Neuromuscular fiber segmentation through particle filtering and discrete optimization
NASA Astrophysics Data System (ADS)
Dietenbeck, Thomas; Varray, François; Kybic, Jan; Basset, Olivier; Cachard, Christian
2014-03-01
We present an algorithm to segment a set of parallel, intertwined and bifurcating fibers from 3D images, targeted for the identification of neuronal fibers in very large sets of 3D confocal microscopy images. The method consists of preprocessing, local calculation of fiber probabilities, seed detection, tracking by particle filtering, global supervised seed clustering and final voxel segmentation. The preprocessing uses a novel random local probability filtering (RLPF). The fiber probabilities computation is performed by means of SVM using steerable filters and the RLPF outputs as features. The global segmentation is solved by discrete optimization. The combination of global and local approaches makes the segmentation robust, yet the individual data blocks can be processed sequentially, limiting memory consumption. The method is automatic but efficient manual interactions are possible if needed. The method is validated on the Neuromuscular Projection Fibers dataset from the Diadem Challenge. On the 15 first blocks present, our method has a 99.4% detection rate. We also compare our segmentation results to a state-of-the-art method. On average, the performances of our method are either higher or equivalent to that of the state-of-the-art method but less user interactions is needed in our approach.
Optimizing Parameters of Process-Based Terrestrial Ecosystem Model with Particle Filter
NASA Astrophysics Data System (ADS)
Ito, A.
2014-12-01
Present terrestrial ecosystem models still contain substantial uncertainties, as model intercomparison studies have shown, because of poor model constraint by observational data. So, development of advanced methodology of data-model fusion, or data-assimilation, is an important task to reduce the uncertainties and improve model predictability. In this study, I apply the Particle filter (or Sequential Monte Carlo filer) to optimize parameters of a process-based terrestrial ecosystem model (VISIT). The Particle filter is one of the data-assimilation methods, in which probability distribution of model state is approximated by many samples of parameter set (i.e., particle). This is a computationally intensive method and applicable to nonlinear systems; this is an advantage of the method in comparison with other techniques like Ensemble Kalman filter and variational method. At several sites, I used flux measurement data of atmosphere-ecosystem CO2 exchange in sequential and non-sequential manners. In the sequential data assimilation, a time-series data at 30-min or daily steps were used to optimize gas-exchange-related parameters; this method would be also effective to assimilate satellite observational data. On the other hand, in the non-sequential case, annual or long-term mean budget was adjusted to observations; this method would be also effective to assimilate carbon stock data. Although there remain technical issues (e.g., appropriate number of particles and likelihood function), I demonstrate that the Partile filter is an effective method of data-assimilation for process-based models, enhancing collaboration between field and model researchers.
Structural notch filter optimization
Felton, R.; Burge, S.; Bradshaw, A.
1995-09-01
A modified algorithm for nonlinear constrained optimization of structural mode filters for an aeroelastic aircraft model is presented. The optimizer set-up and control is implemented in a MATLAB{trademark} graphical user interface environment. It is shown that the modified algorithm gives improved performance over existing nonlinear constrained optimization methods.
Research on improved mechanism for particle filter
NASA Astrophysics Data System (ADS)
Yu, Jinxia; Xu, Jingmin; Tang, Yongli; Zhao, Qian
2013-03-01
Based on the analysis of particle filter algorithm, two improved mechanism are studied so as to improve the performance of particle filter. Firstly, hybrid proposal distribution with annealing parameter is studied in order to use current information of the latest observed measurement to optimize particle filter. Then, resampling step in particle filter is improved by two methods which are based on partial stratified resampling (PSR). One is that it uses the optimal idea to improve the weights after implementing PSR, and the other is that it uses the optimal idea to improve the weights before implementing PSR and uses adaptive mutation operation for all particles so as to assure the diversity of particle sets after PSR. At last, the simulations based on single object tracking are implemented, and the performances of the improved mechanism for particle filter are estimated.
NASA Astrophysics Data System (ADS)
Stevens, Mark R.; Gutchess, Dan; Checka, Neal; Snorrason, MagnÃºs
2006-05-01
Image exploitation algorithms for Intelligence, Surveillance and Reconnaissance (ISR) and weapon systems are extremely sensitive to differences between the operating conditions (OCs) under which they are trained and the extended operating conditions (EOCs) in which the fielded algorithms are tested. As an example, terrain type is an important OC for the problem of tracking hostile vehicles from an airborne camera. A system designed to track cars driving on highways and on major city streets would probably not do well in the EOC of parking lots because of the very different dynamics. In this paper, we present a system we call ALPS for Adaptive Learning in Particle Systems. ALPS takes as input a sequence of video images and produces labeled tracks. The system detects moving targets and tracks those targets across multiple frames using a multiple hypothesis tracker (MHT) tightly coupled with a particle filter. This tracker exploits the strengths of traditional MHT based tracking algorithms by directly incorporating tree-based hypothesis considerations into the particle filter update and resampling steps. We demonstrate results in a parking lot domain tracking objects through occlusions and object interactions.
Optimal filtering and filter stability of linear stochastic delay systems
NASA Technical Reports Server (NTRS)
Kwong, R. H.-S.; Willsky, A. S.
1977-01-01
Optimal filtering equations are obtained for very general linear stochastic delay systems. Stability of the optimal filter is studied in the case where there are no delays in the observations. Using the duality between linear filtering and control, asymptotic stability of the optimal filter is proved. Finally, the cascade of the optimal filter and the deterministic optimal quadratic control system is shown to be asymptotically stable as well.
Optimization of integrated polarization filters.
Gagnon, Denis; Dumont, Joey; DÃ©ziel, Jean-Luc; DubÃ©, Louis J
2014-10-01
This study reports on the design of small footprint, integrated polarization filters based on engineered photonic lattices. Using a rods-in-air lattice as a basis for a TE filter and a holes-in-slab lattice for the analogous TM filter, we are able to maximize the degree of polarization of the output beams up to 98% with a transmission efficiency greater than 75%. The proposed designs allow not only for logical polarization filtering, but can also be tailored to output an arbitrary transverse beam profile. The lattice configurations are found using a recently proposed parallel tabu search algorithm for combinatorial optimization problems in integrated photonics. PMID:25360980
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; G.J. Bruck; M.A. Alvin; T.E. Lippert
1998-04-30
Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and by design issues, such as cantilevered filter elements damaged by ash bridging, or excessively close packing of filtering surfaces resulting in unacceptable pressure drop or filtering surface plugging. This test experience has focused the issues and has helped to define advanced hot gas filter design concepts that offer higher reliability. Westinghouse has identified two advanced ceramic barrier filter concepts that are configured to minimize the possibility of ash bridge formation and to be robust against ash bridges should they occur. The ''inverted candle filter system'' uses arrays of thin-walled, ceramic candle-type filter elements with inside-surface filtering, and contains the filter elements in metal enclosures for complete separation from ash bridges. The ''sheet filter system'' uses ceramic, flat plate filter elements supported from vertical pipe-header arrays that provide geometry that avoids the buildup of ash bridges and allows free fall of the back-pulse released filter cake. The Optimization of Advanced Filter Systems program is being conducted to evaluate these two advanced designs and to ultimately demonstrate one of the concepts in pilot scale. In the Base Contract program, the subject of this report, Westinghouse has developed conceptual designs of the two advanced ceramic barrier filter systems to assess their performance, availability and cost potential, and to identify technical issues that may hinder the commercialization of the technologies. A plan for the Option I, bench-scale test program has also been developed based on the issues identified. The two advanced barrier filter systems have been found to have the potential to be significantly more reliable and less expensive to operate than standard ceramic candle filter system designs. Their key development requirements are the assessment of the design and manufacturing feasibility of the ceramic filter elements, and the small-scale demonstration of their conceptual reliability and availability merits.
NASA Technical Reports Server (NTRS)
Venter, Gerhard; Sobieszczanski-Sobieski Jaroslaw
2002-01-01
The purpose of this paper is to show how the search algorithm known as particle swarm optimization performs. Here, particle swarm optimization is applied to structural design problems, but the method has a much wider range of possible applications. The paper's new contributions are improvements to the particle swarm optimization algorithm and conclusions and recommendations as to the utility of the algorithm, Results of numerical experiments for both continuous and discrete applications are presented in the paper. The results indicate that the particle swarm optimization algorithm does locate the constrained minimum design in continuous applications with very good precision, albeit at a much higher computational cost than that of a typical gradient based optimizer. However, the true potential of particle swarm optimization is primarily in applications with discrete and/or discontinuous functions and variables. Additionally, particle swarm optimization has the potential of efficient computation with very large numbers of concurrently operating processors.
Particle flow for nonlinear filters with log-homotopy
NASA Astrophysics Data System (ADS)
Daum, Fred; Huang, Jim
2008-04-01
We describe a new nonlinear filter that is vastly superior to the classic particle filter. In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particle filter with optimal estimation accuracy for problems with dimension greater than 2 or 3. We consider nonlinear estimation problems with dimensions varying from 1 to 20 that are smooth and fully coupled (i.e. dense not sparse). The new filter implements Bayes' rule using particle flow rather than with a pointwise multiplication of two functions; this avoids one of the fundamental and well known problems in particle filters, namely "particle collapse" as a result of Bayes' rule. We use a log-homotopy to derive the ODE that describes particle flow. This paper was written for normal engineers, who do not have homotopy for breakfast.
Distributed SLAM Using Improved Particle Filter for Mobile Robot Localization
Pei, Fujun; Wu, Mei; Zhang, Simin
2014-01-01
The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particle filter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particle filter, especially in SLAM problem that involves a large number of dimensions. In this paper, particle filter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particle filter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particle filter. Second, an information fusion method was proposed by mixing the innovation method and the number of effective particles method, which combined the advantages of these two methods. And this paper extends the previously known convergence results for particle filter to prove that improved particle filter converges to the optimal filter in mean square as the number of particles goes to infinity. The experiment results show that the proposed algorithm improved the virtue of the DPF-SLAM system in isolate faults and enabled the system to have a better tolerance and robustness. PMID:24883362
Particle Swarm Optimization Toolbox
NASA Technical Reports Server (NTRS)
Grant, Michael J.
