial LES
Pruett, C. David
1997-01-01
Eulerian time-domain filtering seems to be appropriate for LES (large eddy simulation) of flows whose large coherent structures convect approximately at a common characteristic velocity; e.g., mixing layers, jets, and wakes. For these flows, we develop an approach to LES based on an explicit second-order digital Butterworth filter, which is applied in,the time domain in an Eulerian context. The approach is validated through a priori and a posteriori analyses of the simulated flow of a heated, subsonic, axisymmetric jet.
Active Multi-Field Learning for Spam Filtering
Wuying Liu; Lin Wang; Mianzhu Yi; Nan Xie
2015-01-01
Ubiquitous spam messages cause a serious waste of time and resources. This paper addresses the practical spam filtering problem, and proposes a universal approach to fight with various spam messages. The proposed active multi-field learning approach is based on: 1) It is cost-sensitive to obtain a label for a real-world spam filter, which suggests an active learning idea; and 2) Different messages often have a similar multi-field text structure, which suggests a multi-field learning idea. The...
Simulation for noise cancellation using LMS adaptive filter
Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung
2017-06-01
In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.
Burggraeve, A; Van den Kerkhof, T; Hellings, M; Remon, J P; Vervaet, C; De Beer, T
2011-04-18
Fluid bed granulation is a batch process, which is characterized by the processing of raw materials for a predefined period of time, consisting of a fixed spraying phase and a subsequent drying period. The present study shows the multivariate statistical modeling and control of a fluid bed granulation process based on in-line particle size distribution (PSD) measurements (using spatial filter velocimetry) combined with continuous product temperature registration using a partial least squares (PLS) approach. Via the continuous in-line monitoring of the PSD and product temperature during granulation of various reference batches, a statistical batch model was developed allowing the real-time evaluation and acceptance or rejection of future batches. Continuously monitored PSD and product temperature process data of 10 reference batches (X-data) were used to develop a reference batch PLS model, regressing the X-data versus the batch process time (Y-data). Two PLS components captured 98.8% of the variation in the X-data block. Score control charts in which the average batch trajectory and upper and lower control limits are displayed were developed. Next, these control charts were used to monitor 4 new test batches in real-time and to immediately detect any deviations from the expected batch trajectory. By real-time evaluation of new batches using the developed control charts and by computation of contribution plots of deviating process behavior at a certain time point, batch losses or reprocessing can be prevented. Immediately after batch completion, all PSD and product temperature information (i.e., a batch progress fingerprint) was used to estimate some granule properties (density and flowability) at an early stage, which can improve batch release time. Individual PLS models relating the computed scores (X) of the reference PLS model (based on the 10 reference batches) and the density, respectively, flowabililty as Y-matrix, were developed. The scores of the 4 test
Unsupervised Retinal Vessel Segmentation Using Combined Filters.
Directory of Open Access Journals (Sweden)
Wendeson S Oliveira
Full Text Available Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
DEFF Research Database (Denmark)
Lu, Xiaonan; Sun, Kai; Huang, Lipei
2014-01-01
around the switching frequency and its multiples. Although the LCL-filters have several advantages compared to single inductance filter, its resonance problem should be noticed. Conventionally, the resonance analysis is mainly focused on the single inverter system, whereas in a renewable energy system...... to the conventional active damping approaches, the biquad filter based active damping method does not require additional sensors and control loops. Meanwhile, the multiple instable closed-loop poles of the parallel inverter system can be moved to the stable region simultaneously. Real-time simulations based on d...
A statistical approach to evaluate hydrocarbon remediation in the unsaturated zone
International Nuclear Information System (INIS)
Hajali, P.; Marshall, T.; Overman, S.
1991-01-01
This paper presents an evaluation of performance and cleanup effectiveness of a vapor extraction system (VES) in extracting chlorinated hydrocarbons and petroleum-based hydrocarbons (mineral spirits) from the unsaturated zone. The statistical analysis of soil concentration data to evaluate the VES remediation success is described. The site is a former electronics refurbishing facility in southern California; soil contamination from organic solvents was found mainly in five areas (Area A through E) beneath two buildings. The evaluation begins with a brief description of the site background, discusses the statistical approach, and presents conclusions
Dreano, Denis
2015-04-27
A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
Dreano, Denis; Mallick, Bani; Hoteit, Ibrahim
2015-01-01
A statistical model is proposed to filter satellite-derived chlorophyll concentration from the Red Sea, and to predict future chlorophyll concentrations. The seasonal trend is first estimated after filling missing chlorophyll data using an Empirical Orthogonal Function (EOF)-based algorithm (Data Interpolation EOF). The anomalies are then modeled as a stationary Gaussian process. A method proposed by Gneiting (2002) is used to construct positive-definite space-time covariance models for this process. After choosing an appropriate statistical model and identifying its parameters, Kriging is applied in the space-time domain to make a one step ahead prediction of the anomalies. The latter serves as the prediction model of a reduced-order Kalman filter, which is applied to assimilate and predict future chlorophyll concentrations. The proposed method decreases the root mean square (RMS) prediction error by about 11% compared with the seasonal average.
Feature-Based Nonlocal Polarimetric SAR Filtering
Directory of Open Access Journals (Sweden)
Xiaoli Xing
2017-10-01
Full Text Available Polarimetric synthetic aperture radar (PolSAR images are inherently contaminated by multiplicative speckle noise, which complicates the image interpretation and image analyses. To reduce the speckle effect, several adaptive speckle filters have been developed based on the weighted average of the similarity measures commonly depending on the model or probability distribution, which are often affected by the distribution parameters and modeling texture components. In this paper, a novel filtering method introduces the coefficient of variance ( CV and Pauli basis (PB to measure the similarity, and the two features are combined with the framework of the nonlocal mean filtering. The CV is used to describe the complexity of various scenes and distinguish the scene heterogeneity; moreover, the Pauli basis is able to express the polarimetric information in PolSAR image processing. This proposed filtering combines the CV and Pauli basis to improve the estimation accuracy of the similarity weights. Then, the similarity of the features is deduced according to the test statistic. Subsequently, the filtering is proceeded by using the nonlocal weighted estimation. The performance of the proposed filter is tested with the simulated images and real PolSAR images, which are acquired by AIRSAR system and ESAR system. The qualitative and quantitative experiments indicate the validity of the proposed method by comparing with the widely-used despeckling methods.
Haig, Sarah-Jane; Quince, Christopher; Davies, Robert L; Dorea, Caetano C; Collins, Gavin
2014-09-15
Previous laboratory-scale studies to characterise the functional microbial ecology of slow sand filters have suffered from methodological limitations that could compromise their relevance to full-scale systems. Therefore, to ascertain if laboratory-scale slow sand filters (L-SSFs) can replicate the microbial community and water quality production of industrially operated full-scale slow sand filters (I-SSFs), eight cylindrical L-SSFs were constructed and were used to treat water from the same source as the I-SSFs. Half of the L-SSFs sand beds were composed of sterilized sand (sterile) from the industrial filters and the other half with sand taken directly from the same industrial filter (non-sterile). All filters were operated for 10 weeks, with the microbial community and water quality parameters sampled and analysed weekly. To characterize the microbial community phyla-specific qPCR assays and 454 pyrosequencing of the 16S rRNA gene were used in conjunction with an array of statistical techniques. The results demonstrate that it is possible to mimic both the water quality production and the structure of the microbial community of full-scale filters in the laboratory - at all levels of taxonomic classification except OTU - thus allowing comparison of LSSF experiments with full-scale units. Further, it was found that the sand type composing the filter bed (non-sterile or sterile), the water quality produced, the age of the filters and the depth of sand samples were all significant factors in explaining observed differences in the structure of the microbial consortia. This study is the first to the authors' knowledge that demonstrates that scaled-down slow sand filters can accurately reproduce the water quality and microbial consortia of full-scale slow sand filters. Copyright © 2014 Elsevier Ltd. All rights reserved.
Total laparoscopic retrieval of inferior vena cava filter.
Benrashid, Ehsan; Adkar, Shaunak Sanjay; Bennett, Kyla Megan; Zani, Sabino; Cox, Mitchell Wayne
2015-01-01
While there is some local variability in the use of inferior vena cava filters and there has been some evolution in the indications for filter placement over time, inferior vena cava filters remain a standard option for pulmonary embolism prophylaxis. Indications are clear in certain subpopulations of patients, particularly those with deep venous thrombosis and absolute contraindications to anticoagulation. There are, however, a variety of reported inferior vena cava filter complications in the short and long term, making retrieval of the filter desirable in most cases. Here, we present the case of a morbidly obese patient complaining of chronic abdominal pain after inferior vena cava filter placement and malposition of the filter with extensive protrusion outside the inferior vena cava. She underwent successful laparoscopic retrieval of her malpositioned inferior vena cava filters after failure of a conventional endovascular approach.
Total laparoscopic retrieval of inferior vena cava filter
Directory of Open Access Journals (Sweden)
Ehsan Benrashid
2015-08-01
Full Text Available While there is some local variability in the use of inferior vena cava filters and there has been some evolution in the indications for filter placement over time, inferior vena cava filters remain a standard option for pulmonary embolism prophylaxis. Indications are clear in certain subpopulations of patients, particularly those with deep venous thrombosis and absolute contraindications to anticoagulation. There are, however, a variety of reported inferior vena cava filter complications in the short and long term, making retrieval of the filter desirable in most cases. Here, we present the case of a morbidly obese patient complaining of chronic abdominal pain after inferior vena cava filter placement and malposition of the filter with extensive protrusion outside the inferior vena cava. She underwent successful laparoscopic retrieval of her malpositioned inferior vena cava filters after failure of a conventional endovascular approach.
Lisano, Michael E.
2007-01-01
Recent literature in applied estimation theory reflects growing interest in the sigma-point (also called unscented ) formulation for optimal sequential state estimation, often describing performance comparisons with extended Kalman filters as applied to specific dynamical problems [c.f. 1, 2, 3]. Favorable attributes of sigma-point filters are described as including a lower expected error for nonlinear even non-differentiable dynamical systems, and a straightforward formulation not requiring derivation or implementation of any partial derivative Jacobian matrices. These attributes are particularly attractive, e.g. in terms of enabling simplified code architecture and streamlined testing, in the formulation of estimators for nonlinear spaceflight mechanics systems, such as filter software onboard deep-space robotic spacecraft. As presented in [4], the Sigma-Point Consider Filter (SPCF) algorithm extends the sigma-point filter algorithm to the problem of consider covariance analysis. Considering parameters in a dynamical system, while estimating its state, provides an upper bound on the estimated state covariance, which is viewed as a conservative approach to designing estimators for problems of general guidance, navigation and control. This is because, whether a parameter in the system model is observable or not, error in the knowledge of the value of a non-estimated parameter will increase the actual uncertainty of the estimated state of the system beyond the level formally indicated by the covariance of an estimator that neglects errors or uncertainty in that parameter. The equations for SPCF covariance evolution are obtained in a fashion similar to the derivation approach taken with standard (i.e. linearized or extended) consider parameterized Kalman filters (c.f. [5]). While in [4] the SPCF and linear-theory consider filter (LTCF) were applied to an illustrative linear dynamics/linear measurement problem, in the present work examines the SPCF as applied to
Depth Images Filtering In Distributed Streaming
Directory of Open Access Journals (Sweden)
Dziubich Tomasz
2016-04-01
Full Text Available In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related requirements. We discuss the comparison of point cloud filters focusing on their usage for streaming optimization. For the filtering step of the stream pipeline processing we evaluate four filters: Voxel Grid, Radial Outliner Remover, Statistical Outlier Removal and Pass Through. For each of the filters we perform a series of tests for evaluating the impact on the point cloud size and transmitting frequency (analysed for various fps ratio. We present results of the optimization process used for point cloud consolidation in a distributed environment. We describe the processing of the point clouds before and after the transmission. Pre- and post-processing allow the user to send the cloud via network without any delays. The proposed pre-processing compression of the cloud and the post-processing reconstruction of it are focused on assuring that the end-user application obtains the cloud with a given precision.
DEFF Research Database (Denmark)
Zou, Zhixiang; Wang, Zheng; Cheng, Ming
2012-01-01
This paper presents an digital dual-mode-structure repetitive control approach for the single-phase shunt active power filter (APF), which aims to enhance the tracking ability and eliminate arbitrary order harmonic. The proposed repetitive control scheme blends the characteristics of both odd......-harmonic repetitive control and even-harmonic repetitive control. Moreover, the convergence rate is faster than conventional repetitive controller. Additionally, the parameters have been designed and optimized for the dual-mode structure repetitive control to improve the performance of APF system. Experimental...
Comparison of three nonlinear filters for fault detection in continuous glucose monitors.
Mahmoudi, Zeinab; Wendt, Sabrina Lyngbye; Boiroux, Dimitri; Hagdrup, Morten; Norgaard, Kirsten; Poulsen, Niels Kjolstad; Madsen, Henrik; Jorgensen, John Bagterp
2016-08-01
The purpose of this study is to compare the performance of three nonlinear filters in online drift detection of continuous glucose monitors. The nonlinear filters are the extended Kalman filter (EKF), the unscented Kalman filter (UKF), and the particle filter (PF). They are all based on a nonlinear model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest average detection delay of 84.1%, with the average detection sensitivity of 82.6%, and average specificity of 91.0%.
Predicting future protection of respirator users: Statistical approaches and practical implications.
Hu, Chengcheng; Harber, Philip; Su, Jing
2016-01-01
The purpose of this article is to describe a statistical approach for predicting a respirator user's fit factor in the future based upon results from initial tests. A statistical prediction model was developed based upon joint distribution of multiple fit factor measurements over time obtained from linear mixed effect models. The model accounts for within-subject correlation as well as short-term (within one day) and longer-term variability. As an example of applying this approach, model parameters were estimated from a research study in which volunteers were trained by three different modalities to use one of two types of respirators. They underwent two quantitative fit tests at the initial session and two on the same day approximately six months later. The fitted models demonstrated correlation and gave the estimated distribution of future fit test results conditional on past results for an individual worker. This approach can be applied to establishing a criterion value for passing an initial fit test to provide reasonable likelihood that a worker will be adequately protected in the future; and to optimizing the repeat fit factor test intervals individually for each user for cost-effective testing.
[Gohieria fusca found in dust of air-conditioner filters].
Qiang, Chai; Xiao-Dong, Zhan; Wei, Guo; Chao-Pin, Li
2017-09-25
To investigate the pollution status of Gohieria fusca in the air conditioner-filters of different places in Wuhu City. The dust samples were collected from the filters of air-conditioners in dining rooms, shopping malls, hotels and households between June and September, 2013, and G. fusca was detected in the dust samples. There were 430 dust samples collected and 98 were G. fusca positive with the breeding rate of 22.79%. The difference of breeding rates of G. fusca were statistically significant among the different places ( χ 2 =18.294, P air-conditioner filters in Wuhu City gravely.
Rigatos, Gerasimos G
2015-01-01
This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The bo...
Restricted Kalman Filtering Theory, Methods, and Application
Pizzinga, Adrian
2012-01-01
In statistics, the Kalman filter is a mathematical method whose purpose is to use a series of measurements observed over time, containing random variations and other inaccuracies, and produce estimates that tend to be closer to the true unknown values than those that would be based on a single measurement alone. This Brief offers developments on Kalman filtering subject to general linear constraints. There are essentially three types of contributions: new proofs for results already established; new results within the subject; and applications in investment analysis and macroeconomics, where th
Control and filtering for semi-Markovian jump systems
Li, Fanbiao; Wu, Ligang
2017-01-01
This book presents up-to-date research developments and novel methodologies on semi-Markovian jump systems (S-MJS). It presents solutions to a series of problems with new approaches for the control and filtering of S-MJS, including stability analysis, sliding mode control, dynamic output feedback control, robust filter design, and fault detection. A set of newly developed techniques such as piecewise analysis method, positively invariant set approach, event-triggered method, and cone complementary linearization approaches are presented. Control and Filtering for Semi-Markovian Jump Systems is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.
Correction of Bowtie-Filter Normalization and Crescent Artifacts for a Clinical CBCT System.
Zhang, Hong; Kong, Vic; Huang, Ke; Jin, Jian-Yue
2017-02-01
To present our experiences in understanding and minimizing bowtie-filter crescent artifacts and bowtie-filter normalization artifacts in a clinical cone beam computed tomography system. Bowtie-filter position and profile variations during gantry rotation were studied. Two previously proposed strategies (A and B) were applied to the clinical cone beam computed tomography system to correct bowtie-filter crescent artifacts. Physical calibration and analytical approaches were used to minimize the norm phantom misalignment and to correct for bowtie-filter normalization artifacts. A combined procedure to reduce bowtie-filter crescent artifacts and bowtie-filter normalization artifacts was proposed and tested on a norm phantom, CatPhan, and a patient and evaluated using standard deviation of Hounsfield unit along a sampling line. The bowtie-filter exhibited not only a translational shift but also an amplitude variation in its projection profile during gantry rotation. Strategy B was better than strategy A slightly in minimizing bowtie-filter crescent artifacts, possibly because it corrected the amplitude variation, suggesting that the amplitude variation plays a role in bowtie-filter crescent artifacts. The physical calibration largely reduced the misalignment-induced bowtie-filter normalization artifacts, and the analytical approach further reduced bowtie-filter normalization artifacts. The combined procedure minimized both bowtie-filter crescent artifacts and bowtie-filter normalization artifacts, with Hounsfield unit standard deviation being 63.2, 45.0, 35.0, and 18.8 Hounsfield unit for the best correction approaches of none, bowtie-filter crescent artifacts, bowtie-filter normalization artifacts, and bowtie-filter normalization artifacts + bowtie-filter crescent artifacts, respectively. The combined procedure also demonstrated reduction of bowtie-filter crescent artifacts and bowtie-filter normalization artifacts in a CatPhan and a patient. We have developed a step
PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.