2010-01-01
The Particle Swarm Optimization Toolbox is a library of evolutionary optimization tools developed in the MATLAB environment. The algorithms contained in the library include a genetic algorithm (GA), a single-objective particle swarm optimizer (SOPSO), and a multi-objective particle swarm optimizer (MOPSO). Development focused on both the SOPSO and MOPSO. A GA was included mainly for comparison purposes, and the particle swarm optimizers appeared to perform better for a wide variety of optimization problems. All algorithms are capable of performing unconstrained and constrained optimization. The particle swarm optimizers are capable of performing single and multi-objective optimization. The SOPSO and MOPSO algorithms are based on swarming theory and bird-flocking patterns to search the trade space for the optimal solution or optimal trade in competing objectives. The MOPSO generates Pareto fronts for objectives that are in competition. A GA, based on Darwin evolutionary theory, is also included in the library. The GA consists of individuals that form a population in the design space. The population mates to form offspring at new locations in the design space. These offspring contain traits from both of the parents. The algorithm is based on this combination of traits from parents to hopefully provide an improved solution than either of the original parents. As the algorithm progresses, individuals that hold these optimal traits will emerge as the optimal solutions. Due to the generic design of all optimization algorithms, each algorithm interfaces with a user-supplied objective function. This function serves as a "black-box" to the optimizers in which the only purpose of this function is to evaluate solutions provided by the optimizers. Hence, the user-supplied function can be numerical simulations, analytical functions, etc., since the specific detail of this function is of no concern to the optimizer. These algorithms were originally developed to support entry trajectory and guidance design for the Mars Science Laboratory mission but may be applied to any optimization problem.
The Rao-Blackwellized Particle Filter: A Filter Bank Implementation
NASA Astrophysics Data System (ADS)
Hendeby, Gustaf; Karlsson, Rickard; Gustafsson (Eurasipmember), Fredrik
2010-12-01
For computational efficiency, it is important to utilize model structure in particle filtering. One of the most important cases occurs when there exists a linear Gaussian substructure, which can be efficiently handled by Kalman filters. This is the standard formulation of the Rao-Blackwellized particle filter (RBPF). This contribution suggests an alternative formulation of this well-known result that facilitates reuse of standard filtering components and which is also suitable for object-oriented programming. Our RBPF formulation can be seen as a Kalman filter bank with stochastic branching and pruning.
System and Apparatus for Filtering Particles
NASA Technical Reports Server (NTRS)
Agui, Juan H. (Inventor); Vijayakumar, Rajagopal (Inventor)
2015-01-01
A modular pre-filtration apparatus may be beneficial to extend the life of a filter. The apparatus may include an impactor that can collect a first set of particles in the air, and a scroll filter that can collect a second set of particles in the air. A filter may follow the pre-filtration apparatus, thus causing the life of the filter to be increased.
Fully optimal filter for ALLEGRO
NASA Astrophysics Data System (ADS)
Santostasi, Giovanni
2004-03-01
The FAST and SLOW filters are compared when applied to data from one-mode and two-mode resonant gravitational wave detectors. There is no substantial difference between the performance of two filters in the case of the one-mode detector. Notable reduction of the noise temperature is achieved for a two-mode detector when filtering the data with the FAST filter. We explain the principal reason for the better performance of the FAST filter with respect to the SLOW filter. We also observed that the performance of the FAST filter depends on the ratio Î“ between the thermal narrow band noise and the SQUID amplifier white noise.
OPTIMIZATION OF ADVANCED FILTER SYSTEMS
R.A. Newby; M.A. Alvin; G.J. Bruck; T.E. Lippert; E.E. Smeltzer; M.E. Stampahar
2002-06-30
Two advanced, hot gas, barrier filter system concepts have been proposed by the Siemens Westinghouse Power Corporation to improve the reliability and availability of barrier filter systems in applications such as PFBC and IGCC power generation. The two hot gas, barrier filter system concepts, the inverted candle filter system and the sheet filter system, were the focus of bench-scale testing, data evaluations, and commercial cost evaluations to assess their feasibility as viable barrier filter systems. The program results show that the inverted candle filter system has high potential to be a highly reliable, commercially successful, hot gas, barrier filter system. Some types of thin-walled, standard candle filter elements can be used directly as inverted candle filter elements, and the development of a new type of filter element is not a requirement of this technology. Six types of inverted candle filter elements were procured and assessed in the program in cold flow and high-temperature test campaigns. The thin-walled McDermott 610 CFCC inverted candle filter elements, and the thin-walled Pall iron aluminide inverted candle filter elements are the best candidates for demonstration of the technology. Although the capital cost of the inverted candle filter system is estimated to range from about 0 to 15% greater than the capital cost of the standard candle filter system, the operating cost and life-cycle cost of the inverted candle filter system is expected to be superior to that of the standard candle filter system. Improved hot gas, barrier filter system availability will result in improved overall power plant economics. The inverted candle filter system is recommended for continued development through larger-scale testing in a coal-fueled test facility, and inverted candle containment equipment has been fabricated and shipped to a gasifier development site for potential future testing. Two types of sheet filter elements were procured and assessed in the program through cold flow and high-temperature testing. The Blasch, mullite-bonded alumina sheet filter element is the only candidate currently approaching qualification for demonstration, although this oxide-based, monolithic sheet filter element may be restricted to operating temperatures of 538 C (1000 F) or less. Many other types of ceramic and intermetallic sheet filter elements could be fabricated. The estimated capital cost of the sheet filter system is comparable to the capital cost of the standard candle filter system, although this cost estimate is very uncertain because the commercial price of sheet filter element manufacturing has not been established. The development of the sheet filter system could result in a higher reliability and availability than the standard candle filter system, but not as high as that of the inverted candle filter system. The sheet filter system has not reached the same level of development as the inverted candle filter system, and it will require more design development, filter element fabrication development, small-scale testing and evaluation before larger-scale testing could be recommended.
Optimal frequency domain textural edge detection filter
NASA Technical Reports Server (NTRS)
Townsend, J. K.; Shanmugan, K. S.; Frost, V. S.
1985-01-01
An optimal frequency domain textural edge detection filter is developed and its performance evaluated. For the given model and filter bandwidth, the filter maximizes the amount of output image energy placed within a specified resolution interval centered on the textural edge. Filter derivation is based on relating textural edge detection to tonal edge detection via the complex low-pass equivalent representation of narrowband bandpass signals and systems. The filter is specified in terms of the prolate spheroidal wave functions translated in frequency. Performance is evaluated using the asymptotic approximation version of the filter. This evaluation demonstrates satisfactory filter performance for ideal and nonideal textures. In addition, the filter can be adjusted to detect textural edges in noisy images at the expense of edge resolution.
Westinghouse advanced particle filter system
Lippert, T.E.; Bruck, G.J.; Sanjana, Z.N.; Newby, R.A.
1994-10-01
Integrated Gasification Combined Cycles (IGCC) and Pressurized Fluidized Bed Combustion (PFBC) are being developed and demonstrated for commercial, power generation application. Hot gas particulate filters are key components for the successful implementation of IGCC and PFBC in power generation gas turbine cycles. The objective of this work is to develop and qualify through analysis and testing a practical hot gas ceramic barrier filter system that meets the performance and operational requirements of PFBC and IGCC systems. This paper updates the assessment of the Westinghouse hot gas filter design based on ongoing testing and analysis. Results are summarized from recent computational fluid dynamics modeling of the plenum flow during back pulse, analysis of candle stressing under cleaning and process transient conditions and testing and analysis to evaluate potential flow induced candle vibration.
Resource management in particle filtering for multiple object tracking
NASA Astrophysics Data System (ADS)
Pan, Pan; Schonfeld, Dan
2008-01-01
This paper presents a novel particle allocation approach to particle filtering for multiple object tracking which minimizes the total tracking distortion given a fixed number of particles over a video sequence. Under the framework of distributed multiple object tracking, we propose the dynamic proposal variance and optimal particle number allocation algorithm for multi-object tracking to allocate particles among multiple targets as well as multiple frames. Experimental results show the superior performance of our proposed algorithm to traditional particle allocation methods, i.e. a fixed number of particles for each object in each frame. To the best of our knowledge, our approach is the first to provide an optimal allocation of a fixed number of particles among multiple objects and multiple frames.
Buyel, Johannes F; Gruchow, Hannah M; Fischer, Rainer
2015-01-01
The clarification of biological feed stocks during the production of biopharmaceutical proteins is challenging when large quantities of particles must be removed, e.g., when processing crude plant extracts. Single-use depth filters are often preferred for clarification because they are simple to integrate and have a good safety profile. However, the combination of filter layers must be optimized in terms of nominal retention ratings to account for the unique particle size distribution in each feed stock. We have recently shown that predictive models can facilitate filter screening and the selection of appropriate filter layers. Here we expand our previous study by testing several filters with different retention ratings. The filters typically contain diatomite to facilitate the removal of fine particles. However, diatomite can interfere with the recovery of large biopharmaceutical molecules such as virus-like particles and aggregated proteins. Therefore, we also tested filtration devices composed solely of cellulose fibers and cohesive resin. The capacities of both filter types varied from 10 to 50 L m(-2) when challenged with tobacco leaf extracts, but the filtrate turbidity was ~500-fold lower (~3.5 NTU) when diatomite filters were used. We also tested pre-coat filtration with dispersed diatomite, which achieved capacities of up to 120 L m(-2) with turbidities of ~100 NTU using bulk plant extracts, and in contrast to the other depth filters did not require an upstream bag filter. Single pre-coat filtration devices can thus replace combinations of bag and depth filters to simplify the processing of plant extracts, potentially saving on time, labor and consumables. The protein concentrations of TSP, DsRed and antibody 2G12 were not affected by pre-coat filtration, indicating its general applicability during the manufacture of plant-derived biopharmaceutical proteins. PMID:26734037
Buyel, Johannes F.; Gruchow, Hannah M.; Fischer, Rainer
2015-01-01
The clarification of biological feed stocks during the production of biopharmaceutical proteins is challenging when large quantities of particles must be removed, e.g., when processing crude plant extracts. Single-use depth filters are often preferred for clarification because they are simple to integrate and have a good safety profile. However, the combination of filter layers must be optimized in terms of nominal retention ratings to account for the unique particle size distribution in each feed stock. We have recently shown that predictive models can facilitate filter screening and the selection of appropriate filter layers. Here we expand our previous study by testing several filters with different retention ratings. The filters typically contain diatomite to facilitate the removal of fine particles. However, diatomite can interfere with the recovery of large biopharmaceutical molecules such as virus-like particles and aggregated proteins. Therefore, we also tested filtration devices composed solely of cellulose fibers and cohesive resin. The capacities of both filter types varied from 10 to 50 L mâˆ’2 when challenged with tobacco leaf extracts, but the filtrate turbidity was ~500-fold lower (~3.5 NTU) when diatomite filters were used. We also tested preâ€“coat filtration with dispersed diatomite, which achieved capacities of up to 120 L mâˆ’2 with turbidities of ~100 NTU using bulk plant extracts, and in contrast to the other depth filters did not require an upstream bag filter. Single pre-coat filtration devices can thus replace combinations of bag and depth filters to simplify the processing of plant extracts, potentially saving on time, labor and consumables. The protein concentrations of TSP, DsRed and antibody 2G12 were not affected by pre-coat filtration, indicating its general applicability during the manufacture of plant-derived biopharmaceutical proteins. PMID:26734037
Deformable target tracking via particle filter
NASA Astrophysics Data System (ADS)
Wang, Junqing; Shi, Zelin; Huang, Shabai
2005-10-01
We propose an algorithm, which tracks a deformable object in complex scene based on Bayesian estimation in Particle filter framework. In Particle filter framework, both dynamic model and measure model of Particle filter, which utilizes information of structure of target edges and gray level distribution of neighbors of target edges, are respectively constructed in term of interframe correlation in the context of object tracking. The fuzzy metric is constructed to measure the similarity between histograms of template and candidate sub-regions. The tracking window can be adaptively changed with the variation of object appearance. The strategy for template update is applied according to confidence level threshold. Both judgement of occlusion and solution to occlusion are given in term of threshold and temporal window. Those experimental results illustrate that this algorithm can stably track deformable target under complex background at the low computing cost.
Testing particle filters on convective scale dynamics
NASA Astrophysics Data System (ADS)
Haslehner, Mylene; Craig, George. C.; Janjic, Tijana
2014-05-01
Particle filters have been developed in recent years to deal with highly nonlinear dynamics and non Gaussian error statistics that also characterize data assimilation on convective scales. In this work we explore the use of the efficient particle filter (P.v. Leeuwen, 2011) for convective scale data assimilation application. The method is tested in idealized setting, on two stochastic models. The models were designed to reproduce some of the properties of convection, for example the rapid development and decay of convective clouds. The first model is a simple one-dimensional, discrete state birth-death model of clouds (Craig and WÃ¼rsch, 2012). For this model, the efficient particle filter that includes nudging the variables shows significant improvement compared to Ensemble Kalman Filter and Sequential Importance Resampling (SIR) particle filter. The success of the combination of nudging and resampling, measured as RMS error with respect to the 'true state', is proportional to the nudging intensity. Significantly, even a very weak nudging intensity brings notable improvement over SIR. The second model is a modified version of a stochastic shallow water model (WÃ¼rsch and Craig 2013), which contains more realistic dynamical characteristics of convective scale phenomena. Using the efficient particle filter and different combination of observations of the three field variables (wind, water 'height' and rain) allows the particle filter to be evaluated in comparison to a regime where only nudging is used. Sensitivity to the properties of the model error covariance is also considered. Finally, criteria are identified under which the efficient particle filter outperforms nudging alone. References: Craig, G. C. and M. WÃ¼rsch, 2012: The impact of localization and observation averaging for convective-scale data assimilation in a simple stochastic model. Q. J. R. Meteorol. Soc.,139, 515-523. Van Leeuwen, P. J., 2011: Efficient non-linear data assimilation in geophysical fluid dynamics. - Computers and Fluids, doi:10,1016/j.compfluid.2010.11.011, 1096 2011. WÃ¼rsch, M. and G. C. Craig, 2013: A simple dynamical model of cumulus convection for data assimilation research, submitted to Met. Zeitschrift.
Optimal filtering of constant velocity torque data.
Murray, D A
1986-12-01
The purpose of this investigation was to implement an optimal filtering strategy for processing in vivo dynamometric data. The validity of employing commonly accepted analog smoothing methods was also appraised. An inert gravitational model was used to assess the filtering requirements of two Cybex II constant velocity dynamometers at 10 pre-set speeds with three selected loads. Speed settings were recorded as percentages of the servomechanism's maximum tachometer feedback voltage (10 to 100% Vfb max). Spectral analyses of unsmoothed torque and associated angular displacement curves, followed by optimized low-pass digital filtering, revealed the presence of two superimposed contaminating influences: a damped oscillation, representing successive sudden braking and releasing of the servomechanism control system; a relatively stationary oscillatory series, which was attributed to the Cybex motor. The optimal cutoff frequency for any data set was principally a positive function of % Vfb max. This association was represented for each machine by a different, but reliable, third order least-squares polynomial, which could be used to accurately predict the correct smoothing required for any speed setting. Unacceptable errors may be induced, especially when measuring peak torques, if data are inappropriately filtered. Over-smoothing disguises inertial artefacts. The use of Cybex recorder damping settings should be discouraged. Optimal filtering is a minimal requirement of valid data processing. PMID:3784873
Assignment-based particle labeling for PHD particle filter
NASA Astrophysics Data System (ADS)
Danu, Daniel G.; Lang, Thomas; Kirubarajan, Thia
2009-08-01
The probability hypothesis density (PHD) filter is an estimator that approximates, on a given scenario, the multitarget distribution through its first-order multitarget moment. This paper presents two particles labeling algorithms for the PHD particle filter, through which the information on individual targets identity (otherwise hidden within the first-order multitarget moment) is revealed and propagated over time. By maintaining all particles labeled at any time, the individual target distribution estimates are obtained under the form of labeled particle clouds, within the estimated PHD. The partitioning of the PHD into distinct clouds, through labeling, provides over time information on confirmed tracks identity, tracks undergoing initiation or deletion at a given time frame, and clutter regions, otherwise not available in a regular PHD (or track-labeled PHD). Both algorithms imply particles tagging since their inception, in the measurements sampling step, and their re-tagging once they are merged into particle clouds of already confirmed tracks, or are merged for the purpose of initializing new tracks. Particles of a confirmed track cloud preserve their labels over time frames. Two data associations are involved in labels management; one assignment merges measurement clouds into particle clouds of already confirmed tracks, while the following 2D-assignment associates particle clouds corresponding to non-confirmed tracks over two frames, for track initiation. The algorithms are presented on a scenario containing two targets with close and crossing trajectories, with the particle labeled PHD filter tracking under measurement origin uncertainty due to observations variance and clutter.
Efficient particle filter and its application in contour tracking
NASA Astrophysics Data System (ADS)
Chen, Peng; Qian, Hui; Gao, Weisong; Zhu, Miaoliang
2009-10-01
In order to improve the real-time performance of particle filter, this paper proposes an efficient particle filter algorithm and evaluates its usage in object contour tracking application. This new filter uses only one particle to predict next state in certain situations. As particle set size is one, there is no need to resample the particles before prediction. Therefore the real-time performance of particle filter is improved. To maintain the performance of particle filter, we use one particle sample only in prediction step and only in situations where particles are close enough with each other. After prediction step, the original number of particle samples is recovered. Experiment shows that the new particle filter uses less time to process the particles while retain tracking performance comparable with the regular particle filter.
Desensitized Optimal Filtering and Sensor Fusion Toolkit
NASA Technical Reports Server (NTRS)
Karlgaard, Christopher D.
2015-01-01
Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.
MEDOF - MINIMUM EUCLIDEAN DISTANCE OPTIMAL FILTER
NASA Technical Reports Server (NTRS)
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 values of amplitude and phase for the k whose metric was largest, as well as consistency checks, are reported. A finer search can be done in the neighborhood of the optimal k if desired. The filter finally selected is written to disk in terms of drive values, not in terms of the filter's complex transmittance. Optionally, the impulse response of the filter may be created to permit users to examine the response for the features the algorithm deems important to the recognition process under the selected metric, limitations of the filter SLM, etc. MEDOF uses the filter SLM to its greatest potential, therefore filter competence is not compromised for simplicity of computation. MEDOF is written in C-language for Sun series computers running SunOS. With slight modifications, it has been implemented on DEC VAX series computers using the DEC-C v3.30 compiler, although the documentation does not currently support this platform. MEDOF can also be compiled using Borland International Inc.'s Turbo C++ v1.0, but IBM PC memory restrictions greatly reduce the maximum size of the reference images from which the filters can be calculated. MEDOF requires a two dimensional Fast Fourier Transform (2DFFT). One 2DFFT routine which has been used successfully with MEDOF is a routine found in "Numerical Recipes in C: The Art of Scientific Programming," which is available from Cambridge University Press, New Rochelle, NY 10801. The standard distribution medium for MEDOF is a .25 inch streaming magnetic tape cartridge (Sun QIC-24) in UNIX tar format. MEDOF was developed in 1992-1993.
A modified PSO based particle filter algorithm for object tracking
NASA Astrophysics Data System (ADS)
Tang, Yufei; Fu, Siyao; Tang, Bo; He, Haibo
2013-05-01
In this paper, a modified particle swarm optimization (PSO) approach, particle swarm optimization with É›- greedy exploration É›PSO), is used to tackle the object tracking. In the modified É›PSO algorithm, the cooperative learning mechanism among individuals has been introduced, namely, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other best individuals according to certain probability. This kind of biologically-inspired mutual-learning behavior can help to find the global optimum solution with better convergence speed and accuracy. The É›PSO algorithm has been tested on benchmark function and demonstrated its effectiveness in high-dimension multi-modal optimization. In addition to the standard benchmark study, we also combined our new É›PSO approach with the traditional particle filter (PF) algorithm on the object tracking task, such as car tracking in complex environment. Comparative studies between our É›PSO combined PF algorithm with those of existing techniques, such as the particle filter (PF) and classic PSO combined PF will be used to verify and validate the performance of our approach.