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.
Optimal design of active EMC filters
Chand, B.; Kut, T.; Dickmann, S.
2013-07-01
A recent trend in automotive industry is adding electrical drive systems to conventional drives. The electrification allows an expansion of energy sources and provides great opportunities for environmental friendly mobility. The electrical powertrain and its components can also cause disturbances which couple into nearby electronic control units and communication cables. Therefore the communication can be degraded or even permanently disrupted. To minimize these interferences, different approaches are possible. One possibility is to use EMC filters. However, the diversity of filters is very large and the determination of an appropriate filter for each application is time-consuming. Therefore, the filter design is determined by using a simulation tool including an effective optimization algorithm. This method leads to improvements in terms of weight, volume and cost.
A DoS/DDoS Attack Detection System Using Chi-Square Statistic Approach
Directory of Open Access Journals (Sweden)
Fang-Yie Leu
2010-04-01
Full Text Available Nowadays, users can easily access and download network attack tools, which often provide friendly interfaces and easily operated features, from the Internet. Therefore, even a naive hacker can also launch a large scale DoS or DDoS attack to prevent a system, i.e., the victim, from providing Internet services. In this paper, we propose an agent based intrusion detection architecture, which is a distributed detection system, to detect DoS/DDoS attacks by invoking a statistic approach that compares source IP addresses' normal and current packet statistics to discriminate whether there is a DoS/DDoS attack. It first collects all resource IPs' packet statistics so as to create their normal packet distribution. Once some IPs' current packet distribution suddenly changes, very often it is an attack. Experimental results show that this approach can effectively detect DoS/DDoS attacks.
A brief overview of speech enhancement with linear filtering
DEFF Research Database (Denmark)
Benesty, Jacob; Christensen, Mads Græsbøll; Jensen, Jesper Rindom
2014-01-01
In this paper, we provide an overview of some recently introduced principles and ideas for speech enhancement with linear filtering and explore how these are related and how they can be used in various applications. This is done in a general framework where the speech enhancement problem is stated......-to-noise ratio (SNR), and Wiener filters are derived from the conventional speech enhancement approach and the recently introduced orthogonal decomposition approach. For each of the filters, we derive their properties in terms of output SNR and speech distortion. We then demonstrate how the ideas can be applied...
Statistical Approaches for Spatiotemporal Prediction of Low Flows
Fangmann, A.; Haberlandt, U.
2017-12-01
An adequate assessment of regional climate change impacts on streamflow requires the integration of various sources of information and modeling approaches. This study proposes simple statistical tools for inclusion into model ensembles, which are fast and straightforward in their application, yet able to yield accurate streamflow predictions in time and space. Target variables for all approaches are annual low flow indices derived from a data set of 51 records of average daily discharge for northwestern Germany. The models require input of climatic data in the form of meteorological drought indices, derived from observed daily climatic variables, averaged over the streamflow gauges' catchments areas. Four different modeling approaches are analyzed. Basis for all pose multiple linear regression models that estimate low flows as a function of a set of meteorological indices and/or physiographic and climatic catchment descriptors. For the first method, individual regression models are fitted at each station, predicting annual low flow values from a set of annual meteorological indices, which are subsequently regionalized using a set of catchment characteristics. The second method combines temporal and spatial prediction within a single panel data regression model, allowing estimation of annual low flow values from input of both annual meteorological indices and catchment descriptors. The third and fourth methods represent non-stationary low flow frequency analyses and require fitting of regional distribution functions. Method three is subject to a spatiotemporal prediction of an index value, method four to estimation of L-moments that adapt the regional frequency distribution to the at-site conditions. The results show that method two outperforms successive prediction in time and space. Method three also shows a high performance in the near future period, but since it relies on a stationary distribution, its application for prediction of far future changes may be
DEFF Research Database (Denmark)
Lindström, Erik; Madsen, Henrik; Nielsen, Jan Nygaard
Statistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics...... that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical...... and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Itō’s formula, the Black–Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives...
Ensemble Kalman filtering with one-step-ahead smoothing
Raboudi, Naila F.; Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2018-01-01
error statistics. This limits their representativeness of the background error covariances and, thus, their performance. This work explores the efficiency of the one-step-ahead (OSA) smoothing formulation of the Bayesian filtering problem to enhance
Time-area efficient multiplier-free filter architectures for FPGA implementation
DEFF Research Database (Denmark)
Shajaan, Mohammad; Nielsen, Karsten; Sørensen, John Aasted
1995-01-01
Simultaneous design of multiplier-free filters and their hardware implementation in Xilinx field programmable gate array (XC4000) is presented. The filter synthesis method is a new approach based on cascade coupling of low order sections. The complexity of the design algorithm is 𝒪 (filter o...
Search Databases and Statistics
DEFF Research Database (Denmark)
Refsgaard, Jan C; Munk, Stephanie; Jensen, Lars J
2016-01-01
having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database...... searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here....
An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter
Song, Hajoon
2010-07-01
A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.
Lim, Hansaim; Gray, Paul; Xie, Lei; Poleksic, Aleksandar
2016-12-13
Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.
Directory of Open Access Journals (Sweden)
Lili Lei
2012-05-01
Full Text Available A hybrid data assimilation approach combining nudging and the ensemble Kalman filter (EnKF for dynamic analysis and numerical weather prediction is explored here using the non-linear Lorenz three-variable model system with the goal of a smooth, continuous and accurate data assimilation. The hybrid nudging-EnKF (HNEnKF computes the hybrid nudging coefficients from the flow-dependent, time-varying error covariance matrix from the EnKF's ensemble forecasts. It extends the standard diagonal nudging terms to additional off-diagonal statistical correlation terms for greater inter-variable influence of the innovations in the model's predictive equations to assist in the data assimilation process. The HNEnKF promotes a better fit of an analysis to data compared to that achieved by either nudging or incremental analysis update (IAU. When model error is introduced, it produces similar or better root mean square errors compared to the EnKF while minimising the error spikes/discontinuities created by the intermittent EnKF. It provides a continuous data assimilation with better inter-variable consistency and improved temporal smoothness than that of the EnKF. Data assimilation experiments are also compared to the ensemble Kalman smoother (EnKS. The HNEnKF has similar or better temporal smoothness than that of the EnKS, and with much smaller central processing unit (CPU time and data storage requirements.
Identifying city PV roof resource based on Gabor filter
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.
A Distributional Representation Model For Collaborative Filtering
Junlin, Zhang; Heng, Cai; Tongwen, Huang; Huiping, Xue
2015-01-01
In this paper, we propose a very concise deep learning approach for collaborative filtering that jointly models distributional representation for users and items. The proposed framework obtains better performance when compared against current state-of-art algorithms and that made the distributional representation model a promising direction for further research in the collaborative filtering.
Filtering and control of wireless networked systems
Zhang, Dan; Yu, Li
2017-01-01
This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems.
A New Approach to Monte Carlo Simulations in Statistical Physics
Landau, David P.
2002-08-01
Monte Carlo simulations [1] have become a powerful tool for the study of diverse problems in statistical/condensed matter physics. Standard methods sample the probability distribution for the states of the system, most often in the canonical ensemble, and over the past several decades enormous improvements have been made in performance. Nonetheless, difficulties arise near phase transitions-due to critical slowing down near 2nd order transitions and to metastability near 1st order transitions, and these complications limit the applicability of the method. We shall describe a new Monte Carlo approach [2] that uses a random walk in energy space to determine the density of states directly. Once the density of states is known, all thermodynamic properties can be calculated. This approach can be extended to multi-dimensional parameter spaces and should be effective for systems with complex energy landscapes, e.g., spin glasses, protein folding models, etc. Generalizations should produce a broadly applicable optimization tool. 1. A Guide to Monte Carlo Simulations in Statistical Physics, D. P. Landau and K. Binder (Cambridge U. Press, Cambridge, 2000). 2. Fugao Wang and D. P. Landau, Phys. Rev. Lett. 86, 2050 (2001); Phys. Rev. E64, 056101-1 (2001).
Energy Technology Data Exchange (ETDEWEB)
Sikora, Andrzej, E-mail: sikora@iel.wroc.pl [Electrotechnical Institute, Division of Electrotechnology and Materials Science, M. Skłodowskiej-Curie 55/61, 50-369 Wrocław (Poland); Rodak, Aleksander [Faculty of Electronics, Wrocław University of Technology, Janiszewskiego 11/17, 50-372 Wrocław (Poland); Unold, Olgierd [Institute of Computer Engineering, Control and Robotics, Faculty of Electronics, Wrocław University of Technology, Janiszewskiego 11/17, 50-372 Wrocław (Poland); Klapetek, Petr [Czech Metrology Institute, Okružní 31, 638 00 Brno (Czech Republic)
2016-12-15
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. - Highlights: • A novel approach to 2D wavelet-based filter for atomic force microscopy is shown. • The additional AFM measurement signal is used to adjust the filter. • Efficient removal of the local interference phenomena caused artifacts is presented.
International Nuclear Information System (INIS)
Sikora, Andrzej; Rodak, Aleksander; Unold, Olgierd; Klapetek, Petr
2016-01-01
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. - Highlights: • A novel approach to 2D wavelet-based filter for atomic force microscopy is shown. • The additional AFM measurement signal is used to adjust the filter. • Efficient removal of the local interference phenomena caused artifacts is presented.
Crucifix, Michel; Wilkinson, Richard; Carson, Jake; Preston, Simon; Alemeida, Carlos; Rougier, Jonathan
2013-04-01
The existence of an action of astronomical forcing on the Pleistocene climate is almost undisputed. However, quantifying this action is not straightforward. In particular, the phenomenon of deglaciation is generally interpreted as a manifestation of instability, which is typical of non-linear systems. As a consequence, explaining the Pleistocene climate record as the addition of an astronomical contribution and noise-as often done using harmonic analysis tools-is potentially deceptive. Rather, we advocate a methodology in which non-linear stochastic dynamical systems are calibrated on the Pleistocene climate record. The exercise, though, requires careful statistical reasoning and state-of-the-art techniques. In fact, the problem has been judged to be mathematically 'intractable and unsolved' and some pragmatism is justified. In order to illustrate the methodology we consider one dynamical system that potentially captures four dynamical features of the Pleistocene climate : the existence of a saddle-node bifurcation in at least one of its slow components, a time-scale separation between a slow and a fast component, the action of astronomical forcing, and the existence a stochastic contribution to the system dynamics. This model is obviously not the only possible representation of Pleistocene dynamics, but it encapsulates well enough both our theoretical and empirical knowledge into a very simple form to constitute a valid starting point. The purpose of this poster is to outline the practical challenges in calibrating such a model on paleoclimate observations. Just as in time series analysis, there is no one single and universal test or criteria that would demonstrate the validity of an approach. Several methods exist to calibrate the model and judgement develops by the confrontation of the results of the different methods. In particular, we consider here the Kalman filter variants, the Particle Monte-Carlo Markov Chain, and two other variants of Sequential Monte
From inverse problems to learning: a Statistical Mechanics approach
Baldassi, Carlo; Gerace, Federica; Saglietti, Luca; Zecchina, Riccardo
2018-01-01
We present a brief introduction to the statistical mechanics approaches for the study of inverse problems in data science. We then provide concrete new results on inferring couplings from sampled configurations in systems characterized by an extensive number of stable attractors in the low temperature regime. We also show how these result are connected to the problem of learning with realistic weak signals in computational neuroscience. Our techniques and algorithms rely on advanced mean-field methods developed in the context of disordered systems.
Efficient Kernel-Based Ensemble Gaussian Mixture Filtering
Liu, Bo
2015-11-11
We consider the Bayesian filtering problem for data assimilation following the kernel-based ensemble Gaussian-mixture filtering (EnGMF) approach introduced by Anderson and Anderson (1999). In this approach, the posterior distribution of the system state is propagated with the model using the ensemble Monte Carlo method, providing a forecast ensemble that is then used to construct a prior Gaussian-mixture (GM) based on the kernel density estimator. This results in two update steps: a Kalman filter (KF)-like update of the ensemble members and a particle filter (PF)-like update of the weights, followed by a resampling step to start a new forecast cycle. After formulating EnGMF for any observational operator, we analyze the influence of the bandwidth parameter of the kernel function on the covariance of the posterior distribution. We then focus on two aspects: i) the efficient implementation of EnGMF with (relatively) small ensembles, where we propose a new deterministic resampling strategy preserving the first two moments of the posterior GM to limit the sampling error; and ii) the analysis of the effect of the bandwidth parameter on contributions of KF and PF updates and on the weights variance. Numerical results using the Lorenz-96 model are presented to assess the behavior of EnGMF with deterministic resampling, study its sensitivity to different parameters and settings, and evaluate its performance against ensemble KFs. The proposed EnGMF approach with deterministic resampling suggests improved estimates in all tested scenarios, and is shown to require less localization and to be less sensitive to the choice of filtering parameters.
Stochastic processes and filtering theory
Jazwinski, Andrew H
1970-01-01
This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well.Taking the state-space approach to filtering, this text models dynamical systems by finite-dimensional Markov processes, outputs of stochastic difference, and differential equations. Starting with background material on probab
Retrievable Inferior vena cava filters in pregnancy: Risk versus benefit?
Crosby, David A; Ryan, Kevin; McEniff, Niall; Dicker, Patrick; Regan, Carmen; Lynch, Caoimhe; Byrne, Bridgette
2018-03-01
Venous thromboembolism remains one of the leading causes of maternal mortality in the developed world. Retrievable inferior vena cava (IVC) filters have a role in the prevention of lethal pulmonary emboli when anticoagulation is contraindicated or has failed [1]. It is unclear whether or not the physiological changes in pregnancy influence efficacy and complications of these devices. The decision to place an IVC filter in pregnancy is complex and there is limited information in terms of benefit and risk to the mother. The objective of this study was to determine the efficacy and safety of these devices in pregnancy and to compare these with rates reported in the general population. The aim of this study was report three recent cases of retrievable IVC filter use in pregnant women in our department and to perform a systematic review of the literature to identify published cases of filters in pregnancy. The efficacy and complication rates of these devices in pregnancy were estimated and compared to rates reported in the general population in a recent review [2]. Fisher's exact test was used for statistical analysis. In addition to our three cases, 16 publications were identified with retrievable IVC filter use in 40 pregnant women resulting in a total of 43 cases. There was no pulmonary embolus in the pregnant group (0/43) compared to 57/6291 (0.9%) in the general population. Thrombosis of the filter (2.3% vs. 0.9%, p = 0.33) and perforation of the IVC (7.0% vs 4.4%, p = 0.44) were more common in pregnancy compared to the general population but the difference was not statistically significant. Failure to retrieve the filter is more likely to occur in pregnancy (26% vs. 11%, p = 0.006) but this did not correlate with the type of device (p = 0.61), duration of insertion (p = 0.58) or mode of delivery (p = 0.37). Data for retrievable IVC filters in pregnancy is limited and there may be a publication bias towards complicated cases. This study
Proofs and Techniques Useful for Deriving the Kalman Filter
National Research Council Canada - National Science Library
Koks, Don
2008-01-01
This note is a tutorial in matrix manipulation and the normal distribution of statistics, concepts that are important for deriving and analysing the Kalman Filter, a basic tool of signal processing...
Image Recommendation Algorithm Using Feature-Based Collaborative Filtering
Kim, Deok-Hwan
As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
Locally-adaptive Myriad Filters for Processing ECG Signals in Real Time
Directory of Open Access Journals (Sweden)
Nataliya Tulyakova
2017-03-01
Full Text Available The locally adaptive myriad filters to suppress noise in electrocardiographic (ECG signals in almost in real time are proposed. Statistical estimates of efficiency according to integral values of such criteria as mean square error (MSE and signal-to-noise ratio (SNR for the test ECG signals sampled at 400 Hz embedded in additive Gaussian noise with different values of variance are obtained. Comparative analysis of adaptive filters is carried out. High efficiency of ECG filtering and high quality of signal preservation are demonstrated. It is shown that locally adaptive myriad filters provide higher degree of suppressing additive Gaussian noise with possibility of real time implementation.