Covariance Tracking via Geometric Particle Filtering
NASA Astrophysics Data System (ADS)
Liu, Yunpeng; Li, Guangwei; Shi, Zelin
2010-12-01
Region covariance descriptor recently proposed has been approved robust and elegant to describe a region of interest, which has been applied to visual tracking. We develop a geometric method for visual tracking, in which region covariance is used to model objects appearance; then tracking is led by implementing the particle filter with the constraint that the system state lies in a low dimensional manifold: affine Lie group. The sequential Bayesian updating consists of drawing state samples while moving on the manifold geodesics; the region covariance is updated using a novel approach in a Riemannian space. Our main contribution is developing a general particle filtering-based racking algorithm that explicitly take the geometry of affine Lie groups into consideration in deriving the state equation on Lie groups. Theoretic analysis and experimental evaluations demonstrate the promise and effectiveness of the proposed tracking method.
Particle filter-based track before detect algorithms
NASA Astrophysics Data System (ADS)
Boers, Yvo; Driessen, Hans
2004-01-01
In this paper we will give a general system setup, that allows the formulation of a wide range of Track Before Detect (TBD) problems. A general basic particle filter algorithm for this system is also provided. TBD is a technique, where tracks are produced directly on the basis of raw (radar) measurements, e.g. power or IQ data, without intermediate processing and decision making. The advantage over classical tracking is that the full information is integrated over time, this leads to a better detection and tracking performance, especially for weak targets. In this paper we look at the filtering and the detection aspect of TBD. We will formulate a detection result, that allows the user to implement any optimal detector in terms of the weights of a running particle filter. We will give a theoretical as well as a numerical (experimental) justification for this. Furthermore, we show that the TBD setup, that is chosen in this paper, allows a straightforward extension to the multi-target case. This easy extension is also due to the fact that the implementation of the solution is by means of a particle filter.
Optimal digital filtering for tremor suppression.
Gonzalez, J G; Heredia, E A; Rahman, T; Barner, K E; Arce, G R
2000-05-01
Remote manually operated tasks such as those found in teleoperation, virtual reality, or joystick-based computer access, require the generation of an intermediate electrical signal which is transmitted to the controlled subsystem (robot arm, virtual environment, or a cursor in a computer screen). When human movements are distorted, for instance, by tremor, performance can be improved by digitally filtering the intermediate signal before it reaches the controlled device. This paper introduces a novel tremor filtering framework in which digital equalizers are optimally designed through pursuit tracking task experiments. Due to inherent properties of the man-machine system, the design of tremor suppression equalizers presents two serious problems: 1) performance criteria leading to optimizations that minimize mean-squared error are not efficient for tremor elimination and 2) movement signals show ill-conditioned autocorrelation matrices, which often result in useless or unstable solutions. To address these problems, a new performance indicator in the context of tremor is introduced, and the optimal equalizer according to this new criterion is developed. Ill-conditioning of the autocorrelation matrix is overcome using a novel method which we call pulled-optimization. Experiments performed with artificially induced vibrations and a subject with Parkinson's disease show significant improvement in performance. Additional results, along with MATLAB source code of the algorithms, and a customizable demo for PC joysticks, are available on the Internet at http:Â¿tremor-suppression.com. PMID:10851810
GNSS data filtering optimization for ionospheric observation
NASA Astrophysics Data System (ADS)
D'Angelo, G.; Spogli, L.; Cesaroni, C.; Sgrigna, V.; Alfonsi, L.; Aquino, M. H. O.
2015-12-01
In the last years, the use of GNSS (Global Navigation Satellite Systems) data has been gradually increasing, for both scientific studies and technological applications. High-rate GNSS data, able to generate and output 50-Hz phase and amplitude samples, are commonly used to study electron density irregularities within the ionosphere. Ionospheric irregularities may cause scintillations, which are rapid and random fluctuations of the phase and the amplitude of the received GNSS signals. For scintillation analysis, usually, GNSS signals observed at an elevation angle lower than an arbitrary threshold (usually 15Â°, 20Â° or 30Â°) are filtered out, to remove the possible error sources due to the local environment where the receiver is deployed. Indeed, the signal scattered by the environment surrounding the receiver could mimic ionospheric scintillation, because buildings, trees, etc. might create diffusion, diffraction and reflection. Although widely adopted, the elevation angle threshold has some downsides, as it may under or overestimate the actual impact of multipath due to local environment. Certainly, an incorrect selection of the field of view spanned by the GNSS antenna may lead to the misidentification of scintillation events at low elevation angles. With the aim to tackle the non-ionospheric effects induced by multipath at ground, in this paper we introduce a filtering technique, termed SOLIDIFY (Standalone OutLiers IDentIfication Filtering analYsis technique), aiming at excluding the multipath sources of non-ionospheric origin to improve the quality of the information obtained by the GNSS signal in a given site. SOLIDIFY is a statistical filtering technique based on the signal quality parameters measured by scintillation receivers. The technique is applied and optimized on the data acquired by a scintillation receiver located at the Istituto Nazionale di Geofisica e Vulcanologia, in Rome. The results of the exercise show that, in the considered case of a noisy site under quiet ionospheric conditions, the SOLIDIFY optimization maximizes the quality, instead of the quantity, of the data.
Constrained filter optimization for subsurface landmine detection
NASA Astrophysics Data System (ADS)
Torrione, Peter A.; Collins, Leslie; Clodfelter, Fred; Lulich, Dan; Patrikar, Ajay; Howard, Peter; Weaver, Richard; Rosen, Erik
2006-05-01
Previous large-scale blind tests of anti-tank landmine detection utilizing the NIITEK ground penetrating radar indicated the potential for very high anti-tank landmine detection probabilities at very low false alarm rates for algorithms based on adaptive background cancellation schemes. Recent data collections under more heterogeneous multi-layered road-scenarios seem to indicate that although adaptive solutions to background cancellation are effective, the adaptive solutions to background cancellation under different road conditions can differ significantly, and misapplication of these adaptive solutions can reduce landmine detection performance in terms of PD/FAR. In this work we present a framework for the constrained optimization of background-estimation filters that specifically seeks to optimize PD/FAR performance as measured by the area under the ROC curve between two FARs. We also consider the application of genetic algorithms to the problem of filter optimization for landmine detection. Results indicate robust results for both static and adaptive background cancellation schemes, and possible real-world advantages and disadvantages of static and adaptive approaches are discussed.
Optimal edge filters explain human blur detection.
McIlhagga, William H; May, Keith A
2012-01-01
Edges are important visual features, providing many cues to the three-dimensional structure of the world. One of these cues is edge blur. Sharp edges tend to be caused by object boundaries, while blurred edges indicate shadows, surface curvature, or defocus due to relative depth. Edge blur also drives accommodation and may be implicated in the correct development of the eye's optical power. Here we use classification image techniques to reveal the mechanisms underlying blur detection in human vision. Observers were shown a sharp and a blurred edge in white noise and had to identify the blurred edge. The resultant smoothed classification image derived from these experiments was similar to a derivative of a Gaussian filter. We also fitted a number of edge detection models (MIRAGE, N(1), and N(3)(+)) and the ideal observer to observer responses, but none performed as well as the classification image. However, observer responses were well fitted by a recently developed optimal edge detector model, coupled with a Bayesian prior on the expected blurs in the stimulus. This model outperformed the classification image when performance was measured by the Akaike Information Criterion. This result strongly suggests that humans use optimal edge detection filters to detect edges and encode their blur. PMID:22984222
Program Computes SLM Inputs To Implement Optimal Filters
NASA Technical Reports Server (NTRS)
Barton, R. Shane; Juday, Richard D.; Alvarez, Jennifer L.
1995-01-01
Minimum Euclidean Distance Optimal Filter (MEDOF) program generates filters for use in optical correlators. Analytically optimizes filters on arbitrary spatial light modulators (SLMs) of such types as coupled, binary, fully complex, and fractional-2pi-phase. Written in C language.
Groupwise surface correspondence using particle filtering
NASA Astrophysics Data System (ADS)
Li, Guangxu; Kim, Hyoungseop; Tan, Joo Kooi; Ishikawa, Seiji
2015-03-01
To obtain an effective interpretation of organic shape using statistical shape models (SSMs), the correspondence of the landmarks through all the training samples is the most challenging part in model building. In this study, a coarse-tofine groupwise correspondence method for 3-D polygonal surfaces is proposed. We manipulate a reference model in advance. Then all the training samples are mapped to a unified spherical parameter space. According to the positions of landmarks of the reference model, the candidate regions for correspondence are chosen. Finally we refine the perceptually correct correspondences between landmarks using particle filter algorithm, where the likelihood of local surface features are introduced as the criterion. The proposed method was performed on the correspondence of 9 cases of left lung training samples. Experimental results show the proposed method is flexible and under-constrained.
Implementation and performance of FPGA-accelerated particle flow filter
NASA Astrophysics Data System (ADS)
Charalampidis, Dimitrios; Jilkov, Vesselin P.; Wu, Jiande
2015-09-01
The particle flow filters, proposed by Daum & Hwang, provide a powerful means for density-based nonlinear filtering but their computation is intense and may be prohibitive for real-time applications. This paper proposes a design for superfast implementation of the exact particle flow filter using a field-programmable gate array (FPGA) as a parallel environment to speedup computation. Simulation results from a nonlinear filtering example are presented to demonstrate that using FPGA can dramatically accelerate particle flow filters through parallelization at the expense of a tolerable loss in accuracy as compared to nonparallel implementation.
Simulations of acoustic tomography using a particle filter
NASA Astrophysics Data System (ADS)
Zhang, Ming; Xu, Wen; Li, Jianlong
2012-11-01
This paper uses the state-space model to track the sound speed profile between a moving source suspended from a ship and a fixed vertical linear array. The particle filtering approach is presented to handle this nonlinear and non-Gaussian inverse problem. Simulation results show that the particle filter with a high particle number outperforms the extended Kalman filter; however, the performance degrades when the dimension of the state increases.
NASA Astrophysics Data System (ADS)
Hirpa, F. A.; Gebremichael, M.; LEE, H.; Hopson, T. M.
2012-12-01
Hydrologic data assimilation techniques provide a means to improve river discharge forecasts through updating hydrologic model states and correcting the atmospheric forcing data via optimally combining model outputs with observations. The performance of the assimilation procedure, however, depends on the data assimilation techniques used and the amount of uncertainty in the data sets. To investigate the effects of these, we comparatively evaluate three data assimilation techniques, including ensemble Kalman filter (EnKF), particle filter (PF) and variational (VAR) technique, which assimilate discharge and synthetic soil moisture data at various uncertainty levels into the Sacramento Soil Moisture accounting (SAC-SMA) model used by the National Weather Service (NWS) for river forecasting in The United States. The study basin is Greens Bayou watershed with area of 178 km2 in eastern Texas. In the presentation, we summarize the results of the comparisons, and discuss the challenges of applying each technique for hydrologic applications.
Metal finishing wastewater pressure filter optimization
Norford, S.W.; Diener, G.A.; Martin, H.L.
1992-12-31
The 300-M Area Liquid Effluent Treatment Facility (LETF) of the Savannah River Site (SRS) is an end-of-pipe industrial wastewater treatment facility, that uses precipitation and filtration which is the EPA Best Available Technology economically achievable for a Metal Finishing and Aluminum Form Industries. The LETF consists of three close-coupled treatment facilities: the Dilute Effluent Treatment Facility (DETF), which uses wastewater equalization, physical/chemical precipitation, flocculation, and filtration; the Chemical Treatment Facility (CTF), which slurries the filter cake generated from the DETF and pumps it to interim-StatuS RCRA storage tanks; and the Interim Treatment/Storage Facility (IT/SF) which stores the waste from the CTF until the waste is stabilized/solidified for permanent disposal, 85% of the stored waste is from past nickel plating and aluminum canning of depleted uranium targets for the SRS nuclear reactors. Waste minimization and filtration efficiency are key to cost effective treatment of the supernate, because the waste filter cake generated is returned to the IT/SF. The DETF has been successfully optimized to achieve maximum efficiency and to minimize waste generation.
Metal finishing wastewater pressure filter optimization
Norford, S.W.; Diener, G.A.; Martin, H.L.
1992-01-01
The 300-M Area Liquid Effluent Treatment Facility (LETF) of the Savannah River Site (SRS) is an end-of-pipe industrial wastewater treatment facility, that uses precipitation and filtration which is the EPA Best Available Technology economically achievable for a Metal Finishing and Aluminum Form Industries. The LETF consists of three close-coupled treatment facilities: the Dilute Effluent Treatment Facility (DETF), which uses wastewater equalization, physical/chemical precipitation, flocculation, and filtration; the Chemical Treatment Facility (CTF), which slurries the filter cake generated from the DETF and pumps it to interim-StatuS RCRA storage tanks; and the Interim Treatment/Storage Facility (IT/SF) which stores the waste from the CTF until the waste is stabilized/solidified for permanent disposal, 85% of the stored waste is from past nickel plating and aluminum canning of depleted uranium targets for the SRS nuclear reactors. Waste minimization and filtration efficiency are key to cost effective treatment of the supernate, because the waste filter cake generated is returned to the IT/SF. The DETF has been successfully optimized to achieve maximum efficiency and to minimize waste generation.
A multiple feature based particle filter using mutual information maximization
NASA Astrophysics Data System (ADS)
Hong, Kihyun; Han, Kyuseo
2011-01-01
In designing a tracking algorithm, utilizing several different features, e.g., color histogram, gradient histogram and other object descriptors, is preferable to increase robustness of tracking performance. In this paper, we propose a multiple feature fusion framework to improve the tracking by assigning appropriate weights to individual features. The feature weights are optimally obtained by a waterfilling procedure that maximizes mutual information between target object features and query features. Especially, in this paper, we focus on a particle filter tracking implementation of the multiple feature fusion framework. Our experiments show that object tracking with multiple features outperforms single feature based tracking methods and illustrates that the proposed optimal feature weighting increases robustness of multiple-feature based tracking performance.
Human-Manipulator Interface Using Particle Filter
Wang, Xueqian
2014-01-01
This paper utilizes a human-robot interface system which incorporates particle filter (PF) and adaptive multispace transformation (AMT) to track the pose of the human hand for controlling the robot manipulator. This system employs a 3D camera (Kinect) to determine the orientation and the translation of the human hand. We use Camshift algorithm to track the hand. PF is used to estimate the translation of the human hand. Although a PF is used for estimating the translation, the translation error increases in a short period of time when the sensors fail to detect the hand motion. Therefore, a methodology to correct the translation error is required. What is more, to be subject to the perceptive limitations and the motor limitations, human operator is hard to carry out the high precision operation. This paper proposes an adaptive multispace transformation (AMT) method to assist the operator to improve the accuracy and reliability in determining the pose of the robot. The human-robot interface system was experimentally tested in a lab environment, and the results indicate that such a system can successfully control a robot manipulator. PMID:24757430
Tractable particle filters for robot fault diagnosis
NASA Astrophysics Data System (ADS)
Verma, Vandi
Experience has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Classical Monte Carlo methods, such as particle filters, suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.
Application of particle filtering algorithm in image reconstruction of EMT
NASA Astrophysics Data System (ADS)
Wang, Jingwen; Wang, Xu
2015-07-01
To improve the image quality of electromagnetic tomography (EMT), a new image reconstruction method of EMT based on a particle filtering algorithm is presented. Firstly, the principle of image reconstruction of EMT is analyzed. Then the search process for the optimal solution for image reconstruction of EMT is described as a system state estimation process, and the state space model is established. Secondly, to obtain the minimum variance estimation of image reconstruction, the optimal weights of random samples obtained from the state space are calculated from the measured information. Finally, simulation experiments with five different flow regimes are performed. The experimental results have shown that the average image error of reconstruction results obtained by the method mentioned in this paper is 42.61%, and the average correlation coefficient with the original image is 0.8706, which are much better than corresponding indicators obtained by LBP, Landweber and Kalman Filter algorithms. So, this EMT image reconstruction method has high efficiency and accuracy, and provides a new method and means for EMT research.
A hybrid method for optimization of the adaptive Goldstein filter
NASA Astrophysics Data System (ADS)
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
Optimal design of AC filter circuits in HVDC converter stations
Saied, M.M.; Khader, S.A.
1995-12-31
This paper investigates the reactive power as well as the harmonic conditions on both the valve and the AC-network sides of a HVDC converter station. The effect of the AC filter circuits is accurately modeled. The program is then augmented by adding an optimization routine. It can identify the optimal filter configuration, yielding the minimum current distortion factor at the AC network terminals for a prespecified fundamental reactive power to be provided by the filter. Several parameter studies were also conducted to illustrate the effect of accidental or intentional deletion of one of the filter branches.
Optimal filter bandwidth for pulse oximetry
NASA Astrophysics Data System (ADS)
Stuban, Norbert; Niwayama, Masatsugu
2012-10-01
Pulse oximeters contain one or more signal filtering stages between the photodiode and microcontroller. These filters are responsible for removing the noise while retaining the useful frequency components of the signal, thus improving the signal-to-noise ratio. The corner frequencies of these filters affect not only the noise level, but also the shape of the pulse signal. Narrow filter bandwidth effectively suppresses the noise; however, at the same time, it distorts the useful signal components by decreasing the harmonic content. In this paper, we investigated the influence of the filter bandwidth on the accuracy of pulse oximeters. We used a pulse oximeter tester device to produce stable, repetitive pulse waves with digitally adjustable R ratio and heart rate. We built a pulse oximeter and attached it to the tester device. The pulse oximeter digitized the current of its photodiode directly, without any analog signal conditioning. We varied the corner frequency of the low-pass filter in the pulse oximeter in the range of 0.66-15 Hz by software. For the tester device, the R ratio was set to R = 1.00, and the R ratio deviation measured by the pulse oximeter was monitored as a function of the corner frequency of the low-pass filter. The results revealed that lowering the corner frequency of the low-pass filter did not decrease the accuracy of the oxygen level measurements. The lowest possible value of the corner frequency of the low-pass filter is the fundamental frequency of the pulse signal. We concluded that the harmonics of the pulse signal do not contribute to the accuracy of pulse oximetry. The results achieved by the pulse oximeter tester were verified by human experiments, performed on five healthy subjects. The results of the human measurements confirmed that filtering out the harmonics of the pulse signal does not degrade the accuracy of pulse oximetry.
Optimal Filter Systems for Photometric Redshift Estimation
NASA Astrophysics Data System (ADS)
BenÃtez, N.; Moles, M.; Aguerri, J. A. L.; Alfaro, E.; Broadhurst, T.; Cabrera-CaÃ±o, J.; Castander, F. J.; Cepa, J.; CerviÃ±o, M.; CristÃ³bal-Hornillos, D.; FernÃ¡ndez-Soto, A.; GonzÃ¡lez Delgado, R. M.; Infante, L.; MÃ¡rquez, I.; MartÃnez, V. J.; Masegosa, J.; Del Olmo, A.; Perea, J.; Prada, F.; Quintana, J. M.; SÃ¡nchez, S. F.
2009-02-01
In the coming years, several cosmological surveys will rely on imaging data to estimate the redshift of galaxies, using traditional filter systems with 4-5 optical broad bands; narrower filters improve the spectral resolution, but strongly reduce the total system throughput. We explore how photometric redshift performance depends on the number of filters nf , characterizing the survey depth by the fraction of galaxies with unambiguous redshift estimates. For a combination of total exposure time and telescope imaging area of 270 hr m2, 4-5 filter systems perform significantly worse, both in completeness depth and precision, than systems with nf gsim 8 filters. Our results suggest that for low nf the color-redshift degeneracies overwhelm the improvements in photometric depth, and that even at higher nf the effective photometric redshift depth decreases much more slowly with filter width than naively expected from the reduction in the signal-to-noise ratio. Adding near-IR observations improves the performance of low-nf systems, but still the system which maximizes the photometric redshift completeness is formed by nine filters with logarithmically increasing bandwidth (constant resolution) and half-band overlap, reaching ~0.7 mag deeper, with 10% better redshift precision, than 4-5 filter systems. A system with 20 constant-width, nonoverlapping filters reaches only ~0.1 mag shallower than 4-5 filter systems, but has a precision almost three times better, Î´z = 0.014(1 + z) versus Î´z = 0.042(1 + z). We briefly discuss a practical implementation of such a photometric system: the ALHAMBRA Survey.