Joint fundamental frequency and order estimation using optimal filtering
Directory of Open Access Journals (Sweden)
Jakobsson Andreas
2011-01-01
Full Text Available Abstract In this paper, the problem of jointly estimating the number of harmonics and the fundamental frequency of periodic signals is considered. We show how this problem can be solved using a number of methods that either are or can be interpreted as filtering methods in combination with a statistical model selection criterion. The methods in question are the classical comb filtering method, a maximum likelihood method, and some filtering methods based on optimal filtering that have recently been proposed, while the model selection criterion is derived herein from the maximum a posteriori principle. The asymptotic properties of the optimal filtering methods are analyzed and an order-recursive efficient implementation is derived. Finally, the estimators have been compared in computer simulations that show that the optimal filtering methods perform well under various conditions. It has previously been demonstrated that the optimal filtering methods perform extremely well with respect to fundamental frequency estimation under adverse conditions, and this fact, combined with the new results on model order estimation and efficient implementation, suggests that these methods form an appealing alternative to classical methods for analyzing multi-pitch signals.
Hiding objects and creating illusions above a carpet filter using a Fourier optics approach.
Wu, Kedi; Wang, Guo Ping
2010-09-13
Invisibility carpet cloaks are usually used to hide an object beneath carpet. In this paper we propose and demonstrate a carpet filter to hide objects and create illusions above the filter by using a Fourier optics method. Instead of using transformation optics, we get electromagnetic parameters of the filter by optical transfer functions, which play the role of modulating the propagation of the scattering angular spectrum directly from an object above the filter. By further adding a functional layer onto the filter, we can even camouflage the object so that it appears to be a different object. The analytical results are confirmed by numerical simulations. Our method is completely different from the current coordinate transfer method and may provide another point of view to more clearly understand the mechanism of invisibility cloaks.
Mean-square performance of a convex combination of two adaptive filters
DEFF Research Database (Denmark)
Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.
2006-01-01
Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination i...
A Statistical Approach for Gain Bandwidth Prediction of Phoenix-Cell Based Reflect arrays
Directory of Open Access Journals (Sweden)
Hassan Salti
2018-01-01
Full Text Available A new statistical approach to predict the gain bandwidth of Phoenix-cell based reflectarrays is proposed. It combines the effects of both main factors that limit the bandwidth of reflectarrays: spatial phase delays and intrinsic bandwidth of radiating cells. As an illustration, the proposed approach is successfully applied to two reflectarrays based on new Phoenix cells.
Vehicle Sideslip Angle Estimation Based on Hybrid Kalman Filter
Directory of Open Access Journals (Sweden)
Jing Li
2016-01-01
Full Text Available Vehicle sideslip angle is essential for active safety control systems. This paper presents a new hybrid Kalman filter to estimate vehicle sideslip angle based on the 3-DoF nonlinear vehicle dynamic model combined with Magic Formula tire model. The hybrid Kalman filter is realized by combining square-root cubature Kalman filter (SCKF, which has quick convergence and numerical stability, with square-root cubature based receding horizon Kalman FIR filter (SCRHKF, which has robustness against model uncertainty and temporary noise. Moreover, SCKF and SCRHKF work in parallel, and the estimation outputs of two filters are merged by interacting multiple model (IMM approach. Experimental results show the accuracy and robustness of the hybrid Kalman filter.
Sensory Pollution from Bag Filters, Carbon Filters and Combinations
DEFF Research Database (Denmark)
Bekö, Gabriel; Clausen, Geo; Weschler, Charles J.
2008-01-01
by an upstream pre-filter (changed monthly), an EU7 filter protected by an upstream activated carbon (AC) filter, and EU7 filters with an AC filter either downstream or both upstream and downstream. In addition, two types of stand-alone combination filters were evaluated: a bag-type fiberglass filter...... that contained AC and a synthetic fiber cartridge filter that contained AC. Air that had passed through used filters was most acceptable for those sets in which an AC filter was used downstream of the particle filter. Comparable air quality was achieved with the stand-alone bag filter that contained AC...
Filters in topology optimization based on Helmholtz‐type differential equations
DEFF Research Database (Denmark)
Lazarov, Boyan Stefanov; Sigmund, Ole
2011-01-01
The aim of this paper is to apply a Helmholtz‐type partial differential equation as an alternative to standard density filtering in topology optimization problems. Previously, this approach has been successfully applied as a sensitivity filter. The usual filtering techniques in topology...... from the neighbor subdomains is an expensive operation. The proposed filter technique requires only mesh information necessary for the finite element discretization of the problem. The main idea is to define the filtered variable implicitly as a solution of a Helmholtz‐type differential equation...
Use of metameric filters for future interference security image structures
Baloukas, Bill; Larouche, Stéphane; Martinu, Ludvik
2006-02-01
In the present work, we describe innovative approaches and properties that can be added to the already popular thin film optically variable devices (OVD) used on banknotes. We show two practical examples of OVDs, namely (i) a pair of metameric filters offering a hidden image effect as a function of the angle of observation as well as a specific spectral property permitting automatic note readability, and (ii) multi-material filters offering a side-dependent color shift. We first describe the design approach of these new devices followed by their sensitivity to deposition errors especially in the case of the metameric filters where slight thickness variations have a significant effect on the obtained colors. The performance of prototype filters prepared by dual ion beam sputtering (DIBS) is shown.
Optimized Beam Sculpting with Generalized Fringe-rate Filters
Parsons, Aaron R.; Liu, Adrian; Ali, Zaki S.; Cheng, Carina
2016-03-01
We generalize the technique of fringe-rate filtering, whereby visibilities measured by a radio interferometer are re-weighted according to their temporal variation. As the Earth rotates, radio sources traverse through an interferometer’s fringe pattern at rates that depend on their position on the sky. Capitalizing on this geometric interpretation of fringe rates, we employ time-domain convolution kernels to enact fringe-rate filters that sculpt the effective primary beam of antennas in an interferometer. As we show, beam sculpting through fringe-rate filtering can be used to optimize measurements for a variety of applications, including mapmaking, minimizing polarization leakage, suppressing instrumental systematics, and enhancing the sensitivity of power-spectrum measurements. We show that fringe-rate filtering arises naturally in minimum variance treatments of many of these problems, enabling optimal visibility-based approaches to analyses of interferometric data that avoid systematics potentially introduced by traditional approaches such as imaging. Our techniques have recently been demonstrated in Ali et al., where new upper limits were placed on the 21 {cm} power spectrum from reionization, showcasing the ability of fringe-rate filtering to successfully boost sensitivity and reduce the impact of systematics in deep observations.
Dai, Mingwei; Ming, Jingsi; Cai, Mingxuan; Liu, Jin; Yang, Can; Wan, Xiang; Xu, Zongben
2017-09-15
Results from genome-wide association studies (GWAS) suggest that a complex phenotype is often affected by many variants with small effects, known as 'polygenicity'. Tens of thousands of samples are often required to ensure statistical power of identifying these variants with small effects. However, it is often the case that a research group can only get approval for the access to individual-level genotype data with a limited sample size (e.g. a few hundreds or thousands). Meanwhile, summary statistics generated using single-variant-based analysis are becoming publicly available. The sample sizes associated with the summary statistics datasets are usually quite large. How to make the most efficient use of existing abundant data resources largely remains an open question. In this study, we propose a statistical approach, IGESS, to increasing statistical power of identifying risk variants and improving accuracy of risk prediction by i ntegrating individual level ge notype data and s ummary s tatistics. An efficient algorithm based on variational inference is developed to handle the genome-wide analysis. Through comprehensive simulation studies, we demonstrated the advantages of IGESS over the methods which take either individual-level data or summary statistics data as input. We applied IGESS to perform integrative analysis of Crohns Disease from WTCCC and summary statistics from other studies. IGESS was able to significantly increase the statistical power of identifying risk variants and improve the risk prediction accuracy from 63.2% ( ±0.4% ) to 69.4% ( ±0.1% ) using about 240 000 variants. The IGESS software is available at https://github.com/daviddaigithub/IGESS . zbxu@xjtu.edu.cn or xwan@comp.hkbu.edu.hk or eeyang@hkbu.edu.hk. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
International Nuclear Information System (INIS)
Ichinose, Shoichi
2010-01-01
A geometric approach to general quantum statistical systems (including the harmonic oscillator) is presented. It is applied to Casimir energy and the dissipative system with friction. We regard the (N+1)-dimensional Euclidean coordinate system (X i ,τ) as the quantum statistical system of N quantum (statistical) variables (X τ ) and one Euclidean time variable (t). Introducing paths (lines or hypersurfaces) in this space (X τ ,t), we adopt the path-integral method to quantize the mechanical system. This is a new view of (statistical) quantization of the mechanical system. The system Hamiltonian appears as the area. We show quantization is realized by the minimal area principle in the present geometric approach. When we take a line as the path, the path-integral expressions of the free energy are shown to be the ordinary ones (such as N harmonic oscillators) or their simple variation. When we take a hyper-surface as the path, the system Hamiltonian is given by the area of the hyper-surface which is defined as a closed-string configuration in the bulk space. In this case, the system becomes a O(N) non-linear model. We show the recently-proposed 5 dimensional Casimir energy (ArXiv:0801.3064,0812.1263) is valid. We apply this approach to the visco-elastic system, and present a new method using the path-integral for the calculation of the dissipative properties.
Comparison of Three Nonlinear Filters for Fault Detection in Continuous Glucose Monitors
DEFF Research Database (Denmark)
Mahmoudi, Zeinab; Wendt, Sabrina Lyngbye; Boiroux, Dimitri
2016-01-01
model of the glucose-insulin dynamics in people with type 1 diabetes. Drift is modelled by a Gaussian random walk and is detected based on the statistical tests of the 90-min prediction residuals of the filters. The unscented Kalman filter had the highest average F score of 85.9%, and the smallest...
DEFF Research Database (Denmark)
Fock, Jeppe; Sørensen, Jakob Kryger; Lörtscher, Emanuel
2011-01-01
We report on the vibrational fingerprint of single C(60) terminated molecules in a mechanically controlled break junction (MCBJ) setup using a novel statistical approach manipulating the junction mechanically to address different molecular configurations and to monitor the corresponding vibration...
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…
International Nuclear Information System (INIS)
Son, Soon Yong; Lee, Hee Jeong; Lee, Won Hong; Cho, Cheong Chan; Ryu, Meung Sun; Jung, Hong Ryang
2001-01-01
Over-density of pancreatic duct tail part on the endoscopic retrograde pancreatogram results from patient's position and inserted air during the study. The aim of this paper is to decide the filter angle to obtain an uniform density. Endoscopic retrograde pancratography was performed to 234 patients, and angled wedge filter was used differently. They are 10 deg (47), 20 deg (45), 30 deg (50). We also did not use wedge filter to 42 patients. We decided reliance degree in 95%. The statistical difference was p<0.05. The patients' sex rate was 1.8:1 between 18 and 87 years old(average age 58 years). Their body girth was 18.71 cm on the average. Of total 234 patients, difference of right and left average density was 0.01 by 30 deg wedge filter, -0.08 40 deg wedge filter and 0.27 non-wedge filter. These average values of difference density were very significant statistically, and standard deviation also was close to regular distribution. In conclusion, there is a usefulness of angled wedge filter for increasing diagnostic value of pancreatic duct tail part on the endoscopic retrograde pancreatogram
Favaro, Livio; Gamba, Marco; Alfieri, Chiara; Pessani, Daniela; McElligott, Alan G
2015-11-25
The African penguin is a nesting seabird endemic to southern Africa. In penguins of the genus Spheniscus vocalisations are important for social recognition. However, it is not clear which acoustic features of calls can encode individual identity information. We recorded contact calls and ecstatic display songs of 12 adult birds from a captive colony. For each vocalisation, we measured 31 spectral and temporal acoustic parameters related to both source and filter components of calls. For each parameter, we calculated the Potential of Individual Coding (PIC). The acoustic parameters showing PIC ≥ 1.1 were used to perform a stepwise cross-validated discriminant function analysis (DFA). The DFA correctly classified 66.1% of the contact calls and 62.5% of display songs to the correct individual. The DFA also resulted in the further selection of 10 acoustic features for contact calls and 9 for display songs that were important for vocal individuality. Our results suggest that studying the anatomical constraints that influence nesting penguin vocalisations from a source-filter perspective, can lead to a much better understanding of the acoustic cues of individuality contained in their calls. This approach could be further extended to study and understand vocal communication in other bird species.
Onisko, Agnieszka; Druzdzel, Marek J; Austin, R Marshall
2016-01-01
Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.
M2 FILTER FOR SPECKLE NOISE SUPPRESSION IN BREAST ULTRASOUND IMAGES
Directory of Open Access Journals (Sweden)
E.S. Samundeeswari
2016-11-01
Full Text Available Breast cancer, commonly found in women is a serious life threatening disease due to its invasive nature. Ultrasound (US imaging method plays an effective role in screening early detection and diagnosis of Breast cancer. Speckle noise generally affects medical ultrasound images and also causes a number of difficulties in identifying the Region of Interest. Suppressing speckle noise is a challenging task as it destroys fine edge details. No specific filter is designed yet to get a noise free BUS image that is contaminated by speckle noise. In this paper M2 filter, a novel hybrid of linear and nonlinear filter is proposed and compared to other spatial filters with 3×3 kernel size. The performance of the proposed M2 filter is measured by statistical quantity parameters like MSE, PSNR and SSI. The experimental analysis clearly shows that the proposed M2 filter outperforms better than other spatial filters by 2% high PSNR values with regards to speckle suppression.
Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis
Gu, Y.H.; Katsaggelos, A.K.
1994-01-01
A new multiresolution image analysis approach using adaptive Hermite-Binomial filters is presented in this paper. According to the local image structural and textural properties, the analysis filter kernels are made adaptive both in their scales and orders. Applications of such an adaptive filtering
SAR Imagery Segmentation by Statistical Region Growing and Hierarchical Merging
Energy Technology Data Exchange (ETDEWEB)
Ushizima, Daniela Mayumi; Carvalho, E.A.; Medeiros, F.N.S.; Martins, C.I.O.; Marques, R.C.P.; Oliveira, I.N.S.
2010-05-22
This paper presents an approach to accomplish synthetic aperture radar (SAR) image segmentation, which are corrupted by speckle noise. Some ordinary segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, eliminating preprocessing steps, an advantage over most of the current methods. The algorithm comprises a statistical region growing procedure combined with hierarchical region merging to extract regions of interest from SAR images. The region growing step over-segments the input image to enable region aggregation by employing a combination of the Kolmogorov-Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for the process coordination. We have tested and assessed the proposed technique on artificially speckled image and real SAR data containing different types of targets.
Cryogenic Filters for RFI Mitigation in Radioastronomy
Tuccari, G.; Caddemi, A.; Barbarino, S.; Nicotra, G.; Consoli, F.; Schilliro, F.; Catalfamo, F.
2005-01-01
RFI mitigation in Radioastronomy can be achieved adopting cryogenic filters in appropriate typologies. A study has been conducted in L, C and X band with the evaluation of the filter architecture in copper, with theoretical estimation, computer simulations, prototypes realization, laboratory measurements. Such work has been preliminary to the realization of HTS samples with the purpose of a similar complete characterization approach.
Dynamic beam filtering for miscentered patients.
Mao, Andrew; Shyr, William; Gang, Grace J; Stayman, J Webster
2018-02-01
Accurate centering of the patient within the bore of a CT scanner takes time and is often difficult to achieve precisely. Patient miscentering can result in significant dose and image noise penalties with the use of traditional bowtie filters. This work describes a system to dynamically position an x-ray beam filter during image acquisition to enable more consistent image performance and potentially lower dose needed for CT imaging. We propose a new approach in which two orthogonal low-dose scout images are used to estimate a parametric model of the object describing its shape, size, and location within the field of view (FOV). This model is then used to compute an optimal filter motion profile by minimizing the variance of the expected detector fluence for each projection. Dynamic filtration was implemented on a cone-beam CT (CBCT) test bench using two different physical filters: 1) an aluminum bowtie and 2) a structured binary filter called a multiple aperture device (MAD). Dynamic filtration performance was compared to a static filter in studies of dose and reconstruction noise as a function of the degree of miscentering of a homogeneous water phantom. Estimated filter trajectories were found to be largely sinusoidal with an amplitude proportional to the amount of miscentering. Dynamic filtration demonstrated an improved ability to keep the spatial distribution of dose and reconstruction noise at baseline levels across varying levels of miscentering, reducing the maximum noise and dose deviation from 53% to 15% and 42% to 14% respectively for the bowtie filter, and 25% to 8% and 24% to 15% respectively for the MAD filter. Dynamic positioning of beam filters during acquisition improves dose utilization and image quality over static filters for miscentered patients. Such dynamic filters relax positioning requirements and have the potential to reduce set-up time and lower dose requirements.
A new greedy search method for the design of digital IIR filter
Directory of Open Access Journals (Sweden)
Ranjit Kaur
2015-07-01
Full Text Available A new greedy search method is applied in this paper to design the optimal digital infinite impulse response (IIR filter. The greedy search method is based on binary successive approximation (BSA and evolutionary search (ES. The suggested greedy search method optimizes the magnitude response and the phase response simultaneously and also finds the lowest order of the filter. The order of the filter is controlled by a control gene whose value is also optimized along with the filter coefficients to obtain optimum order of designed IIR filter. The stability constraints of IIR filter are taken care of during the design procedure. To determine the trade-off relationship between conflicting objectives in the non-inferior domain, the weighting method is exploited. The proposed approach is effectively applied to solve the multiobjective optimization problems of designing the digital low-pass (LP, high-pass (HP, bandpass (BP, and bandstop (BS filters. It has been demonstrated that this technique not only fulfills all types of filter performance requirements, but also the lowest order of the filter can be found. The computational experiments show that the proposed approach gives better digital IIR filters than the existing evolutionary algorithm (EA based methods.
International Nuclear Information System (INIS)
Yeh, L.
1992-01-01
The phase-space-picture approach to quantum non-equilibrium statistical mechanics via the characteristic function of infinite- mode squeezed coherent states is introduced. We use quantum Brownian motion as an example to show how this approach provides an interesting geometrical interpretation of quantum non-equilibrium phenomena
Zhang, Harrison G; Ying, Gui-Shuang
2018-02-09
The aim of this study is to evaluate the current practice of statistical analysis of eye data in clinical science papers published in British Journal of Ophthalmology ( BJO ) and to determine whether the practice of statistical analysis has improved in the past two decades. All clinical science papers (n=125) published in BJO in January-June 2017 were reviewed for their statistical analysis approaches for analysing primary ocular measure. We compared our findings to the results from a previous paper that reviewed BJO papers in 1995. Of 112 papers eligible for analysis, half of the studies analysed the data at an individual level because of the nature of observation, 16 (14%) studies analysed data from one eye only, 36 (32%) studies analysed data from both eyes at ocular level, one study (1%) analysed the overall summary of ocular finding per individual and three (3%) studies used the paired comparison. Among studies with data available from both eyes, 50 (89%) of 56 papers in 2017 did not analyse data from both eyes or ignored the intereye correlation, as compared with in 60 (90%) of 67 papers in 1995 (P=0.96). Among studies that analysed data from both eyes at an ocular level, 33 (92%) of 36 studies completely ignored the intereye correlation in 2017, as compared with in 16 (89%) of 18 studies in 1995 (P=0.40). A majority of studies did not analyse the data properly when data from both eyes were available. The practice of statistical analysis did not improve in the past two decades. Collaborative efforts should be made in the vision research community to improve the practice of statistical analysis for ocular data. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Investigation and deactivation of B Plant HEPA filters
International Nuclear Information System (INIS)
Roege, P.E.
1997-01-01
This paper describes the integrated approach used to manage environmental, safety, and health considerations related to the B Plant canyon exhaust air filters at the US Department of Energy (DOE) Hanford Site. The narrative illustrates the development and implementation of integrated safety management as applied to a facility and its systems undergoing deactivation. During their lifetime, the high efficiency particulate air (HEPA) filters prevented the release of significant quantities of radioactive materials into the air. As the material in B Plant AVESF accumulated on the filters, it created an unusual situation. Over long periods of time, the radiation dose from the filter loading, combined with aging and chemical exposure actually degrade those filters which were intended to protect against any release to the environment
Goodenberger, Martin H; Wagner-Bartak, Nicolaus A; Gupta, Shiva; Liu, Xinming; Yap, Ramon Q; Sun, Jia; Tamm, Eric P; Jensen, Corey T
The purpose of this study was to compare abdominopelvic computed tomography images reconstructed with adaptive statistical iterative reconstruction-V (ASIR-V) with model-based iterative reconstruction (Veo 3.0), ASIR, and filtered back projection (FBP). Abdominopelvic computed tomography scans for 36 patients (26 males and 10 females) were reconstructed using FBP, ASIR (80%), Veo 3.0, and ASIR-V (30%, 60%, 90%). Mean ± SD patient age was 32 ± 10 years with mean ± SD body mass index of 26.9 ± 4.4 kg/m. Images were reviewed by 2 independent readers in a blinded, randomized fashion. Hounsfield unit, noise, and contrast-to-noise ratio (CNR) values were calculated for each reconstruction algorithm for further comparison. Phantom evaluation of low-contrast detectability (LCD) and high-contrast resolution was performed. Adaptive statistical iterative reconstruction-V 30%, ASIR-V 60%, and ASIR 80% were generally superior qualitatively compared with ASIR-V 90%, Veo 3.0, and FBP (P ASIR-V 60% with respective CNR values of 5.54 ± 2.39, 8.78 ± 3.15, and 3.49 ± 1.77 (P ASIR 80% had the best and worst spatial resolution, respectively. Adaptive statistical iterative reconstruction-V 30% and ASIR-V 60% provided the best combination of qualitative and quantitative performance. Adaptive statistical iterative reconstruction 80% was equivalent qualitatively, but demonstrated inferior spatial resolution and LCD.
Statistical approach of weakly nonlinear ablative Rayleigh-Taylor instability
International Nuclear Information System (INIS)
Garnier, J.; Masse, L.
2005-01-01
A weakly nonlinear model is proposed for the Rayleigh-Taylor instability in presence of ablation and thermal transport. The nonlinear effects for a single-mode disturbance are computed, included the nonlinear correction to the exponential growth of the fundamental modulation. Mode coupling in the spectrum of a multimode disturbance is thoroughly analyzed by a statistical approach. The exponential growth of the linear regime is shown to be reduced by the nonlinear mode coupling. The saturation amplitude is around 0.1λ for long wavelengths, but higher for short instable wavelengths in the ablative regime
Statistical margin to DNB safety analysis approach for LOFT
International Nuclear Information System (INIS)
Atkinson, S.A.
1982-01-01
A method was developed and used for LOFT thermal safety analysis to estimate the statistical margin to DNB for the hot rod, and to base safety analysis on desired DNB probability limits. This method is an advanced approach using response surface analysis methods, a very efficient experimental design, and a 2nd-order response surface equation with a 2nd-order error propagation analysis to define the MDNBR probability density function. Calculations for limiting transients were used in the response surface analysis thereby including transient interactions and trip uncertainties in the MDNBR probability density
Design Characteristics and Tobacco Metal Concentrations in Filtered Cigars.
Caruso, Rosalie V; O'Connor, Richard J; Travers, Mark J; Delnevo, Cristine D; Stephens, W Edryd
2015-11-01
While U.S. cigarette consumption has declined, cigar use has steadily increased, for reasons including price compared to cigarettes and the availability of filtered varieties resembling cigarettes, and flavors that have been banned in cigarettes (excluding menthol). Little published data exists on the design characteristics of such cigars. A variety of filtered cigar brands were tested for design characteristics such as whole cigar weight, ventilation, and per-cigar tobacco weight. Cigar sticks were then sent to the University of St. Andrews for metal concentration testing of As, Pb, Cr, Ni, and Cd. Large and small cigars were statistically different between cigar weight (p ≤ .001), per-cigar tobacco weight (p = .001), rod diameter (p = .006), and filter diameter (p = .012). The differences in mean ventilation (overall mean = 19.6%, min. = 0.84%, max. = 57.6%) across filtered cigar brands were found to be statistically significant (p = .031), and can be compared to the ventilation of the average of 2013 U.S. Marlboro Red, Gold, and Silver packs at 29% ventilation. There were no significant differences for metal concentrations between cigar types (p = .650), with Pb and As levels being similar to U.S. 2009 cigarette concentrations, Cd cigar levels being slightly higher, and Cr and Ni levels much lower than cigarette levels. With cigar use rising, and filtered cigars displaying substantial similarities to filtered cigarettes, more research on product characteristics is warranted. Future plans include testing tobacco alkaloid and more observation of cigar weight for tax bracket purposes. © The Author 2015. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1991-03-01
Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x 2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs
Green, Robert D.; Agui, Juan H.; Vijayakumar, R.
2017-01-01
The air revitalization system aboard the International Space Station (ISS) provides the vital function of maintaining a clean cabin environment for the crew and the hardware. This becomes a serious challenge in pressurized space compartments since no outside air ventilation is possible, and a larger particulate load is imposed on the filtration system due to lack of sedimentation due to the microgravity environment in Low Earth Orbit (LEO). The ISS Environmental Control and Life Support (ECLS) system architecture in the U.S. Segment uses a distributed particulate filtration approach consisting of traditional High-Efficiency Particulate Adsorption (HEPA) media filters deployed at multiple locations in each U.S. Segment module; these filters are referred to as Bacterial Filter Elements, or BFEs. These filters see a replacement interval, as part of maintenance, of 2-5 years dependent on location in the ISS. In this work, we present particulate removal efficiency, pressure drop, and leak test results for a sample set of 8 BFEs returned from the ISS after filter replacement. The results can potentially be utilized by the ISS Program to ascertain whether the present replacement interval can be maintained or extended to balance the on-ground filter inventory with extension of the lifetime of ISS beyond 2024. These results can also provide meaningful guidance for particulate filter designs under consideration for future deep space exploration missions.
Control strategies of active filters in the context of power conditioning
DEFF Research Database (Denmark)
Dan, Stan George; Benjamin, Doniga Daniel; Magureanu, R.
2005-01-01
performance of active filters, and thus constitute a viable improved approach for harmonic compensation. In this paper a parallel hybrid filter is studied for current harmonic compensation. The hybrid filter is formed by a single tuned LC filter per phase and a small-rated power three-phase active filter......Active Power Filters have been intensively explored in the past decade. Various topologies and control schemes have been documented aiming at reducing the cost and improving the performance of the compensation system. Hybrid active filters inherit the efficiency of passive filters and the improved......, which are directly connected in series without any matching transformer. The required rating of the active filter is much smaller than a conventional standalone active filter would be. No additional switching-ripple filter is required for the hybrid filter because the LC filter functions not only...
Design of efficient circularly symmetric two-dimensional variable digital FIR filters.
Bindima, Thayyil; Elias, Elizabeth
2016-05-01
Circularly symmetric two-dimensional (2D) finite impulse response (FIR) filters find extensive use in image and medical applications, especially for isotropic filtering. Moreover, the design and implementation of 2D digital filters with variable fractional delay and variable magnitude responses without redesigning the filter has become a crucial topic of interest due to its significance in low-cost applications. Recently the design using fixed word length coefficients has gained importance due to the replacement of multipliers by shifters and adders, which reduces the hardware complexity. Among the various approaches to 2D design, transforming a one-dimensional (1D) filter to 2D by transformation, is reported to be an efficient technique. In this paper, 1D variable digital filters (VDFs) with tunable cut-off frequencies are designed using Farrow structure based interpolation approach, and the sub-filter coefficients in the Farrow structure are made multiplier-less using canonic signed digit (CSD) representation. The resulting performance degradation in the filters is overcome by using artificial bee colony (ABC) optimization. Finally, the optimized 1D VDFs are mapped to 2D using generalized McClellan transformation resulting in low complexity, circularly symmetric 2D VDFs with real-time tunability.
Kalman Filtering for Delayed Singular Systems with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang
2016-01-01
Kalman filtering problem for singular systems is dealt with,where the measurements consist of instantaneous measurements and delayed ones,and the plant includes multiplicative noise.By utilizing standard singular value decomposition,the restricted equivalent delayed system is presented,and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma.The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations,and a numerical example is presented to show the validity and efficiency of the proposed approach,where the comparison between the filter and predictor is also given.
Kalman Filtering for Delayed Singular Systems with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang
2016-01-01
Kalman filtering problem for singular systems is dealt with, where the measurements consist of instantaneous measurements and delayed ones, and the plant includes multiplicative noise. By utilizing standard singular value decomposition, the restricted equivalent delayed system is presented, and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma. The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations, and a numerical example is presented to show the validity and efficiency of the proposed approach, where the comparison between the filter and predictor is also given.
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.
Word2vec and dictionary based approach for uyghur text filtering
Tohti, Turdi; Zhao, Yunxing; Musajan, Winira
2017-08-01
With emerging of deep learning, the expression of words in computer has made major breakthroughs and the effect of text processing based on word vector has also been significantly improved. This paper maps all patterns into a more abstract vector space by Uyghur-Chinese dictionary and deep learning tool Word2vec, at first. Secondly, a similar pattern is found according the characteristics of the original pattern. Finally, texts are filtered using Wu-Manber algorithm. Experiments show that this method can get obvious filtering accuracy and recall of Uyghur text information improved.
Tornadoes and related damage costs: statistical modeling with a semi-Markov approach
Corini, Chiara; D'Amico, Guglielmo; Petroni, Filippo; Prattico, Flavio; Manca, Raimondo
2015-01-01
We propose a statistical approach to tornadoes modeling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modeling the tornadoes intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornadoes intensity into six states, it is possible to model the tornadoes intensity by using Markov and semi-Markov models. We demonstrate that the semi-Markov approach is able to reprod...
Design of Microwave Multibandpass Filters with Quasilumped Resonators
Directory of Open Access Journals (Sweden)
Dejan Miljanović
2015-01-01
Full Text Available Design of RF and microwave filters has always been the challenging engineering field. Modern filter design techniques involve the use of the three-dimensional electromagnetic (3D EM solvers for predicting filter behavior, yielding the most accurate filter characteristics. However, the 3D EM simulations are time consuming. In this paper, we propose electric-circuit models, instead of 3D EM models, suitable for design of RF and microwave filters with quasilumped coupled resonators. Using the diakoptic approach, the 3D filter structure is decomposed into domains that are modeled by electric networks. The coupling between these domains is modeled by capacitors and coupled inductors. Furthermore, we relate the circuit-element values to the physical dimensions of the 3D filter structure. We propose the filter design procedure that is based on the circuit models and fast circuit-level simulations, yielding the element values from which the physical dimensions can be obtained. The obtained dimensions should be slightly refined for achieving the desired filter characteristics. The mathematical problems encountered in the procedure are solved by numerical and symbolic computations. The procedure is exemplified by designing a triple-bandpass filter and validated by measurements on the fabricated filter. The simulation and experimental results are in good agreement.
Kawaji, Keigo; Patel, Mita B; Cantrell, Charles G; Tanaka, Akiko; Marino, Marco; Tamura, Satoshi; Wang, Hui; Wang, Yi; Carroll, Timothy J; Ota, Takeyoshi; Patel, Amit R
2017-07-01
To introduce a pair of accelerated non-Cartesian acquisition principles that when combined, exploit the periodicity of k-space acquisition, and thereby enable acquisition of high-temporal cine Cardiac Magnetic Resonance (CMR). The mathematical formulation of a noniterative, undersampled non-Cartesian cine acquisition and reconstruction is presented. First, a low-pass filtering step that exploits streaking artifact redundancy is provided (i.e., Dynamically Interleaved Streak removal in the Power-spectrum Encoded domain with Low-pass filtering [DISPEL]). Next, an effective radial acquisition for the DISPEL approach that exploits the property of prime numbers is described (i.e., Modulo-Prime Spoke [MoPS]). Both DISPEL and MoPS are examined using numerical simulation of a digital heart phantom to show that high-temporal cine-CMR is feasible without removing physiologic motion vs aperiodic interleaving using Golden Angles. The combined high-temporal cine approach is next examined in 11 healthy subjects for a time-volume curve assessment of left ventricular systolic and diastolic performance vs conventional Cartesian cine-CMR reference. The DISPEL method was first shown using simulation under different streak cycles to allow separation of undersampled radial streaking artifacts from physiologic motion with a sufficiently frequent streak-cycle interval. Radial interleaving with MoPS is next shown to allow interleaves with pseudo-Golden-Angle variants, and be more compatible with DISPEL against irrational and nonperiodic rotation angles, including the Golden-Angle-derived rotations. In the in vivo data, the proposed method showed no statistical difference in the systolic performance, while diastolic parameters sensitive to the cine's temporal resolution were statistically significant (P cine). We demonstrate a high-temporal resolution cine-CMR using DISPEL and MoPS, whose streaking artifact was separated from physiologic motion. © 2017 American Association of Physicists
Switching Kalman filter for failure prognostic
Lim, Chi Keong Reuben; Mba, David
2015-02-01
The use of condition monitoring (CM) data to predict remaining useful life have been growing with increasing use of health and usage monitoring systems on aircraft. There are many data-driven methodologies available for the prediction and popular ones include artificial intelligence and statistical based approach. The drawback of such approaches is that they require a lot of failure data for training which can be scarce in practice. In lieu of this, methods using state-space and regression-based models that extract information from the data history itself have been explored. However, such methods have their own limitations as they utilize a single time-invariant model which does not represent changing degradation path well. This causes most degradation modeling studies to focus only on segments of their CM data that behaves close to the assumed model. In this paper, a state-space based method; the Switching Kalman Filter (SKF), is adopted for model estimation and life prediction. The SKF approach however, uses multiple models from which the most probable model is inferred from the CM data using Bayesian estimation before it is applied for prediction. At the same time, the inference of the degradation model itself can provide maintainers with more information for their planning. This SKF approach is demonstrated with a case study on gearbox bearings that were found defective from the Republic of Singapore Air Force AH64D helicopter. The use of in-service CM data allows the approach to be applied in a practical scenario and results showed that the developed SKF approach is a promising tool to support maintenance decision-making.
Kalman filter for statistical monitoring of forest cover across sub-continental regions [Symposium
Raymond L. Czaplewski
1991-01-01
The Kalman filter is a generalization of the composite estimator. The univariate composite estimate combines 2 prior estimates of population parameter with a weighted average where the scalar weight is inversely proportional to the variances. The composite estimator is a minimum variance estimator that requires no distributional assumptions other than estimates of the...