Symmetric Phase-Only Filtering in Particle-Image Velocimetry
NASA Technical Reports Server (NTRS)
Wemet, Mark P.
2008-01-01
Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle-image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times. A summary of PIV and traditional PIV data processing is prerequisite to a meaningful description of SPOF PIV processing. In PIV, a pulsed laser is used to illuminate a substantially planar region of a flowing fluid in which particles are entrained. An electronic camera records digital images of the particles at two instants of time. The components of velocity of the fluid in the illuminated plane can be obtained by determining the displacements of particles between the two illumination pulses. The objective in PIV data processing is to compute the particle displacements from the digital image data. In traditional PIV data processing, to which the present innovation applies, the two images are divided into a grid of subregions and the displacements determined from cross-correlations between the corresponding sub-regions in the first and second images. The cross-correlation process begins with the calculation of the Fourier transforms (or fast Fourier transforms) of the subregion portions of the images. The Fourier transforms from the corresponding subregions are multiplied, and this product is inverse Fourier transformed, yielding the cross-correlation intensity distribution. The average displacement of the particles across a subregion results in a displacement of the correlation peak from the center of the correlation plane. The velocity is then computed from the displacement of the correlation peak and the time between the recording of the two images. The process as described thus far is performed for all the subregions. The resulting set of velocities in grid cells amounts to a velocity vector map of the flow field recorded on the image plane. In traditional PIV processing, surface flare light and bright background light give rise to a large, broad correlation peak, at the center of the correlation plane, that can overwhelm the true particle- displacement correlation peak. This has made it necessary to resort to tedious image-masking and background-subtraction procedures to recover the relatively small amplitude particle-displacement correlation peak. SPOF is a variant of phase-only filtering (POF), which, in turn, is a variant of matched spatial filtering (MSF). In MSF, one projects a first image (denoted the input image) onto a second image (denoted the filter) as part of a computation to determine how much and what part of the filter is present in the input image. MSF is equivalent to cross-correlation. In POF, the frequency-domain content of the MSF filter is modified to produce a unitamplitude (phase-only) object. POF is implemented by normalizing the Fourier transform of the filter by its magnitude. The advantage of POFs is that they yield correlation peaks that are sharper and have higher signal-to-noise ratios than those obtained through traditional MSF. In the SPOF, these benefits of POF can be extended to PIV data processing. The SPOF yields even better performance than the POF approach, which is uniquely applicable to PIV type image data. In SPOF as now applied to PIV data processing, a subregion of the first image is treated as the input image and the corresponding subregion of the second image is treated as the filter. The Fourier transforms from both the firs and second- image subregions are normalized by the square roots of their respective magnitudes. This scheme yields optimal performance because the amounts of normalization applied to the spatial-frequency contents of the input and filter scenes are just enough to enhance their high-spatial-frequency contents while reducing their spurious low-spatial-frequency content. As a result, in SPOF PIV processing, particle-displacement correlation peaks can readily be detected above spurious background peaks, without need for masking or background subtraction.
Simultaneous Eye Tracking and Blink Detection with Interactive Particle Filters
NASA Astrophysics Data System (ADS)
Wu, Junwen; Trivedi, Mohan M.
2007-12-01
We present a system that simultaneously tracks eyes and detects eye blinks. Two interactive particle filters are used for this purpose, one for the closed eyes and the other one for the open eyes. Each particle filter is used to track the eye locations as well as the scales of the eye subjects. The set of particles that gives higher confidence is defined as the primary set and the other one is defined as the secondary set. The eye location is estimated by the primary particle filter, and whether the eye status is open or closed is also decided by the label of the primary particle filter. When a new frame comes, the secondary particle filter is reinitialized according to the estimates from the primary particle filter. We use autoregression models for describing the state transition and a classification-based model for measuring the observation. Tensor subspace analysis is used for feature extraction which is followed by a logistic regression model to give the posterior estimation. The performance is carefully evaluated from two aspects: the blink detection rate and the tracking accuracy. The blink detection rate is evaluated using videos from varying scenarios, and the tracking accuracy is given by comparing with the benchmark data obtained using the Vicon motion capturing system. The setup for obtaining benchmark data for tracking accuracy evaluation is presented and experimental results are shown. Extensive experimental evaluations validate the capability of the algorithm.
Optimal Gain Filter Design for Perceptual Acoustic Echo Suppressor
NASA Astrophysics Data System (ADS)
Kim, Kihyeon; Ko, Hanseok
This Letter proposes an optimal gain filter for the perceptual acoustic echo suppressor. We designed an optimally-modified log-spectral amplitude estimation algorithm for the gain filter in order to achieve robust suppression of echo and noise. A new parameter including information about interferences (echo and noise) of single-talk duration is statistically analyzed, and then the speech absence probability and the a posteriori SNR are judiciously estimated to determine the optimal solution. The experiments show that the proposed gain filter attains a significantly improved reduction of echo and noise with less speech distortion.
Ballistic target tracking algorithm based on improved particle filtering
NASA Astrophysics Data System (ADS)
Ning, Xiao-lei; Chen, Zhan-qi; Li, Xiao-yang
2015-10-01
Tracking ballistic re-entry target is a typical nonlinear filtering problem. In order to track the ballistic re-entry target in the nonlinear and non-Gaussian complex environment, a novel chaos map particle filter (CMPF) is used to estimate the target state. CMPF has better performance in application to estimate the state and parameter of nonlinear and non-Gassuian system. The Monte Carlo simulation results show that, this method can effectively solve particle degeneracy and particle impoverishment problem by improving the efficiency of particle sampling to obtain the better particles to part in estimation. Meanwhile CMPF can improve the state estimation precision and convergence velocity compared with EKF, UKF and the ordinary particle filter.
Entropy-based optimization of wavelet spatial filters.
Farina, Darino; Kamavuako, Ernest Nlandu; Wu, Jian; Naddeo, Francesco
2008-03-01
A new class of spatial filters for surface electromyographic (EMG) signal detection is proposed. These filters are based on the 2-D spatial wavelet decomposition of the surface EMG recorded with a grid of electrodes and inverse transformation after zeroing a subset of the transformation coefficients. The filter transfer function depends on the selected mother wavelet in the two spatial directions. Wavelet parameterization is proposed with the aim of signal-based optimization of the transfer function of the spatial filter. The optimization criterion was the minimization of the entropy of the time samples of the output signal. The optimized spatial filter is linear and space invariant. In simulated and experimental recordings, the optimized wavelet filter showed increased selectivity with respect to previously proposed filters. For example, in simulation, the ratio between the peak-to-peak amplitude of action potentials generated by motor units 20 degrees apart in the transversal direction was 8.58% (with monopolar recording), 2.47% (double differential), 2.59% (normal double differential), and 0.47% (optimized wavelet filter). In experimental recordings, the duration of the detected action potentials decreased from (mean +/- SD) 6.9 +/- 0.3 ms (monopolar recording), to 4.5 +/- 0.2 ms (normal double differential), 3.7 +/- 0.2 (double differential), and 3.0 +/- 0.1 ms (optimized wavelet filter). In conclusion, the new class of spatial filters with the proposed signal-based optimization of the transfer function allows better discrimination of individual motor unit activities in surface EMG recordings than it was previously possible. PMID:18334382
Method of concurrently filtering particles and collecting gases
Mitchell, Mark A; Meike, Annemarie; Anderson, Brian L
2015-04-28
A system for concurrently filtering particles and collecting gases. Materials are be added (e.g., via coating the ceramic substrate, use of loose powder(s), or other means) to a HEPA filter (ceramic, metal, or otherwise) to collect gases (e.g., radioactive gases such as iodine). The gases could be radioactive, hazardous, or valuable gases.
NASA Astrophysics Data System (ADS)
Noh, S. J.; Tachikawa, Y.; Shiiba, M.; Kim, S.
2011-10-01
Data assimilation techniques have received growing attention due to their capability to improve prediction. Among various data assimilation techniques, sequential Monte Carlo (SMC) methods, known as "particle filters", are a Bayesian learning process that has the capability to handle non-linear and non-Gaussian state-space models. In this paper, we propose an improved particle filtering approach to consider different response times of internal state variables in a hydrologic model. The proposed method adopts a lagged filtering approach to aggregate model response until the uncertainty of each hydrologic process is propagated. The regularization with an additional move step based on the Markov chain Monte Carlo (MCMC) methods is also implemented to preserve sample diversity under the lagged filtering approach. A distributed hydrologic model, water and energy transfer processes (WEP), is implemented for the sequential data assimilation through the updating of state variables. The lagged regularized particle filter (LRPF) and the sequential importance resampling (SIR) particle filter are implemented for hindcasting of streamflow at the Katsura catchment, Japan. Control state variables for filtering are soil moisture content and overland flow. Streamflow measurements are used for data assimilation. LRPF shows consistent forecasts regardless of the process noise assumption, while SIR has different values of optimal process noise and shows sensitive variation of confidential intervals, depending on the process noise. Improvement of LRPF forecasts compared to SIR is particularly found for rapidly varied high flows due to preservation of sample diversity from the kernel, even if particle impoverishment takes place.
Multifeature fusion tracking in a particle filter framework
NASA Astrophysics Data System (ADS)
Pei, Lizhi; Zhang, Peng; Wang, Runsheng
2009-10-01
Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. In this study we used the particle filtering technique with multiple features to track the moving object effectively in video image. The object tracking system relies on the deterministic search of window, whose color content matches a reference histogram model. A simple histogram-based color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by PCA transform technique. Our observation system of particle filter uses the combination of color and PCA features with a likelihood measurement. Experiment results show that the algorithm can effectively handle the effect of illumination, and is stable and robust.
Particle filter-based prognostics: Review, discussion and perspectives
NASA Astrophysics Data System (ADS)
Jouin, Marine; Gouriveau, Rafael; Hissel, Daniel; PÃ©ra, Marie-CÃ©cile; Zerhouni, Noureddine
2016-05-01
Particle filters are of great concern in a large variety of engineering fields such as robotics, statistics or automatics. Recently, it has developed among Prognostics and Health Management (PHM) applications for diagnostics and prognostics. According to some authors, it has ever become a state-of-the-art technique for prognostics. Nowadays, around 50 papers dealing with prognostics based on particle filters can be found in the literature. However, no comprehensive review has been proposed on the subject until now. This paper aims at analyzing the way particle filters are used in that context. The development of the tool in the prognostics' field is discussed before entering the details of its practical use and implementation. Current issues are identified, analyzed and some solutions or work trails are proposed. All this aims at highlighting future perspectives as well as helping new users to start with particle filters in the goal of prognostics.