Directory of Open Access Journals (Sweden)
T. Subbulakshmi
2014-10-01
Full Text Available Distributed Denial of Service (DDoS attacks were considered to be a tremendous threat to the current information security infrastructure. During DDoS attack, multiple malicious hosts that are recruited by the attackers launch a coordinated attack against one host or a network victim, which cause denial of service to legitimate users. The existing techniques suffer from more number of false alarms and more human intervention for attack detection. The objective of this paper is to monitor the network online which automatically initiates detection mechanism if there is any suspicious activity and also defense the hosts from being arrived at the network. Both spoofed and non spoofed IP’s are detected in this approach. Non spoofed IP’s are detected using Enhanced Support Vector Machines (ESVM and spoofed IP’s are detected using Hop Count Filtering (HCF mechanism. The detected IP’s are maintained separately to initiate the defense process. The attack strength is calculated using Lanchester Law which initiates the defense mechanism. Based on the calculated attack strength any of the defense schemes such as Rate based limiting or History based IP filtering is automatically initiated to drop the packets from the suspected IP. The integrated online monitoring approach for detection and defense of DDoS attacks is deployed in an experimental testbed. The online approach is found to be obvious in the field of integrated DDoS detection and defense.
Computer simulation of HTGR fuel microspheres using a Monte-Carlo statistical approach
International Nuclear Information System (INIS)
Hedrick, C.E.
1976-01-01
The concept and computational aspects of a Monte-Carlo statistical approach in relating structure of HTGR fuel microspheres to the uranium content of fuel samples have been verified. Results of the preliminary validation tests and the benefits to be derived from the program are summarized
Texture classification using autoregressive filtering
Lawton, W. M.; Lee, M.
1984-01-01
A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.
Filtering observations without the initial guess
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
DEFF Research Database (Denmark)
Blaabjerg, Frede; Chiarantoni, Ernesto; Aquila, Antonio Dell
2004-01-01
Three-phase active rectifiers based on the voltage source converter topology can successfully replace traditional thyristor based rectifiers or diode bridge plus chopper in interfacing dc-systems to the grid. However, if the application in which they are employed has a high safety issue......, to the grid side stiffness and to the parameters of the controller has never been detailed considered. In this paper the experimental results of an LCL-filter-based three-phase active rectifier are analysed with the circuit theory approach. A ?virtual circuit? is synthesized in role of the digital controller...
Early and Late Retrieval of the ALN Removable Vena Cava Filter: Results from a Multicenter Study
International Nuclear Information System (INIS)
Pellerin, O.; Barral, F. G.; Lions, C.; Novelli, L.; Beregi, J. P.; Sapoval, M.
2008-01-01
Retrieval of removable inferior vena cava (IVC) filters in selected patients is widely practiced. The purpose of this multicenter study was to evaluate the feasibility and results of percutaneous removal of the ALN removable filter in a large patient cohort. Between November 2003 and June 2006, 123 consecutive patients were referred for percutaneous extraction of the ALN filter at three centers. The ALN filter is a removable filter that can be implanted through a femoral/jugular vein approach and extracted by the jugular vein approach. Filter removal was attempted after an implantation period of 93 ± 15 days (range, 6-722 days) through the right internal jugular vein approach using the dedicated extraction kit after control inferior vena cavography. Following filter removal, vena cavograms were obtained in all patients. Successful extraction was achieved in all but one case. Among these successful retrievals, additional manipulation using a femoral approach was needed when the apex of the filter was close to the IVC wall in two patients. No immediate IVC complications were observed according to the postimplantation cavography. Neither technical nor clinical differences between early and late filter retrieval were noticed. Our data confirm the safety of ALN filter retrieval up to 722 days after implantation. In infrequent cases, additional endovenous filter manipulation is needed to facilitate extraction.
Adair, Desmond; Jaeger, Martin; Price, Owen M.
2018-01-01
The use of a portfolio curriculum approach, when teaching a university introductory statistics and probability course to engineering students, is developed and evaluated. The portfolio curriculum approach, so called, as the students need to keep extensive records both as hard copies and digitally of reading materials, interactions with faculty,…
Bessel smoothing filter for spectral-element mesh
Trinh, P. T.; Brossier, R.; Métivier, L.; Virieux, J.; Wellington, P.
2017-06-01
Smoothing filters are extremely important tools in seismic imaging and inversion, such as for traveltime tomography, migration and waveform inversion. For efficiency, and as they can be used a number of times during inversion, it is important that these filters can easily incorporate prior information on the geological structure of the investigated medium, through variable coherent lengths and orientation. In this study, we promote the use of the Bessel filter to achieve these purposes. Instead of considering the direct application of the filter, we demonstrate that we can rely on the equation associated with its inverse filter, which amounts to the solution of an elliptic partial differential equation. This enhances the efficiency of the filter application, and also its flexibility. We apply this strategy within a spectral-element-based elastic full waveform inversion framework. Taking advantage of this formulation, we apply the Bessel filter by solving the associated partial differential equation directly on the spectral-element mesh through the standard weak formulation. This avoids cumbersome projection operators between the spectral-element mesh and a regular Cartesian grid, or expensive explicit windowed convolution on the finite-element mesh, which is often used for applying smoothing operators. The associated linear system is solved efficiently through a parallel conjugate gradient algorithm, in which the matrix vector product is factorized and highly optimized with vectorized computation. Significant scaling behaviour is obtained when comparing this strategy with the explicit convolution method. The theoretical numerical complexity of this approach increases linearly with the coherent length, whereas a sublinear relationship is observed practically. Numerical illustrations are provided here for schematic examples, and for a more realistic elastic full waveform inversion gradient smoothing on the SEAM II benchmark model. These examples illustrate well the
Page, Ralph H.; Doty, Patrick F.
2017-08-01
The various technologies presented herein relate to a tiled filter array that can be used in connection with performance of spatial sampling of optical signals. The filter array comprises filter tiles, wherein a first plurality of filter tiles are formed from a first material, the first material being configured such that only photons having wavelengths in a first wavelength band pass therethrough. A second plurality of filter tiles is formed from a second material, the second material being configured such that only photons having wavelengths in a second wavelength band pass therethrough. The first plurality of filter tiles and the second plurality of filter tiles can be interspersed to form the filter array comprising an alternating arrangement of first filter tiles and second filter tiles.
Nonlinear dynamical system identification using unscented Kalman filter
Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan
2016-11-01
Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.
Soja, B.; Krasna, H.; Boehm, J.; Gross, R. S.; Abbondanza, C.; Chin, T. M.; Heflin, M. B.; Parker, J. W.; Wu, X.
2017-12-01
The most recent realizations of the ITRS include several innovations, two of which are especially relevant to this study. On the one hand, the IERS ITRS combination center at DGFI-TUM introduced a two-level approach with DTRF2014, consisting of a classical deterministic frame based on normal equations and an optional coordinate time series of non-tidal displacements calculated from geophysical loading models. On the other hand, the JTRF2014 by the combination center at JPL is a time series representation of the ITRF determined by Kalman filtering. Both the JTRF2014 and the second level of the DTRF2014 are thus able to take into account short-term variations in the station coordinates. In this study, based on VLBI data, we combine these two approaches, applying them to the determination of both terrestrial and celestial reference frames. Our product has two levels like DTRF2014, with the second level being a Kalman filter solution like JTRF2014. First, we compute a classical TRF and CRF in a global least-squares adjustment by stacking normal equations from 5446 VLBI sessions between 1979 and 2016 using the Vienna VLBI and Satellite Software VieVS (solution level 1). Next, we obtain coordinate residuals from the global adjustment by applying the level-1 TRF and CRF in the single-session analysis and estimating coordinate offsets. These residuals are fed into a Kalman filter and smoother, taking into account the stochastic properties of the individual stations and radio sources. The resulting coordinate time series (solution level 2) serve as an additional layer representing irregular variations not considered in the first level of our approach. Both levels of our solution are implemented in VieVS in order to test their individual and combined performance regarding the repeatabilities of estimated baseline lengths, EOP, and radio source coordinates.
Learning approach to the detection of gravitational wave transients
International Nuclear Information System (INIS)
Chassande-Mottin, E.
2003-01-01
We investigate the class of quadratic detectors (i.e., the statistic is a bilinear function of the data) for the detection of poorly modeled gravitational transients of short duration. We point out that all such detection methods are equivalent to passing the signal through a filter bank and linearly combining the output energy. Existing methods for the choice of the filter bank and of the weight parameters (to be multiplied by the output energy of each filter before summation) rely essentially on the two following ideas: (i) the use of the likelihood function based on a (possibly noninformative) statistical model of the signal and the noise; (ii) the use of Monte Carlo simulations for the tuning of parametric filters to get the best detection probability while keeping the false alarm rate fixed. We propose a third approach according to which the filter bank is 'learned' from a set of training data. By-products of this viewpoint are that, contrarily to previous methods, (i) there is no requirement of an explicit description of the probability density function of the data when the signal is present and (ii) the filters we use are nonparametric. The learning procedure may be described as a two step process: first, estimate the mean and covariance of the signal with the training data; second, find the filters which maximize a contrast criterion referred to as the deflection between the 'noise only' and 'signal + noise' hypotheses. The deflection is homogeneous to the signal-to-noise ratio and it uses the quantities estimated at the first step. We apply this original method to the problem of the detection of supernovae core collapses. We use the catalog of waveforms provided recently by Dimmelmeier et al. to train our algorithm. We expect such a detector to have better performances in this particular problem provided that the reference signals are reliable
A CLT on the SNR of Diagonally Loaded MVDR Filters
Rubio, Francisco; Mestre, Xavier; Hachem, Walid
2012-08-01
This paper studies the fluctuations of the signal-to-noise ratio (SNR) of minimum variance distorsionless response (MVDR) filters implementing diagonal loading in the estimation of the covariance matrix. Previous results in the signal processing literature are generalized and extended by considering both spatially as well as temporarily correlated samples. Specifically, a central limit theorem (CLT) is established for the fluctuations of the SNR of the diagonally loaded MVDR filter, under both supervised and unsupervised training settings in adaptive filtering applications. Our second-order analysis is based on the Nash-Poincar\\'e inequality and the integration by parts formula for Gaussian functionals, as well as classical tools from statistical asymptotic theory. Numerical evaluations validating the accuracy of the CLT confirm the asymptotic Gaussianity of the fluctuations of the SNR of the MVDR filter.
Guenther Tulip Filter Retrieval from a Left-sided Inferior Vena Cava
International Nuclear Information System (INIS)
Brountzos, Elias N.; Kaufman, John A.; Lakin, Paul L.
2004-01-01
Optional (retrievable) inferior cava filters (IVC) may have advantages over permanent filters in a certain subset of patients, especially in view of recent concerns about the long-term thrombotic complications of the latter. Retrieval of the Guenther Tulip Filter (GTF), an optional filter, has been reported in a total of 76 patients. We present the first description of GTF retrieval from a left-sided IVC using the right internal jugular approach
Filter forensics: microbiota recovery from residential HVAC filters.
Maestre, Juan P; Jennings, Wiley; Wylie, Dennis; Horner, Sharon D; Siegel, Jeffrey; Kinney, Kerry A
2018-01-30
Establishing reliable methods for assessing the microbiome within the built environment is critical for understanding the impact of biological exposures on human health. High-throughput DNA sequencing of dust samples provides valuable insights into the microbiome present in human-occupied spaces. However, the effect that different sampling methods have on the microbial community recovered from dust samples is not well understood across sample types. Heating, ventilation, and air conditioning (HVAC) filters hold promise as long-term, spatially integrated, high volume samplers to characterize the airborne microbiome in homes and other climate-controlled spaces. In this study, the effect that dust recovery method (i.e., cut and elution, swabbing, or vacuuming) has on the microbial community structure, membership, and repeatability inferred by Illumina sequencing was evaluated. The results indicate that vacuum samples captured higher quantities of total, bacterial, and fungal DNA than swab or cut samples. Repeated swab and vacuum samples collected from the same filter were less variable than cut samples with respect to both quantitative DNA recovery and bacterial community structure. Vacuum samples captured substantially greater bacterial diversity than the other methods, whereas fungal diversity was similar across all three methods. Vacuum and swab samples of HVAC filter dust were repeatable and generally superior to cut samples. Nevertheless, the contribution of environmental and human sources to the bacterial and fungal communities recovered via each sampling method was generally consistent across the methods investigated. Dust recovery methodologies have been shown to affect the recovery, repeatability, structure, and membership of microbial communities recovered from dust samples in the built environment. The results of this study are directly applicable to indoor microbiota studies utilizing the filter forensics approach. More broadly, this study provides a
Application of difference filter to Feynman-α analysis
International Nuclear Information System (INIS)
Mouri, Tomoaki; Ohtani, Nobuo
1997-11-01
The Feynman-α method has been developed for monitoring sub-criticality in nuclear fuel facilities. It is difficult to apply the Feynman-α method which estimates statistical variation of the number of neutron counts per unit time, to the system in transient condition such that the averaged neutron flux varies with time. In the application of Feynman-α method to such system, it is suggested to remove the averaged variation of neutron flux from neutron count data by the use of the difference filter. In this study, we applied the difference filter to reactor noise data at sub-criticality near to criticality, where the prompt decay constant was difficult to estimate due to the large effect of delayed neutron. With the difference filter, accurate prompt decay constants for effective multiplication factors from 0.999 to 0.994 were obtained by Feynman-α method. It was cleared that the difference filter is effective to estimate accurate prompt decay constant, so that there is the prospect to be able to apply Feynman-α method having the difference filter to the system in the transient condition. (author)
Percutaneous Retrieval of Permanent Inferior Vena Cava Filters
Energy Technology Data Exchange (ETDEWEB)
Tamrazi, Anobel, E-mail: atamraz1@jhmi.edu; Wadhwa, Vibhor, E-mail: vwadhwa1@jhmi.edu; Holly, Brian, E-mail: bholly3@jhmi.edu [Johns Hopkins University School of Medicine, Division of Vascular & Interventional Radiology (United States); Bhagat, Nikhil, E-mail: nikhibhagat@gmail.com [Kaiser Permanente, Division of Vascular & Interventional Radiology (United States); Marx, Jonathan K., E-mail: jmarx9@jhmi.edu [Johns Hopkins University School of Medicine, Division of Vascular & Interventional Radiology (United States); Streiff, Michael, E-mail: mstreif@jhmi.edu [Johns Hopkins University School of Medicine, Department of Hematology (United States); Lessne, Mark L., E-mail: mlessne@gmail.com [Johns Hopkins University School of Medicine, Division of Vascular & Interventional Radiology (United States)
2016-04-15
PurposeTo evaluate the feasibility, risks, and techniques of percutaneous removal of permanent TrapEase and Simon Nitinol IVC filters.Materials and MethodsBetween August 2011 and August 2015, 12 patients (5 women, 7 men; age range, 26–75 years) underwent an attempt at percutaneous removal of permanent TrapEase (10) and Simon Nitinol (2) IVC filters due to a history of IVC filter complications or need for lifelong anticoagulation due to the filter. Medical records were reviewed for filter dwell time, presence of iliocaval deep venous thrombosis, procedural technique, and complications.ResultsFilter dwell times ranged from 7 days to 15 years (mean 5.1 years). Successful removal of permanent IVC filters was possible in 11 of 12 patients (91.6 %). In 1 patient, a chronically thrombosed IVC filter could not be removed despite laser sheath assistance, but was successfully recanalized with the PowerWire RF guidewire. In the failed retrieval attempt, a stent was placed through the chronically thrombosed IVC filter with restoration of in-line flow. One major complication of large venous groin hematoma was encountered.ConclusionsIn carefully selected patients, percutaneous removal of permanent IVC filters can be performed safely despite prolonged filter dwell times. Extraction of chronically embedded permanent IVC filters may be facilitated by jugular and femoral approaches, often with laser sheath assistance. Chronic filter thrombosis and caval scarring may increase the risk of retrieval failure.
Percutaneous Retrieval of Permanent Inferior Vena Cava Filters
International Nuclear Information System (INIS)
Tamrazi, Anobel; Wadhwa, Vibhor; Holly, Brian; Bhagat, Nikhil; Marx, Jonathan K.; Streiff, Michael; Lessne, Mark L.
2016-01-01
PurposeTo evaluate the feasibility, risks, and techniques of percutaneous removal of permanent TrapEase and Simon Nitinol IVC filters.Materials and MethodsBetween August 2011 and August 2015, 12 patients (5 women, 7 men; age range, 26–75 years) underwent an attempt at percutaneous removal of permanent TrapEase (10) and Simon Nitinol (2) IVC filters due to a history of IVC filter complications or need for lifelong anticoagulation due to the filter. Medical records were reviewed for filter dwell time, presence of iliocaval deep venous thrombosis, procedural technique, and complications.ResultsFilter dwell times ranged from 7 days to 15 years (mean 5.1 years). Successful removal of permanent IVC filters was possible in 11 of 12 patients (91.6 %). In 1 patient, a chronically thrombosed IVC filter could not be removed despite laser sheath assistance, but was successfully recanalized with the PowerWire RF guidewire. In the failed retrieval attempt, a stent was placed through the chronically thrombosed IVC filter with restoration of in-line flow. One major complication of large venous groin hematoma was encountered.ConclusionsIn carefully selected patients, percutaneous removal of permanent IVC filters can be performed safely despite prolonged filter dwell times. Extraction of chronically embedded permanent IVC filters may be facilitated by jugular and femoral approaches, often with laser sheath assistance. Chronic filter thrombosis and caval scarring may increase the risk of retrieval failure.
Directory of Open Access Journals (Sweden)
Jeng-Wen Lin
2009-01-01
Full Text Available This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a “batch” of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring.
In Situ Cleanable Alternative HEPA Filter Media
International Nuclear Information System (INIS)
Adamson, D. J.; Terry, M. T.
2002-01-01
Energy's Hazardous Facilities'', found that conventional glass fiber HEPA filters are structurally weak and easily damaged by water or fire. The structurally stronger sintered metal and ceramic filters would reduce the potential of a catastrophic HEPA filter failure due to filter media breakthrough in the process ventilation system. An in situ regenerable system may also find application in recovering nuclear materials, such as plutonium, collected on glove box exhaust HEPA filters. This innovative approach of the in situ regenerative filtration system may be a significant improvement upon the shortfalls of conventional disposable HEPA filters
Active Damping Techniques for LCL-Filtered Inverters-Based Microgrids
DEFF Research Database (Denmark)
Lorzadeh, Iman; Firoozabadi, Mehdi Savaghebi; Askarian Abyaneh, Hossein
2015-01-01
LCL-type filters are widely used in gridconnected voltage source inverters, since it provides switching ripples reduction with lower cost and weight than the L-type counterpart. However, the inclusion of LCL-filters in voltage source inverters complicates the current control design regarding system...... the different active damping approaches for grid-connected inverters with LCL filters, which are based on high-order filters and additional feedbacks methods. These techniques are analyzed and discussed in detail....... stability issues; because an inherent resonance peak appears due to zero impedance at that resonance frequency. Moreover, in grid-interactive low-voltage microgrids, the interactions among the LCL-filtered-based parallel inverters may result in a more complex multiresonance issue which may compromise...
Thermodynamics and statistical mechanics an integrated approach
Shell, M Scott
2015-01-01
Learn classical thermodynamics alongside statistical mechanics with this fresh approach to the subjects. Molecular and macroscopic principles are explained in an integrated, side-by-side manner to give students a deep, intuitive understanding of thermodynamics and equip them to tackle future research topics that focus on the nanoscale. Entropy is introduced from the get-go, providing a clear explanation of how the classical laws connect to the molecular principles, and closing the gap between the atomic world and thermodynamics. Notation is streamlined throughout, with a focus on general concepts and simple models, for building basic physical intuition and gaining confidence in problem analysis and model development. Well over 400 guided end-of-chapter problems are included, addressing conceptual, fundamental, and applied skill sets. Numerous worked examples are also provided together with handy shaded boxes to emphasize key concepts, making this the complete teaching package for students in chemical engineer...
Correlation of Spatially Filtered Dynamic Speckles in Distance Measurement Application
International Nuclear Information System (INIS)
Semenov, Dmitry V.; Nippolainen, Ervin; Kamshilin, Alexei A.; Miridonov, Serguei V.
2008-01-01
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 patterns 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
Sikora, Andrzej; Rodak, Aleksander; Unold, Olgierd; Klapetek, Petr
2016-12-01
In this paper a novel approach for the practical utilization of the 2D wavelet filter in terms of the artifacts removal from atomic force microscopy measurements results is presented. The utilization of additional data such as summary photodiode signal map is implemented in terms of the identification of the areas requiring the data processing, filtering settings optimization and the verification of the process performance. Such an approach allows to perform the filtering parameters adjustment by average user, while the straightforward method requires an expertise in this field. The procedure was developed as the function of the Gwyddion software. The examples of filtering the phase imaging and Electrostatic Force Microscopy measurement result are presented. As the wavelet filtering feature may remove a local artifacts, its superior efficiency over similar approach with 2D Fast Fourier Transformate based filter (2D FFT) can be noticed. Copyright © 2016 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Sharifi, Mohsen; Bay, Curt; Skrocki, Laura; Lawson, David; Mazdeh, Shahnaz
2012-01-01
Objectives: The purpose of this study was to evaluate the necessity of and recommend indications for inferior vena cava (IVC) filter implantation during percutaneous endovenous intervention (PEVI) for deep venous thrombosis (DVT).BackgroundPEVI has emerged as a powerful tool in the management of acute proximal DVT. Instrumentation of extensive fresh thrombus is potentially associated with iatrogenic pulmonary embolism (PE). The true frequency of this complication has not been studied in a randomized fashion. We evaluated IVC filter implantation during PEVI for DVT. Methods: A total of 141 patients with symptomatic proximal DVT undergoing PEVI for symptomatic DVT were randomized to receive an IVC filter (70 patients) or no filter (71 patients; control group). The anticoagulation and PEVI regimen were similar between the two groups. Patients with development of symptoms suggestive of PE underwent objective testing for PE. Results: PE developed in 1 of the 14 symptomatic patients in the filter group and 8 of the 22 patients in the control group (P = 0.048). There was no mortality in any group. Three patients (4.2%) in the control group had transient hemodynamic instability necessitating resuscitory efforts. Predictors of iatrogenic PE were found to be PE at admission; involvement of two or more adjacent venous segments with acute thrombus; inflammatory form of DVT (severe erythema, edema, pain, and induration); and vein diameter of ≥7 mm with preserved architecture. Conclusions: IVC filter implantation during PEVI reduces the risk of iatrogenic PE by eightfold without a mortality benefit. A selective approach may be exercised in filter implantation during PEVI.
Energy Technology Data Exchange (ETDEWEB)
Sharifi, Mohsen, E-mail: seyedmohsensharifi@yahoo.com [Arizona Cardiovascular Consultants (United States); Bay, Curt [A.T. Still University, Arizona School of Health Sciences (United States); Skrocki, Laura; Lawson, David; Mazdeh, Shahnaz [Arizona Cardiovascular Consultants (United States)
2012-12-15
Objectives: The purpose of this study was to evaluate the necessity of and recommend indications for inferior vena cava (IVC) filter implantation during percutaneous endovenous intervention (PEVI) for deep venous thrombosis (DVT).BackgroundPEVI has emerged as a powerful tool in the management of acute proximal DVT. Instrumentation of extensive fresh thrombus is potentially associated with iatrogenic pulmonary embolism (PE). The true frequency of this complication has not been studied in a randomized fashion. We evaluated IVC filter implantation during PEVI for DVT. Methods: A total of 141 patients with symptomatic proximal DVT undergoing PEVI for symptomatic DVT were randomized to receive an IVC filter (70 patients) or no filter (71 patients; control group). The anticoagulation and PEVI regimen were similar between the two groups. Patients with development of symptoms suggestive of PE underwent objective testing for PE. Results: PE developed in 1 of the 14 symptomatic patients in the filter group and 8 of the 22 patients in the control group (P = 0.048). There was no mortality in any group. Three patients (4.2%) in the control group had transient hemodynamic instability necessitating resuscitory efforts. Predictors of iatrogenic PE were found to be PE at admission; involvement of two or more adjacent venous segments with acute thrombus; inflammatory form of DVT (severe erythema, edema, pain, and induration); and vein diameter of {>=}7 mm with preserved architecture. Conclusions: IVC filter implantation during PEVI reduces the risk of iatrogenic PE by eightfold without a mortality benefit. A selective approach may be exercised in filter implantation during PEVI.
Enhanced performance of a filter-sensor system.
Sasaki, Isao; Josowicz, Mira; Janata, Jirí; Glezer, Ari
2006-06-01
In this paper are addressed two important, but seemingly unrelated issues: long term performance of a gas sensing array and performance of an air purification unit. It is shown that when considered together, the system can be regarded as a "smart filter". The enhancement is achieved by periodic differential sampling and measurement of the "upstream" and "downstream" gases of a filter. The correctly functioning filter supplies the "zero gas" from the downstream for the continuous sensor baseline correction. A key element in this scheme is the synthetic jet that delivers well-defined pulses of the two gases. The deterioration of the performance of the "smart filter" can be diagnosed from the response pattern of the sensor. The approach has been demonstrated on removal/sensing of ammonia gas from air.
Multiple model cardinalized probability hypothesis density filter
Georgescu, Ramona; Willett, Peter
2011-09-01
The Probability Hypothesis Density (PHD) filter propagates the first-moment approximation to the multi-target Bayesian posterior distribution while the Cardinalized PHD (CPHD) filter propagates both the posterior likelihood of (an unlabeled) target state and the posterior probability mass function of the number of targets. Extensions of the PHD filter to the multiple model (MM) framework have been published and were implemented either with a Sequential Monte Carlo or a Gaussian Mixture approach. In this work, we introduce the multiple model version of the more elaborate CPHD filter. We present the derivation of the prediction and update steps of the MMCPHD particularized for the case of two target motion models and proceed to show that in the case of a single model, the new MMCPHD equations reduce to the original CPHD equations.
Efficient Filtering of Noisy Fingerprint Images
Directory of Open Access Journals (Sweden)
Maria Liliana Costin
2016-01-01
Full Text Available Fingerprint identification is an important field in the wide domain of biometrics with many applications, in different areas such: judicial, mobile phones, access systems, airports. There are many elaborated algorithms for fingerprint identification, but none of them can guarantee that the results of identification are always 100 % accurate. A first step in a fingerprint image analysing process consists in the pre-processing or filtering. If the result after this step is not by a good quality the upcoming identification process can fail. A major difficulty can appear in case of fingerprint identification if the images that should be identified from a fingerprint image database are noisy with different type of noise. The objectives of the paper are: the successful completion of the noisy digital image filtering, a novel more robust algorithm of identifying the best filtering algorithm and the classification and ranking of the images. The choice about the best filtered images of a set of 9 algorithms is made with a dual method of fuzzy and aggregation model. We are proposing through this paper a set of 9 filters with different novelty designed for processing the digital images using the following methods: quartiles, medians, average, thresholds and histogram equalization, applied all over the image or locally on small areas. Finally the statistics reveal the classification and ranking of the best algorithms.
Velocity measurements in a rigid ceramic filter in a parallel-flow arrangement
International Nuclear Information System (INIS)
Al-Hajeri, M.H.; Aroussi, A.; Witry, A.
2002-01-01
Rigid ceramic filters have been developed for cleaning the hot combustion gas streams upstream of the turbine in a combined cycle power plant. To obtain continues operation a periodic cleaning is necessary and the cleaning efficiency depends on the distribution of the filtration cake. Consequently uniform particle deposition on the filter element surface is desired. The flow around three filter elements in cross flow is investigated computationally using the commercial code FLUENT. Three filter elements are placed in a two-dimensional rectangle duct with fixed face velocity and varying the velocity ratio between the approach and face velocity. Particle trajectories are obtained for a number of particle diameters and different inlet (approach) velocity to face filtration velocity ratios to investigate the behavior of particles around the filter element. (author)
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Hoteit, Ibrahim; Luo, Xiaodong; Pham, Dinh-Tuan; Moroz, Irene M.
2010-01-01
In this contribution, we present a Gaussian mixture‐based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz‐96 model to illustrate the performance of the PKF.
Nonlinear Filtering with IMM Algorithm for Ultra-Tight GPS/INS Integration
Directory of Open Access Journals (Sweden)
Dah-Jing Jwo
2013-05-01
Full Text Available Abstract This paper conducts a performance evaluation for the ultra-tight integration of a Global positioning system (GPS and an inertial navigation system (INS, using nonlinear filtering approaches with an interacting multiple model (IMM algorithm. An ultra-tight GPS/INS architecture involves the integration of in-phase and quadrature components from the correlator of a GPS receiver with INS data. An unscented Kalman filter (UKF, which employs a set of sigma points by deterministic sampling, avoids the error caused by linearization as in an extended Kalman filter (EKF. Based on the filter structural adaptation for describing various dynamic behaviours, the IMM nonlinear filtering provides an alternative for designing the adaptive filter in the ultra-tight GPS/INS integration. The use of IMM enables tuning of an appropriate value for the process of noise covariance so as to maintain good estimation accuracy and tracking capability. Two examples are provided to illustrate the effectiveness of the design and demonstrate the effective improvement in navigation estimation accuracy. A performance comparison among various filtering methods for ultra-tight integration of GPS and INS is also presented. The IMM based nonlinear filtering approach demonstrates the effectiveness of the algorithm for improved positioning performance.
Sitaram, Mahesh I; Padiyar, KR; Ramanarayanan, V
1998-01-01
Active filters have long been in use for the filtering of power system load harmonics. In this paper, the digital simulation results of a hybrid active power filter system for a rectifier load are presented. The active filter is used for filtering higher order harmonics as the dominant harmonics are filtered by the passive filter. This reduces the rating of the active filter significantly. The DC capacitor voltage of the active filter is controlled using a PI controller.
Active filtering applied to radiographic images unfolded by the Richardson-Lucy algorithm
International Nuclear Information System (INIS)
Almeida, Gevaldo L. de; Silvani, Maria Ines; Lopes, Ricardo T.
2011-01-01
Degradation of images caused by systematic uncertainties can be reduced when one knows the features of the spoiling agent. Typical uncertainties of this kind arise in radiographic images due to the non - zero resolution of the detector used to acquire them, and from the non-punctual character of the source employed in the acquisition, or from the beam divergence when extended sources are used. Both features blur the image, which, instead of a single point exhibits a spot with a vanishing edge, reproducing hence the point spread function - PSF of the system. Once this spoiling function is known, an inverse problem approach, involving inversion of matrices, can then be used to retrieve the original image. As these matrices are generally ill-conditioned, due to statistical fluctuation and truncation errors, iterative procedures should be applied, such as the Richardson-Lucy algorithm. This algorithm has been applied in this work to unfold radiographic images acquired by transmission of thermal neutrons and gamma-rays. After this procedure, the resulting images undergo an active filtering which fairly improves their final quality at a negligible cost in terms of processing time. The filter ruling the process is based on the matrix of the correction factors for the last iteration of the deconvolution procedure. Synthetic images degraded with a known PSF, and undergone to the same treatment, have been used as benchmark to evaluate the soundness of the developed active filtering procedure. The deconvolution and filtering algorithms have been incorporated to a Fortran program, written to deal with real images, generate the synthetic ones and display both. (author)
International Nuclear Information System (INIS)
Lü, Xiaoshu; Lu, Tao; Kibert, Charles J.; Viljanen, Martti
2015-01-01
Highlights: • This paper presents a new modeling method to forecast energy demands. • The model is based on physical–statistical approach to improving forecast accuracy. • A new method is proposed to address the heterogeneity challenge. • Comparison with measurements shows accurate forecasts of the model. • The first physical–statistical/heterogeneous building energy modeling approach is proposed and validated. - Abstract: Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy
The impact of seasonal signals on spatio-temporal filtering
Gruszczynski, Maciej; Klos, Anna; Bogusz, Janusz
2016-04-01
Existence of Common Mode Errors (CMEs) in permanent GNSS networks contribute to spatial and temporal correlation in residual time series. Time series from permanently observing GNSS stations of distance less than 2 000 km are similarly influenced by such CME sources as: mismodelling (Earth Orientation Parameters - EOP, satellite orbits or antenna phase center variations) during the process of the reference frame realization, large-scale atmospheric and hydrospheric effects as well as small scale crust deformations. Residuals obtained as a result of detrending and deseasonalising of topocentric GNSS time series arranged epoch-by-epoch form an observation matrix independently for each component (North, East, Up). CME is treated as internal structure of the data. Assuming a uniform temporal function across the network it is possible to filter CME out using PCA (Principal Component Analysis) approach. Some of above described CME sources may be reflected as a wide range of frequencies in GPS residual time series. In order to determine an impact of seasonal signals modeling to existence of spatial correlation in network and consequently the results of CME filtration, we chose two ways of modeling. The first approach was commonly presented by previous authors, who modeled with the Least-Squares Estimation (LSE) only annual and semi-annual oscillations. In the second one the set of residuals was a result of modeling of deterministic part that included fortnightly periods plus up to 9th harmonics of Chandlerian, tropical and draconitic oscillations. Correlation coefficients for residuals in parallel with KMO (Kaiser-Meyer-Olkin) statistic and Bartlett's test of sphericity were determined. For this research we used time series expressed in ITRF2008 provided by JPL (Jet Propulsion Laboratory). GPS processing was made using GIPSY-OASIS software in a PPP (Precise Point Positioning) mode. In order to form GPS station network that meet demands of uniform spatial response to the
An Extension to a Filter Implementation of Local Quadratic Surface for Image Noise Estimation
DEFF Research Database (Denmark)
Nielsen, Allan Aasbjerg
1999-01-01
Based on regression analysis this paper gives a description for simple image filter design. Specifically 3x3 filter implementations of a quadratic surface, residuals from this surface, gradients and the Laplacian are given. For the residual a 5x5 filter is given also. It is shown that the 3x3......) it is concluded that if striping is to be considered as a part of the noise, the residual from a 3x3 median filter seems best. If we are interested in a salt-and-pepper noise estimator the proposed extension to the 3x3 filter for the residual from a quadratic surface seems best. Simple statistics...