Contrasting Particle Clogging in Soils and Granular Media Filters
NASA Astrophysics Data System (ADS)
Mays, D. C.
2005-12-01
Deposition of colloidal particles leads to permeability reduction (or clogging) in the soil geomembrane, which reduces fluxes, alters flow patterns, and limits both colloid-associated contaminant transport and delivery of colloidal reactants for purposes of remediation. Comparison of experimental results for soils and granular media filters reveals qualitatively different clogging phenomena with regard to (1) particle stabilization, (2) fluid velocity, and (3) the fractal dimension of particle deposits. These differences have important implications for contaminant hydrology, because the classical approach for understanding particles in natural environments is taken from the filtration literature, which is based on clean granular media. Accordingly, many of the relevant experiments have been performed with granular filters using media such as glass beads or quartz sand. In such filters, clogging is associated with destabilized particles, slower fluid velocity and deposits with smaller fractal dimensions. In contrast, in soils clogging is associated with stabilized particles, faster fluid velocity and deposits with larger fractal dimensions. With regard to these variables, soils are opposite to filters but identical to cake filtration. Numerous examples will be presented from the filtration literature and the soil science literature to illustrate these differing viewpoints. This analysis demonstrates that experiments on clean granular media filters should not be expected to predict particle clogging in soils, sandstones or other natural porous materials containing more than a few percent fines.
Geomagnetic modeling by optimal recursive filtering
NASA Technical Reports Server (NTRS)
Gibbs, B. P.; Estes, R. H.
1981-01-01
The results of a preliminary study to determine the feasibility of using Kalman filter techniques for geomagnetic field modeling are given. Specifically, five separate field models were computed using observatory annual means, satellite, survey and airborne data for the years 1950 to 1976. Each of the individual field models used approximately five years of data. These five models were combined using a recursive information filter (a Kalman filter written in terms of information matrices rather than covariance matrices.) The resulting estimate of the geomagnetic field and its secular variation was propogated four years past the data to the time of the MAGSAT data. The accuracy with which this field model matched the MAGSAT data was evaluated by comparisons with predictions from other pre-MAGSAT field models. The field estimate obtained by recursive estimation was found to be superior to all other models.
Cheng, Wen-Chang
2012-01-01
In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options. PMID:23235453
Forward-looking infrared 3D target tracking via combination of particle filter and SIFT
NASA Astrophysics Data System (ADS)
Li, Xing; Cao, Zhiguo; Yan, Ruicheng; Li, Tuo
2013-10-01
Aiming at the problem of tracking 3D target in forward-looking infrared (FLIR) image, this paper proposes a high-accuracy robust tracking algorithm based on SIFT and particle filter. The main contribution of this paper is the proposal of a new method of estimating the affine transformation matrix parameters based on Monte Carlo methods of particle filter. At first, we extract SIFT features on infrared image, and calculate the initial affine transformation matrix with optimal candidate key points. Then we take affine transformation parameters as particles, and use SIR (Sequential Importance Resampling) particle filter to estimate the best position, thus implementing our algorithm. The experiments demonstrate that our algorithm proves to be robust with high accuracy.
Optimal Sharpening of Compensated Comb Decimation Filters: Analysis and Design
Troncoso Romero, David Ernesto
2014-01-01
Comb filters are a class of low-complexity filters especially useful for multistage decimation processes. However, the magnitude response of comb filters presents a droop in the passband region and low stopband attenuation, which is undesirable in many applications. In this work, it is shown that, for stringent magnitude specifications, sharpening compensated comb filters requires a lower-degree sharpening polynomial compared to sharpening comb filters without compensation, resulting in a solution with lower computational complexity. Using a simple three-addition compensator and an optimization-based derivation of sharpening polynomials, we introduce an effective low-complexity filtering scheme. Design examples are presented in order to show the performance improvement in terms of passband distortion and selectivity compared to other methods based on the traditional Kaiser-Hamming sharpening and the Chebyshev sharpening techniques recently introduced in the literature. PMID:24578674
Particle-filter-based phase estimation in digital holographic interferometry.
Waghmare, Rahul G; Ram Sukumar, P; Subrahmanyam, G R K S; Singh, Rakesh Kumar; Mishra, Deepak
2016-03-01
In this paper, we propose a particle-filter-based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non-Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal-tracking approach on a Taylor series expanded state model and a polar-to-Cartesian-conversion-based measurement model. Computation of sample weights through non-Gaussian likelihood forms the major contribution of the proposed particle-filter-based approach compared to the existing unscented-Kalman-filter-based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state-of-the-art especially at very low signal-to-noise ratios (i.e., especially in the range of -5 to 20 dB). The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation. PMID:26974901
Analysis of Video-Based Microscopic Particle Trajectories Using Kalman Filtering
Wu, Pei-Hsun; Agarwal, Ashutosh; Hess, Henry; Khargonekar, Pramod P.; Tseng, Yiider
2010-01-01
Abstract The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including inÂ situ single cell particle tracking and inÂ vitro motility assays. However, the image acquisition process is complicated by system noise, which causes positioning error in the trajectories derived from image analysis. Here, we explore the possibility of reducing the positioning error by the application of a Kalman filter, a powerful algorithm to estimate the state of a linear dynamic system from noisy measurements. We show that the optimal Kalman filter parameters can be determined in an appropriate experimental setting, and that the Kalman filter can markedly reduce the positioning error while retaining the intrinsic fluctuations of the dynamic process. We believe the Kalman filter can potentially serve as a powerful tool to infer a trajectory of ultra-high fidelity from noisy images, revealing the details of dynamic cellular processes. PMID:20550894
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). he collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. or fibrous particulate filters, measurement of collectio...
COMPUTATIONS ON THE PERFORMANCE OF PARTICLE FILTERS AND ELECTRONIC AIR CLEANERS
The paper discusses computations on the performance of particle filters and electronic air cleaners (EACs). The collection efficiency of particle filters and ACs is calculable if certain factors can be assumed or calibrated. For fibrous particulate filters, measurement of colle...
Optimization of OT-MACH Filter Generation for Target Recognition
NASA Technical Reports Server (NTRS)
Johnson, Oliver C.; Edens, Weston; Lu, Thomas T.; Chao, Tien-Hsin
2009-01-01
An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, alpha, beta, and gamma. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of alpha, beta, gamma values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.
NASA Astrophysics Data System (ADS)
Sambaer, Wannes; Zatloukal, Martin; Kimmer, Dusan
2013-04-01
Realistic SEM image based 3D filter model considering transition/free molecular flow regime, Brownian diffusion, aerodynamic slip, particle-fiber and particle-particle interactions together with a novel Euclidian distance map based methodology for the pressure drop calculation has been utilized for a polyurethane nanofiber based filter prepared via electrospinning process in order to more deeply understand the effect of particle-fiber friction coefficient on filter clogging and basic filter characteristics. Based on the performed theoretical analysis, it has been revealed that the increase in the fiber-particle friction coefficient causes, firstly, more weaker particle penetration in the filter, creation of dense top layers and generation of higher pressure drop (surface filtration) in comparison with lower particle-fiber friction coefficient filter for which deeper particle penetration takes place (depth filtration), secondly, higher filtration efficiency, thirdly, higher quality factor and finally, higher quality factor sensitivity to the increased collected particle mass. Moreover, it has been revealed that even if the particle-fiber friction coefficient is different, the cake morphology is very similar.
Robust tracking algorithm using mean-shift and particle filter
NASA Astrophysics Data System (ADS)
Wang, Jianhua; Liang, Wei
2011-12-01
Aiming to the problems that Mean-Shift algorithm costs low computation, but easy to fall into local maximum, and huge computation of Particle Filter tracking algorithm leads to low real-time processing capacity, according to the need of real stereo vision measurement system, a kind of tracking algorithm which combines Mean-Shift and Particle Filter by essentiality function is proposed. Under the condition without occlusion, Mean-Shift is used to track object. When object is occluded, Particle Filter is applied to accomplish the later object tracking. These two algorithms alternate by a defined threshold. The tracking algorithm is used into real stereo vision measurement system. Experiment result indicates that the algorithm takes on high efficiency, so it is of high practicability.
Fish tracking by combining motion based segmentation and particle filtering
NASA Astrophysics Data System (ADS)
Bichot, E.; Mascarilla, L.; Courtellemont, P.
2006-01-01
In this paper, we suggest a new importance sampling scheme to improve a particle filtering based tracking process. This scheme relies on exploitation of motion segmentation. More precisely, we propagate hypotheses from particle filtering to blobs of similar motion to target. Hence, search is driven toward regions of interest in the state space and prediction is more accurate. We also propose to exploit segmentation to update target model. Once the moving target has been identified, a representative model is learnt from its spatial support. We refer to this model in the correction step of the tracking process. The importance sampling scheme and the strategy to update target model improve the performance of particle filtering in complex situations of occlusions compared to a simple Bootstrap approach as shown by our experiments on real fish tank sequences.
Estimate the Electromechanical States Using Particle Filtering and Smoothing
Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang
2012-07-22
Accurate knowledge of electromechanical states is critical for efficient and reliable control of a power system. This paper proposes a particle filtering approach to estimate the electromechanical states of power systems from Phasor Measurement Unit (PMU) data. Without having to go through laborious linearization procedure, the proposed particle filtering techniques can estimate states of a complex power system, which is often non-linear and has non-Gaussian noise. The proposed method is evaluated using a multi-machine system with both large and small disturbances. Sensitivity studies of the dynamic state estimation performance are also presented to show the robustness of the proposed method. The inherent decoupling properties of particle filtering make it highly scalable and the potential to reduce computational time through parallel implementation is very promising.
Multiple states and joint objects particle filter for eye tracking
NASA Astrophysics Data System (ADS)
Xiong, Jin; Jiang, Zhaohui; Liu, Junwei; Feng, Huanqing
2007-11-01
Recent works have proven that the particle filter is a powerful tracking technique for non-linear and non-Gaussian estimation problem. This paper presents an extension algorithm based on the color-based particle filter framework, which is applicable for complex eye tracking because of two main innovations. Firstly, an employment of an extra discrete-value variable and its associated transition probability matrix (TPM) makes it feasible in tracking multiple states of the eye during blinking. Secondly, the joint-object thought used in state vector eliminates the distraction from eyes to each other. The experimental results illustrate that the proposed algorithm is efficient for eye tracking.
Identifying Optimal Measurement Subspace for the Ensemble Kalman Filter
Zhou, Ning; Huang, Zhenyu; Welch, Greg; Zhang, J.
2012-05-24
To reduce the computational load of the ensemble Kalman filter while maintaining its efficacy, an optimization algorithm based on the generalized eigenvalue decomposition method is proposed for identifying the most informative measurement subspace. When the number of measurements is large, the proposed algorithm can be used to make an effective tradeoff between computational complexity and estimation accuracy. This algorithm also can be extended to other Kalman filters for measurement subspace selection.
Optimal filtering methods to structural damage estimation under ground excitation.
Hsieh, Chien-Shu; Liaw, Der-Cherng; Lin, Tzu-Hsuan
2013-01-01
This paper considers the problem of shear building damage estimation subject to earthquake ground excitation using the Kalman filtering approach. The structural damage is assumed to take the form of reduced elemental stiffness. Two damage estimation algorithms are proposed: one is the multiple model approach via the optimal two-stage Kalman estimator (OTSKE), and the other is the robust two-stage Kalman filter (RTSKF), an unbiased minimum-variance filtering approach to determine the locations and extents of the damage stiffness. A numerical example of a six-storey shear plane frame structure subject to base excitation is used to illustrate the usefulness of the proposed results. PMID:24453869
Optimal Filtering Methods to Structural Damage Estimation under Ground Excitation
Hsieh, Chien-Shu; Liaw, Der-Cherng; Lin, Tzu-Hsuan
2013-01-01
This paper considers the problem of shear building damage estimation subject to earthquake ground excitation using the Kalman filtering approach. The structural damage is assumed to take the form of reduced elemental stiffness. Two damage estimation algorithms are proposed: one is the multiple model approach via the optimal two-stage Kalman estimator (OTSKE), and the other is the robust two-stage Kalman filter (RTSKF), an unbiased minimum-variance filtering approach to determine the locations and extents of the damage stiffness. A numerical example of a six-storey shear plane frame structure subject to base excitation is used to illustrate the usefulness of the proposed results. PMID:24453869
Design and performance optimization of fiber optic adaptive filters.
Paparao, P; Ghosh, A; Allen, S D
1991-05-10
There is a great need for easy-to-fabricate and versatile fiber optic signal processing systems in which optical fibers are used for the delay and storage of wideband guided lightwave signals. We describe the design of the least-mean-square algorithm-based fiber optic adaptive filters for processing guided lightwave signals in real time. Fiber optic adaptive filters can learn to change their parameters or to process a set of characteristics of the input signal. In our realization we employ as few electronic devices as possible and use optical computation to utilize the advantages of optics in the processing speed, parallelism, and interconnection. Many schemes for optical adaptive filtering of electronic signals are available in the literature. The new optical adaptive filters described in this paper are for optical processing of guided lightwave signals, not electronic signals. We analyzed the convergence or learning characteristics of the adaptive filtering process as a function of the filter parameters and the fiber optic hardware errors. From this analysis we found that the effects of the optical round-off errors and noise can be reduced, and the learning speed can be comparatively increased in our design through an optimal selection of the filter parameters. A general knowledge of the fiber optic hardware, the statistics of the lightwave signal, and the desired goal of the adaptive processing are enough for this optimum selection of the parameters. Detailed computer simulations validate the theoretical results of performance optimization. PMID:20700365
Optimal Recursive Digital Filters for Active Bending Stabilization
NASA Technical Reports Server (NTRS)
Orr, Jeb S.
2013-01-01
In the design of flight control systems for large flexible boosters, it is common practice to utilize active feedback control of the first lateral structural bending mode so as to suppress transients and reduce gust loading. Typically, active stabilization or phase stabilization is achieved by carefully shaping the loop transfer function in the frequency domain via the use of compensating filters combined with the frequency response characteristics of the nozzle/actuator system. In this paper we present a new approach for parameterizing and determining optimal low-order recursive linear digital filters so as to satisfy phase shaping constraints for bending and sloshing dynamics while simultaneously maximizing attenuation in other frequency bands of interest, e.g. near higher frequency parasitic structural modes. By parameterizing the filter directly in the z-plane with certain restrictions, the search space of candidate filter designs that satisfy the constraints is restricted to stable, minimum phase recursive low-pass filters with well-conditioned coefficients. Combined with optimal output feedback blending from multiple rate gyros, the present approach enables rapid and robust parametrization of autopilot bending filters to attain flight control performance objectives. Numerical results are presented that illustrate the application of the present technique to the development of rate gyro filters for an exploration-class multi-engined space launch vehicle.
Sequential Bearings-Only-Tracking Initiation with Particle Filtering Method
Hao, Chengpeng
2013-01-01
The tracking initiation problem is examined in the context of autonomous bearings-only-tracking (BOT) of a single appearing/disappearing target in the presence of clutter measurements. In general, this problem suffers from a combinatorial explosion in the number of potential tracks resulted from the uncertainty in the linkage between the target and the measurement (a.k.a the data association problem). In addition, the nonlinear measurements lead to a non-Gaussian posterior probability density function (pdf) in the optimal Bayesian sequential estimation framework. The consequence of this nonlinear/non-Gaussian context is the absence of a closed-form solution. This paper models the linkage uncertainty and the nonlinear/non-Gaussian estimation problem jointly with solid Bayesian formalism. A particle filtering (PF) algorithm is derived for estimating the model's parameters in a sequential manner. Numerical results show that the proposed solution provides a significant benefit over the most commonly used methods, IPDA and IMMPDA. The posterior CramÃ©r-Rao bounds are also involved for performance evaluation. PMID:24453865
Optimization of filtering schemes for broadband astro-combs.
Chang, Guoqing; Li, Chih-Hao; Phillips, David F; Szentgyorgyi, Andrew; Walsworth, Ronald L; Kärtner, Franz X
2012-10-22
To realize a broadband, large-line-spacing astro-comb, suitable for wavelength calibration of astrophysical spectrographs, from a narrowband, femtosecond laser frequency comb ("source-comb"), one must integrate the source-comb with three additional components: (1) one or more filter cavities to multiply the source-comb's repetition rate and thus line spacing; (2) power amplifiers to boost the power of pulses from the filtered comb; and (3) highly nonlinear optical fiber to spectrally broaden the filtered and amplified narrowband frequency comb. In this paper we analyze the interplay of Fabry-Perot (FP) filter cavities with power amplifiers and nonlinear broadening fiber in the design of astro-combs optimized for radial-velocity (RV) calibration accuracy. We present analytic and numeric models and use them to evaluate a variety of FP filtering schemes (labeled as identical, co-prime, fraction-prime, and conjugate cavities), coupled to chirped-pulse amplification (CPA). We find that even a small nonlinear phase can reduce suppression of filtered comb lines, and increase RV error for spectrograph calibration. In general, filtering with two cavities prior to the CPA fiber amplifier outperforms an amplifier placed between the two cavities. In particular, filtering with conjugate cavities is able to provide <1 cm/s RV calibration error with >300 nm wavelength coverage. Such superior performance will facilitate the search for and characterization of Earth-like exoplanets, which requires <10 cm/s RV calibration error. PMID:23187265
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387
Nonlinear Statistical Signal Processing: A Particle Filtering Approach
Candy, J
2007-09-19
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.
A local particle filter for high dimensional geophysical systems
NASA Astrophysics Data System (ADS)
Penny, S. G.; Miyoshi, T.
2015-12-01
A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The standard Sampling Importance Resampling (SIR) particle filter is augmented with an observation-space localization approach, for which an independent analysis is computed locally at each gridpoint. The deterministic resampling approach of Kitagawa is adapted for application locally and combined with interpolation of the analysis weights to smooth the transition between neighboring points. Gaussian noise is applied with magnitude equal to the local analysis spread to prevent particle degeneracy while maintaining the estimate of the growing dynamical instabilities. The approach is validated against the Local Ensemble Transform Kalman Filter (LETKF) using the 40-variable Lorenz-96 model. The results show that: (1) the accuracy of LPF surpasses LETKF as the forecast length increases (thus increasing the degree of nonlinearity), (2) the cost of LPF is significantly lower than LETKF as the ensemble size increases, and (3) LPF prevents filter divergence experienced by LETKF in cases with non-Gaussian observation error distributions.
Lubricant wear particle analysis by filter patch extraction
Smart, C.L.
1996-07-01
Lubricating Oil Analysis (LOA) has become an important part of a comprehensive Reliability Centered Maintenance (RCM) program. However, knowing the condition of the lubricant alone does not provide a complete description of equipment reliability. Condition monitoring for equipment can be accomplished through Wear Particle Analysis (WPA). This usually involves separating suspended materials and wear products from the lubricant by magnetic (ferrographic) means. This paper will present a simple, low-cost, alternate method of particle acquisition called Filter Patch Extraction (FPE). This method removes solids, regardless of their composition, from the lubricant by vacuum filtration and deposits them onto a filter for microscopic examination similar to that of analytical ferrography. A large filter pore size retains suspended materials and permits rapid filtration of large volumes of lubricant thereby increasing the accuracy of the wear and cleanliness profile that can be established for a given machine. Qualitative trending of equipment wear and lubricant system cleanliness are easily performed with FPE. Equipment condition is determined by then characterizing the metal particles which are recovered. Examined filters are easily archived in filter holders for future reference. Equipment for FPE is inexpensive and readily available. The technique is field-portable, allowing WPA to be performed on-site, eliminating delays with remote laboratories while building customer participation and support. There are numerous advantages for using FPE in a machine condition monitoring program.
Assessment of optimally filtered recent geodetic mean dynamic topographies
NASA Astrophysics Data System (ADS)
Siegismund, F.
2013-01-01