Kuik, Friderike; Lauer, Axel; von Schneidemesser, Erika; Butler, Tim
2017-04-01
Many European cities continue to struggle with meeting the European air quality limits for NO2. In Berlin, Germany, most of the exceedances in NO2 recorded at monitoring sites near busy roads can be largely attributed to emissions from traffic. In order to assess the impact of changes in traffic emissions on air quality at policy relevant scales, we combine the regional atmosphere-chemistry transport model WRF-Chem at a resolution of 1kmx1km with a statistical downscaling approach. Here, we build on the recently published study evaluating the performance of a WRF-Chem setup in representing observed urban background NO2 concentrations from Kuik et al. (2016) and extend this setup by developing and testing an approach to statistically downscale simulated urban background NO2 concentrations to street level. The approach uses a multilinear regression model to relate roadside NO2 concentrations observed with the municipal monitoring network with observed NO2 concentrations at urban background sites and observed traffic counts. For this, the urban background NO2 concentrations are decomposed into a long term, a synoptic and a diurnal component using the Kolmogorov-Zurbenko filtering method. We estimate the coefficients of the regression model for five different roadside stations in Berlin representing different street types. In a next step we combine the coefficients with simulated urban background concentrations and observed traffic counts, in order to estimate roadside NO2 concentrations based on the results obtained with WRF-Chem at the five selected stations. In a third step, we extrapolate the NO2 concentrations to all major roads in Berlin. The latter is based on available data for Berlin of daily mean traffic counts, diurnal and weekly cycles of traffic as well as simulated urban background NO2 concentrations. We evaluate the NO2 concentrations estimated with this method at street level for Berlin with additional observational data from stationary measurements and
Harrou, Fouzi; Sun, Ying; Taghezouit, Bilal; Saidi, Ahmed; Hamlati, Mohamed-Elkarim
2017-01-01
This study reports the development of an innovative fault detection and diagnosis scheme to monitor the direct current (DC) side of photovoltaic (PV) systems. Towards this end, we propose a statistical approach that exploits the advantages of one
Energy Technology Data Exchange (ETDEWEB)
Piepel, Gregory F.; Matzke, Brett D.; Sego, Landon H.; Amidan, Brett G.
2013-04-27
This report discusses the methodology, formulas, and inputs needed to make characterization and clearance decisions for Bacillus anthracis-contaminated and uncontaminated (or decontaminated) areas using a statistical sampling approach. Specifically, the report includes the methods and formulas for calculating the • number of samples required to achieve a specified confidence in characterization and clearance decisions • confidence in making characterization and clearance decisions for a specified number of samples for two common statistically based environmental sampling approaches. In particular, the report addresses an issue raised by the Government Accountability Office by providing methods and formulas to calculate the confidence that a decision area is uncontaminated (or successfully decontaminated) if all samples collected according to a statistical sampling approach have negative results. Key to addressing this topic is the probability that an individual sample result is a false negative, which is commonly referred to as the false negative rate (FNR). The two statistical sampling approaches currently discussed in this report are 1) hotspot sampling to detect small isolated contaminated locations during the characterization phase, and 2) combined judgment and random (CJR) sampling during the clearance phase. Typically if contamination is widely distributed in a decision area, it will be detectable via judgment sampling during the characterization phrase. Hotspot sampling is appropriate for characterization situations where contamination is not widely distributed and may not be detected by judgment sampling. CJR sampling is appropriate during the clearance phase when it is desired to augment judgment samples with statistical (random) samples. The hotspot and CJR statistical sampling approaches are discussed in the report for four situations: 1. qualitative data (detect and non-detect) when the FNR = 0 or when using statistical sampling methods that account
DEFF Research Database (Denmark)
Ramezani, Malek; Golestan, Saeed; Li, Shuhui
2018-01-01
In recent years, a large number of three-phase phase-locked loops (PLLs) have been developed. One of the most popular ones is the complex coefficient filterbased PLL (CCF-PLL). The CCFs benefit from a sequence selective filtering ability and, hence, enable the CCF-PLL to selectively reject/extract...... disturbances before the PLL control loop while maintaining an acceptable dynamic behavior. The aim of this paper is presenting a simple yet effective approach to enhance the standard CCF-PLL performance without requiring any additional computational load....
Li, S. G.; Shi, L.
2014-10-01
The recommendation system for virtual items in massive multiplayer online role-playing games (MMORPGs) has aroused the interest of researchers. Of the many approaches to construct a recommender system, collaborative filtering (CF) has been the most successful one. However, the traditional CFs just lure customers into the purchasing action and overlook customers' satisfaction, moreover, these techniques always suffer from low accuracy under cold-start conditions. Therefore, a novel collaborative filtering (NCF) method is proposed to identify like-minded customers according to the preference similarity coefficient (PSC), which implies correlation between the similarity of customers' characteristics and the similarity of customers' satisfaction level for the product. Furthermore, the analytic hierarchy process (AHP) is used to determine the relative importance of each characteristic of the customer and the improved ant colony optimisation (IACO) is adopted to generate the expression of the PSC. The IACO creates solutions using the Markov random walk model, which can accelerate the convergence of algorithm and prevent prematurity. For a target customer whose neighbours can be found, the NCF can predict his satisfaction level towards the suggested products and recommend the acceptable ones. Under cold-start conditions, the NCF will generate the recommendation list by excluding items that other customers prefer.
Czech Academy of Sciences Publication Activity Database
Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.
2013-01-01
Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf
A variational Bayesian multiple particle filtering scheme for large-dimensional systems
Ait-El-Fquih, Boujemaa
2016-06-14
This paper considers the Bayesian filtering problem in high-dimensional nonlinear state-space systems. In such systems, classical particle filters (PFs) are impractical due to the prohibitive number of required particles to obtain reasonable performances. One approach that has been introduced to overcome this problem is the concept of multiple PFs (MPFs), where the state-space is split into low-dimensional subspaces and then a separate PF is applied to each subspace. Remarkable performances of MPF-like filters motivated our investigation here into a new strategy that combines the variational Bayesian approach to split the state-space with random sampling techniques, to derive a new computationally efficient MPF. The propagation of each particle in the prediction step of the resulting filter requires generating only a single particle in contrast with standard MPFs, for which a set of (children) particles is required. We present simulation results to evaluate the behavior of the proposed filter and compare its performances against standard PF and a MPF.
A variational Bayesian multiple particle filtering scheme for large-dimensional systems
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim
2016-01-01
This paper considers the Bayesian filtering problem in high-dimensional nonlinear state-space systems. In such systems, classical particle filters (PFs) are impractical due to the prohibitive number of required particles to obtain reasonable performances. One approach that has been introduced to overcome this problem is the concept of multiple PFs (MPFs), where the state-space is split into low-dimensional subspaces and then a separate PF is applied to each subspace. Remarkable performances of MPF-like filters motivated our investigation here into a new strategy that combines the variational Bayesian approach to split the state-space with random sampling techniques, to derive a new computationally efficient MPF. The propagation of each particle in the prediction step of the resulting filter requires generating only a single particle in contrast with standard MPFs, for which a set of (children) particles is required. We present simulation results to evaluate the behavior of the proposed filter and compare its performances against standard PF and a MPF.
CSIR Research Space (South Africa)
Ntaka, L
2013-08-01
Full Text Available . In this work, statistical inference approach specifically the non-parametric bootstrapping and linear model were applied. Data used to develop the model were sourced from the literature. 104 data points with information on aggregation, natural organic matter...
Tsutsumi, Morito; Seya, Hajime
2009-12-01
This study discusses the theoretical foundation of the application of spatial hedonic approaches—the hedonic approach employing spatial econometrics or/and spatial statistics—to benefits evaluation. The study highlights the limitations of the spatial econometrics approach since it uses a spatial weight matrix that is not employed by the spatial statistics approach. Further, the study presents empirical analyses by applying the Spatial Autoregressive Error Model (SAEM), which is based on the spatial econometrics approach, and the Spatial Process Model (SPM), which is based on the spatial statistics approach. SPMs are conducted based on both isotropy and anisotropy and applied to different mesh sizes. The empirical analysis reveals that the estimated benefits are quite different, especially between isotropic and anisotropic SPM and between isotropic SPM and SAEM; the estimated benefits are similar for SAEM and anisotropic SPM. The study demonstrates that the mesh size does not affect the estimated amount of benefits. Finally, the study provides a confidence interval for the estimated benefits and raises an issue with regard to benefit evaluation.
Filter assembly for metallic and intermetallic tube filters
Alvin, Mary Anne; Lippert, Thomas E.; Bruck, Gerald J.; Smeltzer, Eugene E.
2001-01-01
A filter assembly (60) for holding a filter element (28) within a hot gas cleanup system pressure vessel is provided, containing: a filter housing (62), said filter housing having a certain axial length and having a peripheral sidewall, said sidewall defining an interior chamber (66); a one piece, all metal, fail-safe/regenerator device (68) within the interior chamber (66) of the filter housing (62) and/or extending beyond the axial length of the filter housing, said device containing an outward extending radial flange (71) within the filter housing for seating an essential seal (70), the device also having heat transfer media (72) disposed inside and screens (80) for particulate removal; one compliant gasket (70) positioned next to and above the outward extending radial flange of the fail-safe/regenerator device; and a porous metallic corrosion resistant superalloy type filter element body welded at the bottom of the metal fail-safe/regenerator device.
Kalman filter to update forest cover estimates
Raymond L. Czaplewski
1990-01-01
The Kalman filter is a statistical estimator that combines a time-series of independent estimates, using a prediction model that describes expected changes in the state of a system over time. An expensive inventory can be updated using model predictions that are adjusted with more recent, but less expensive and precise, monitoring data. The concepts of the Kalman...
An adaptive Kalman filter for speckle reductions in ultrasound images
International Nuclear Information System (INIS)
Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.
1988-01-01
Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction
Dynamics of Electricity Demand in Lesotho: A Kalman Filter Approach
Directory of Open Access Journals (Sweden)
Thamae Retselisitsoe Isaiah
2015-04-01
Full Text Available This study provides an empirical analysis of the time-varying price and income elasticities of electricity demand in Lesotho for the period 1995-2012 using the Kalman filter approach. The results reveal that economic growth has been one of the main drivers of electricity consumption in Lesotho while electricity prices are found to play a less significant role since they are monopoly-driven and relatively low when compared to international standards. These findings imply that increases in electricity prices in Lesotho might not have a significant impact on consumption in the short-run. However, if the real electricity prices become too high over time, consumers might change their behavior and sensitivity to price and hence, energy policymakers will need to reconsider their impact in the long-run. Furthermore, several exogenous shocks seem to have affected the sensitivity of electricity demand during the period prior to regulation, which made individuals, businesses and agencies to be more sensitive to electricity costs. On the other hand, the period after regulation has been characterized by more stable and declining sensitivity of electricity demand. Therefore, factors such as regulation and changes in the country’s economic activities appear to have affected both price and income elasticities of electricity demand in Lesotho.
Filter material charging apparatus for filter assembly for radioactive contaminants
International Nuclear Information System (INIS)
Goldsmith, J.M.; O'Nan, A. Jr.
1977-01-01
A filter charging apparatus for a filter assembly is described. The filter assembly includes a housing with at least one filter bed therein and the filter charging apparatus for adding filter material to the filter assembly includes a tank with an opening therein, the tank opening being disposed in flow communication with opposed first and second conduit means, the first conduit means being in flow communication with the filter assembly housing and the second conduit means being in flow communication with a blower means. Upon activation of the blower means, the blower means pneumatically conveys the filter material from the tank to the filter housing
The statistical-inference approach to generalized thermodynamics
International Nuclear Information System (INIS)
Lavenda, B.H.; Scherer, C.
1987-01-01
Limit theorems, such as the central-limit theorem and the weak law of large numbers, are applicable to statistical thermodynamics for sufficiently large sample size of indipendent and identically distributed observations performed on extensive thermodynamic (chance) variables. The estimation of the intensive thermodynamic quantities is a problem in parametric statistical estimation. The normal approximation to the Gibbs' distribution is justified by the analysis of large deviations. Statistical thermodynamics is generalized to include the statistical estimation of variance as well as mean values
Strategists and Non-Strategists in Austrian Enterprises—Statistical Approaches
Duller, Christine
2011-09-01
The purpose of this work is to determine with a modern statistical approach which variables can indicate whether an arbitrary enterprise uses strategic management as basic business concept. "Strategic management is an ongoing process that evaluates and controls the business and the industries in which the company is involved; assesses its competitors and sets goals and strategies to meet all existing and potential competitors; and then reassesses each strategy annually or quarterly (i.e. regularly) to determine how it has been implemented and whether it has succeeded or needs replacement by a new strategy to meet changed circumstances, new technology, new competitors, a new economic environment or a new social, financial or political environment." [12] In Austria 70% to 80% of all enterprises can be classified as family firms. In literature the empirically untested hypothesis can be found that family firms tend to have less formalised management accounting systems than non-family enterprises. But it is unknown whether the use of strategic management accounting systems is influenced more by the fact of structure (family or non-family enterprise) or by the effect of size (number of employees). Therefore, the goal is to split up enterprises into two subgroups, namely strategists and non-strategists and to get information on the variables of influence (size, structure, branches, etc.). Two statistical approaches are used: On the one hand a classical cluster analysis is implemented to design two subgroups and on the other hand a latent class model is built up for this problem. After a description of the theoretical background first results of both strategies are compared.
Multirate Filter Bank Representations of RS and BCH Codes
Directory of Open Access Journals (Sweden)
Van Meerbergen Geert
2008-01-01
Full Text Available Abstract This paper addresses the use of multirate filter banks in the context of error-correction coding. An in-depth study of these filter banks is presented, motivated by earlier results and applications based on the filter bank representation of Reed-Solomon (RS codes, such as Soft-In Soft-Out RS-decoding or RS-OFDM. The specific structure of the filter banks (critical subsampling is an important aspect in these applications. The goal of the paper is twofold. First, the filter bank representation of RS codes is now explained based on polynomial descriptions. This approach allows us to gain new insight in the correspondence between RS codes and filter banks. More specifically, it allows us to show that the inherent periodically time-varying character of a critically subsampled filter bank matches remarkably well with the cyclic properties of RS codes. Secondly, an extension of these techniques toward the more general class of BCH codes is presented. It is demonstrated that a BCH code can be decomposed into a sum of critically subsampled filter banks.
Multirate Filter Bank Representations of RS and BCH Codes
Directory of Open Access Journals (Sweden)
Marc Moonen
2009-01-01
Full Text Available This paper addresses the use of multirate filter banks in the context of error-correction coding. An in-depth study of these filter banks is presented, motivated by earlier results and applications based on the filter bank representation of Reed-Solomon (RS codes, such as Soft-In Soft-Out RS-decoding or RS-OFDM. The specific structure of the filter banks (critical subsampling is an important aspect in these applications. The goal of the paper is twofold. First, the filter bank representation of RS codes is now explained based on polynomial descriptions. This approach allows us to gain new insight in the correspondence between RS codes and filter banks. More specifically, it allows us to show that the inherent periodically time-varying character of a critically subsampled filter bank matches remarkably well with the cyclic properties of RS codes. Secondly, an extension of these techniques toward the more general class of BCH codes is presented. It is demonstrated that a BCH code can be decomposed into a sum of critically subsampled filter banks.
A Formal Approach for RT-DVS Algorithms Evaluation Based on Statistical Model Checking
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Shengxin Dai
2015-01-01
Full Text Available Energy saving is a crucial concern in embedded real time systems. Many RT-DVS algorithms have been proposed to save energy while preserving deadline guarantees. This paper presents a novel approach to evaluate RT-DVS algorithms using statistical model checking. A scalable framework is proposed for RT-DVS algorithms evaluation, in which the relevant components are modeled as stochastic timed automata, and the evaluation metrics including utilization bound, energy efficiency, battery awareness, and temperature awareness are expressed as statistical queries. Evaluation of these metrics is performed by verifying the corresponding queries using UPPAAL-SMC and analyzing the statistical information provided by the tool. We demonstrate the applicability of our framework via a case study of five classical RT-DVS algorithms.
Theoretical approaches to the steady-state statistical physics of interacting dissipative units
Bertin, Eric
2017-02-01
The aim of this review is to provide a concise overview of some of the generic approaches that have been developed to deal with the statistical description of large systems of interacting dissipative ‘units’. The latter notion includes, e.g. inelastic grains, active or self-propelled particles, bubbles in a foam, low-dimensional dynamical systems like driven oscillators, or even spatially extended modes like Fourier modes of the velocity field in a fluid. We first review methods based on the statistical properties of a single unit, starting with elementary mean-field approximations, either static or dynamic, that describe a unit embedded in a ‘self-consistent’ environment. We then discuss how this basic mean-field approach can be extended to account for spatial dependences, in the form of space-dependent mean-field Fokker-Planck equations, for example. We also briefly review the use of kinetic theory in the framework of the Boltzmann equation, which is an appropriate description for dilute systems. We then turn to descriptions in terms of the full N-body distribution, starting from exact solutions of one-dimensional models, using a matrix-product ansatz method when correlations are present. Since exactly solvable models are scarce, we also present some approximation methods which can be used to determine the N-body distribution in a large system of dissipative units. These methods include the Edwards approach for dense granular matter and the approximate treatment of multiparticle Langevin equations with colored noise, which models systems of self-propelled particles. Throughout this review, emphasis is put on methodological aspects of the statistical modeling and on formal similarities between different physical problems, rather than on the specific behavior of a given system.
Winery wastewater treatment using the land filter technique.
Christen, E W; Quayle, W C; Marcoux, M A; Arienzo, M; Jayawardane, N S
2010-08-01
This study outlines a new approach to the treatment of winery wastewater by application to a land FILTER (Filtration and Irrigated cropping for Land Treatment and Effluent Reuse) system. The land FILTER system was tested at a medium size rural winery crushing approximately 20,000 tonnes of grapes. The approach consisted of a preliminary treatment through a coarse screening and settling in treatment ponds, followed by application to the land FILTER planted to pasture. The land FILTER system efficiently dealt with variable volumes and nutrient loads in the wastewater. It was operated to minimize pollutant loads in the treated water (subsurface drainage) and provide adequate leaching to manage salt in the soil profile. The land FILTER system was effective in neutralizing the pH of the wastewater and removing nutrient pollutants to meet EPA discharge limits. However, suspended solids (SS) and biological oxygen demand (BOD) levels in the subsurface drainage waters slightly exceeded EPA limits for discharge. The high organic content in the wastewater initially caused some soil blockage and impeded drainage in the land FILTER site. This was addressed by reducing the hydraulic loading rate to allow increased soil drying between wastewater irrigations. The analysis of soil characteristics after the application of wastewater found that there was some potassium accumulation in the profile but sodium and nutrients decreased after wastewater application. Thus, the wastewater application and provision of subsurface drainage ensured adequate leaching, and so was adequate to avoid the risk of soil salinisation. Crown Copyright 2010. Published by Elsevier Ltd. All rights reserved.
A statistical-based approach for fault detection and diagnosis in a photovoltaic system
Garoudja, Elyes; Harrou, Fouzi; Sun, Ying; Kara, Kamel; Chouder, Aissa; Silvestre, Santiago
2017-01-01
This paper reports a development of a statistical approach for fault detection and diagnosis in a PV system. Specifically, the overarching goal of this work is to early detect and identify faults on the DC side of a PV system (e.g., short
Do blue-light filtering intraocular lenses affect visual function?
Lavric, Alenka; Pompe, Manca Tekavcic
2014-11-01
To study different aspects of visual function, macular changes, and subjective differences between the eye with an ultraviolet (UV) and blue-light filtering intraocular lens (IOL) and the fellow eye with a UV-light filtering IOL. Thirty patients (60 eyes) with senile cataract had both cataracts extracted, and an IOL was implanted at least 2 years before clinical evaluation. In one eye, AcrySof SA60AT (a UV-light filtering IOL) was implanted, whereas in the contralateral eye, AcrySof IQ SN60WF (a blue-light filtering IOL) was implanted. Each patient underwent visual acuity testing, color vision testing (Ishihara and Farnsworth-Munsell 100-hue tests), and contrast sensitivity (CS) testing. The macula was evaluated with optical coherence tomography and with clinical examination. Patients were asked if they noted any difference between the implanted IOLs concerning visual impression. Subjective visual quality was evaluated using the National Eye Institute Visual Functioning Questionnaire. There was a borderline statistically significant difference in the mean best-corrected visual acuity (p = 0.05). As regards color vision, no significant changes in Ishihara and Farnsworth-Munsell 100-hue error scores were detected between both eyes (p = 0.48 and p = 0.59, respectively). Analysis of CS showed no significant difference between the groups at any spatial frequency. There were also no statistically significant differences in central macular thickness and total macular volume between the two IOL groups (p = 0.72 and p = 0.61, respectively). In both IOL groups, three eyes developed an epiretinal membrane, and six eyes developed early signs of age-related macular degeneration. This study showed no significant effects of a blue-light filtering IOL on visual acuity and no influence on color perception and CS. After more than 2 years, there were no significant differences in macular changes between the IOL groups. Clinical evidence of the effect of a blue-light filtering IOL on
Flow Equation Approach to the Statistics of Nonlinear Dynamical Systems
Marston, J. B.; Hastings, M. B.
2005-03-01
The probability distribution function of non-linear dynamical systems is governed by a linear framework that resembles quantum many-body theory, in which stochastic forcing and/or averaging over initial conditions play the role of non-zero . Besides the well-known Fokker-Planck approach, there is a related Hopf functional methodootnotetextUriel Frisch, Turbulence: The Legacy of A. N. Kolmogorov (Cambridge University Press, 1995) chapter 9.5.; in both formalisms, zero modes of linear operators describe the stationary non-equilibrium statistics. To access the statistics, we investigate the method of continuous unitary transformationsootnotetextS. D. Glazek and K. G. Wilson, Phys. Rev. D 48, 5863 (1993); Phys. Rev. D 49, 4214 (1994). (also known as the flow equation approachootnotetextF. Wegner, Ann. Phys. 3, 77 (1994).), suitably generalized to the diagonalization of non-Hermitian matrices. Comparison to the more traditional cumulant expansion method is illustrated with low-dimensional attractors. The treatment of high-dimensional dynamical systems is also discussed.
Subband Approach to Bandlimited Crosstalk Cancellation System in Spatial Sound Reproduction
Bai, Mingsian R.; Lee, Chih-Chung
2006-12-01
Crosstalk cancellation system (CCS) plays a vital role in spatial sound reproduction using multichannel loudspeakers. However, this technique is still not of full-blown use in practical applications due to heavy computation loading. To reduce the computation loading, a bandlimited CCS is presented in this paper on the basis of subband filtering approach. A pseudoquadrature mirror filter (QMF) bank is employed in the implementation of CCS filters which are bandlimited to 6 kHz, where human's localization is the most sensitive. In addition, a frequency-dependent regularization scheme is adopted in designing the CCS inverse filters. To justify the proposed system, subjective listening experiments were undertaken in an anechoic room. The experiments include two parts: the source localization test and the sound quality test. Analysis of variance (ANOVA) is applied to process the data and assess statistical significance of subjective experiments. The results indicate that the bandlimited CCS performed comparably well as the fullband CCS, whereas the computation loading was reduced by approximately eighty percent.
International Nuclear Information System (INIS)
Gullberg, G.T.; Budinger, T.F.
1981-01-01
A back projection of filtered projection (BKFIL) reconstruction algorithm is presented that is applicable to single-photon emission computed tomography (ECT) in the presence of a constant attenuating medium such as the brain. The filters used in transmission computed tomography (TCT)-comprised of a ramp multiplied by window functions-are modified so that the single-photon ECT filter is a function of the constant attenuation coefficient. The filters give good reconstruction results with sufficient angular and lateral sampling. With continuous samples the BKFIL algorithm has a point spread function that is the Hankel transform of the window function. The resolution and statistical properties of the filters are demonstrated by various simulations which assume an ideal detector response. Statistical formulas for the reconstructed image show that the square of the percent-root-mean-square (percent-rms) uncertainty of the reconstruction is inversely proportional to the total measured counts. The results indicate that constant attenuation can be compensated for by using an attenuation-dependent filter that reconstructs the transverse section reliably. Computer time requirements are two times that of conventional TCT or positron ECT and there is no increase in memory requirements
The new RERO Statistics services
CERN. Geneva
2015-01-01
More and more RERO professionals are asking for statistics on the use of the RERO DOC server. They want to know, for a certain period, what are the most consulted records, which files are more uploaded, which collections are more popular, etc. They also want to customize results by selecting multiple filters, such as: what are the document types most seen for the University of Fribourg from January to March 2014 in Economics. Using recent tools such as Elasticsearch, AngularJS, Highcharts, etc., we decided to develop a new specific interface using the server logs and the Invenio API. One of the most challenging aspect was how to filter internet bots (crawlers, robots), especially those appearing as humans.
Shanmugavadivu, P.; Eliahim Jeevaraj, P. S.
2014-06-01
The Adaptive Iterated Functions Systems (AIFS) Filter presented in this paper has an outstanding potential to attenuate the fixed-value impulse noise in images. This filter has two distinct phases namely noise detection and noise correction which uses Measure of Statistics and Iterated Function Systems (IFS) respectively. The performance of AIFS filter is assessed by three metrics namely, Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index Matrix (MSSIM) and Human Visual Perception (HVP). The quantitative measures PSNR and MSSIM endorse the merit of this filter in terms of degree of noise suppression and details/edge preservation respectively, in comparison with the high performing filters reported in the recent literature. The qualitative measure HVP confirms the noise suppression ability of the devised filter. This computationally simple noise filter broadly finds application wherein the images are highly degraded by fixed-value impulse noise.
Tornadoes and related damage costs: statistical modelling with a semi-Markov approach
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Guglielmo D’Amico
2016-09-01
Full Text Available We propose a statistical approach to modelling for predicting and simulating occurrences of tornadoes and accumulated cost distributions over a time interval. This is achieved by modelling the tornado intensity, measured with the Fujita scale, as a stochastic process. Since the Fujita scale divides tornado intensity into six states, it is possible to model the tornado intensity by using Markov and semi-Markov models. We demonstrate that the semi-Markov approach is able to reproduce the duration effect that is detected in tornado occurrence. The superiority of the semi-Markov model as compared to the Markov chain model is also affirmed by means of a statistical test of hypothesis. As an application, we compute the expected value and the variance of the costs generated by the tornadoes over a given time interval in a given area. The paper contributes to the literature by demonstrating that semi-Markov models represent an effective tool for physical analysis of tornadoes as well as for the estimation of the economic damages to human things.
APPLYING OF COLLABORATIVE FILTERING ALGORITHM FOR PROCESSING OF MEDICAL DATA
Directory of Open Access Journals (Sweden)
Карина Владимировна МЕЛЬНИК
2015-05-01
Full Text Available The problem of improving of effectiveness of medical facility for implementation of social project is considered. There are different approaches to solve this problem, some of which require additional funding, which is usually absent. Therefore, it was proposed to use the approach of processing and application of patients’ data from medical records. The selection of a representative sample of patients was carried out using the technique of collaborative filtering. Review of the methods of collaborative filtering is performed, which showed that there are three main groups of methods. The first group calculates various measures of similarity between the object. The second group is data mining techniques. The third group of methods is a hybrid approach. The Gower coefficient for calculation of similarity measure of medical records of patients is considered in the article. A model of risk assessment of diseases based on collaborative filtering techniques is developed.
Radiocarbon data collection, filtering and analysis at the NRL TEAMS facility
International Nuclear Information System (INIS)
Tumey, S.J.; Grabowski, K.S.; Knies, D.L.; Mignerey, A.C.
2004-01-01
This report describes a novel approach to collection and analysis of radiocarbon data due to the unique design of the Naval Research Laboratory Trace Element Accelerator Mass Spectrometry system. In this approach, targets are loaded on the cathode wheel such that samples are clustered in groups of five between standards. Each target is measured until the external error asymptotically reaches a constant value and the internal error dominates, or until a predefined time limit is reached. These measurements are repeated until the desired level of counting statistics is attained. Cycle control software saves the measured beam currents and count rate, as well as all system parameters to disc at fixed intervals throughout each measurement. Data visualization software has aided in system diagnosis by exposing relationships between the measured isotope ratios and system parameters. A filtering algorithm is employed to the data set of each target measurement in an attempt to achieve a Gaussian distribution. Final results are generated by a radiocarbon calculator that allows a user to select which target measurements to treat as samples, standards and blanks, and calculates the desired values (i.e. conventional radiocarbon age, percent modern carbon, etc.) with full error propagation. Currently, a Microsoft Access [reg] relational database is being developed which will be integrated into the existing LabVIEW [reg] control, filtering and calculation programs in order to streamline the process from sample submission to report generation, as well as improve quality control
A statistical approach to the prediction of pressure tube fracture toughness
International Nuclear Information System (INIS)
Pandey, M.D.; Radford, D.D.
2008-01-01
The fracture toughness of the zirconium alloy (Zr-2.5Nb) is an important parameter in determining the flaw tolerance for operation of pressure tubes in a nuclear reactor. Fracture toughness data have been generated by performing rising pressure burst tests on sections of pressure tubes removed from operating reactors. The test data were used to generate a lower-bound fracture toughness curve, which is used in defining the operational limits of pressure tubes. The paper presents a comprehensive statistical analysis of burst test data and develops a multivariate statistical model to relate toughness with material chemistry, mechanical properties, and operational history. The proposed model can be useful in predicting fracture toughness of specific in-service pressure tubes, thereby minimizing conservatism associated with a generic lower-bound approach
Naik, Naren; Vasu, R M; Ananthasayanam, M R
2010-02-20
The problem of reconstruction of a refractive-index distribution (RID) in optical refraction tomography (ORT) with optical path-length difference (OPD) data is solved using two adaptive-estimation-based extended-Kalman-filter (EKF) approaches. First, a basic single-resolution EKF (SR-EKF) is applied to a state variable model describing the tomographic process, to estimate the RID of an optically transparent refracting object from noisy OPD data. The initialization of the biases and covariances corresponding to the state and measurement noise is discussed. The state and measurement noise biases and covariances are adaptively estimated. An EKF is then applied to the wavelet-transformed state variable model to yield a wavelet-based multiresolution EKF (MR-EKF) solution approach. To numerically validate the adaptive EKF approaches, we evaluate them with benchmark studies of standard stationary cases, where comparative results with commonly used efficient deterministic approaches can be obtained. Detailed reconstruction studies for the SR-EKF and two versions of the MR-EKF (with Haar and Daubechies-4 wavelets) compare well with those obtained from a typically used variant of the (deterministic) algebraic reconstruction technique, the average correction per projection method, thus establishing the capability of the EKF for ORT. To the best of our knowledge, the present work contains unique reconstruction studies encompassing the use of EKF for ORT in single-resolution and multiresolution formulations, and also in the use of adaptive estimation of the EKF's noise covariances.
The Need for Anticoagulation Following Inferior Vena Cava Filter Placement: Systematic Review
International Nuclear Information System (INIS)
Ray, Charles E.; Prochazka, Allan
2008-01-01
Purpose. To perform a systemic review to determine the effect of anticoagulation on the rates of venous thromboembolism (pulmonary embolus, deep venous thrombosis, inferior vena cava (IVC) filter thrombosis) following placement of an IVC filter. Methods. A comprehensive computerized literature search was performed to identify relevant articles. Data were abstracted by two reviewers. Studies were included if it could be determined whether or not subjects received anticoagulation following filter placement, and if follow-up data were presented. A meta-analysis of patients from all included studies was performed. A total of 14 articles were included in the final analysis, but the data from only nine articles could be used in the meta-analysis; five studies were excluded because they did not present raw data which could be analyzed in the meta-analysis. A total of 1,369 subjects were included in the final meta-analysis. Results. The summary odds ratio for the effect of anticoagulation on venous thromboembolism rates following filter deployment was 0.639 (95% CI 0.351 to 1.159, p = 0.141). There was significant heterogeneity in the results from different studies [Q statistic of 15.95 (p = 0.043)]. Following the meta-analysis, there was a trend toward decreased venous thromboembolism rates in patients with post-filter anticoagulation (12.3% vs. 15.8%), but the result failed to reach statistical significance. Conclusion. Inferior vena cava filters can be placed in patients who cannot receive concomitant anticoagulation without placing them at significantly higher risk of development of venous thromboembolism
Topology for Statistical Modeling of Petascale Data
Energy Technology Data Exchange (ETDEWEB)
Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Levine, Joshua [Univ. of Utah, Salt Lake City, UT (United States); Gyulassy, Attila [Univ. of Utah, Salt Lake City, UT (United States); Bremer, P. -T. [Univ. of Utah, Salt Lake City, UT (United States)
2013-10-31
Many commonly used algorithms for mathematical analysis do not scale well enough to accommodate the size or complexity of petascale data produced by computational simulations. The primary goal of this project is to develop new mathematical tools that address both the petascale size and uncertain nature of current data. At a high level, the approach of the entire team involving all three institutions is based on the complementary techniques of combinatorial topology and statistical modelling. In particular, we use combinatorial topology to filter out spurious data that would otherwise skew statistical modelling techniques, and we employ advanced algorithms from algebraic statistics to efficiently find globally optimal fits to statistical models. The overall technical contributions can be divided loosely into three categories: (1) advances in the field of combinatorial topology, (2) advances in statistical modelling, and (3) new integrated topological and statistical methods. Roughly speaking, the division of labor between our 3 groups (Sandia Labs in Livermore, Texas A&M in College Station, and U Utah in Salt Lake City) is as follows: the Sandia group focuses on statistical methods and their formulation in algebraic terms, and finds the application problems (and data sets) most relevant to this project, the Texas A&M Group develops new algebraic geometry algorithms, in particular with fewnomial theory, and the Utah group develops new algorithms in computational topology via Discrete Morse Theory. However, we hasten to point out that our three groups stay in tight contact via videconference every 2 weeks, so there is much synergy of ideas between the groups. The following of this document is focused on the contributions that had grater direct involvement from the team at the University of Utah in Salt Lake City.