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Recently, Particle Swarm Optimization (PSO) is introduced as an alternative to ParticleFilter (PF) for object tracking. In this work, we compare a PSO tracker with two PF trackers, a classical PF tracker and an Enhanced ParticleFilter (EPF) tracker, introduced in this paper. The accuracy of the tracking and, in particular, occlusion handling are considered. The different trackers are

Howida A. Abd El-Halym; Imbaby I. Mahmoud; A. AbdelTawab; D. Habib

ParticleFilter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of\\u000a PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating\\u000a stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony\\u000a Optimization is incorporated into the generic

In this paper, a novel discrete optimization approach is developed to optimally solve the optimization problem of power system shunt filter design based on discrete multi objective particle swarm optimization MOPSO technique to ensure harmonic current reduction and noise mitigation on electrical utility grid. In this novel optimization approach, multi objective particle swarm optimization MOPSO is implemented to tackle a

The paper presents a novel discrete search optimization and approach to solve the problem of the hybrid power filter compensator with design a C-type filter and fixed capacitor bank using discrete multi objective particle swarm optimization MOPSO method. This is to ensure both loss reduction and harmonic current mitigation on electrical utility grid. This novel optimization approach, a multi objective

Particlefilters have revolutionized object tracking in video sequences. The conventional particlefilter, also called the CONDENSATION filter, uses the state transition distribu- tion as the proposal distribution, from which the particles are drawn at each iteration. However, the transition distribution does not take into account the current observations, and thus many particles can be wasted in low likelihood regions.

A new method of genetic algorithm (GA) optimized the extended Kalman particlefilter (EKPF) is proposed in this paper. The algorithm of extended Kalman particlefilter is a suboptimal filtering algorithm with good performance for target tracking and non-linear tracking problem. In the implementation of the extended Kalman particlefilter, a re-sampling scheme is used to decrease the degeneracy phenomenon

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 particlefiltering, 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.

Dietenbeck, Thomas; Varray, François; Kybic, Jan; Basset, Olivier; Cachard, Christian

In this paper, we analyze the computational challenges in implementing particlefiltering, especially to video sequences. Particlefiltering is a technique used for filtering nonlinear dynamical systems driven by non-Gaussian noise processes. It has found widespread applications in detection, navigation, and tracking problems. Although, in general, particlefiltering methods yield improved results, it is difficult to achieve real time performance. In this paper, we analyze the computational drawbacks of traditional particlefiltering algorithms, and present a method for implementing the particlefilter using the Independent Metropolis Hastings sampler, that is highly amenable to pipelined implementations and parallelization. We analyze the implementations of the proposed algorithm, and, in particular, concentrate on implementations that have minimum processing times. It is shown that the design parameters for the fastest implementation can be chosen by solving a set of convex programs. The proposed computational methodology was verified using a cluster of PCs for the application of visual tracking. We demonstrate a linear speed-up of the algorithm using the methodology proposed in the paper. PMID:18390378

According to the effective sampling of particles and the particles impoverishment caused by re-sampling in particlefilter,\\u000a an improved particlefiltering algorithm based on observation inversion optimal sampling was proposed. Firstly, virtual observations\\u000a were generated from the latest observation, and two sampling strategies were presented. Then, the previous time particles\\u000a were sampled by utilizing the function inversion relationship between observation

In target tracking, if the dynamic model satisfies the Kalman filter assumptions, Kalman filter is optimal, Particlefilter is a second-best. Usually, systems are often unable to meet the best, at this time particlefilter is usually better than any other filtering method. In order to solve the degradation and deprivation of particlefilter in Iteration. This article introduces crossover

We present in this paper two improved particlefilter algorithms for ballistic target tracking. The first algorithm is a sampling\\/importance resampling (SIR) filter that uses an optimized importance function plus residual resampling to combat particle degeneracy, and also incorporates a Metropolis-Hastings (MH) move step to reduce particle impoverishment. The second proposed algorithm is an auxiliary particlefilter (APF). Both algorithms

This paper proposes a novel hybrid learning algorithm with stable learning laws for Adaptive Network-based Fuzzy Inference System (ANFIS) as a system identifier. The proposed hybrid learning algorithm is based on the particle swarm optimization (PSO) for training the antecedent part and the extended Kalman filter (EKF) for training the conclusion part. Lyapunov stability theory is used to study the

Mahdi Aliyari Shoorehdeli; Mohammad Teshnehlab; Ali Khaki Sedigh

In this paper, the particlefilter is used to solve the nonlinear and nonGaussian estimation problem in multiple targets tracking and multiple sensor fusion process. The weight of the particle is evaluated through the combination of Joint Probability Data Association (JPDA) and multiple hypothesis tracking (MHT), which makes the probabilistic assignment based on all reasonable hypotheses in a sliding window

We present a new unscented particlefilter for dynamic systems that outperforms the general particlefilter and the unscented particlefilter when the variance of the observation noise is small. Our algorithm uses a bank of unscented Kalman filters to refine the prediction in particlefilter. The key difference with the traditional unscented particlefilter is the introduction of an

This paper proposes Hybrid Genetic Algorithm (GA)-Adaptive Particle Swarm Optimization (APSO) aided Unscented Kalman Filter (UKF) to estimate the harmonic components present in power system voltage/current waveforms. The initial choice of the process and measurement error covariance matrices Q and R (called tuning of the filter) plays a vital role in removal of noise. Hence, hybrid GA-APSO algorithm is used to estimate the error covariance matrices by minimizing the Root Mean Square Error(RMSE) of the UKF. Simulation results are presented to demonstrate the estimation accuracy is significantly improved in comparison with that of conventional UKF.

Several particle membrane filters (8×8 mm2) with circular, hexagonal and rectangular through holes are reported. By varying hole dimensions from 6 to 12 ?m, opening factors from 4 to 45% are achieved. In order to improve the filter robustness, a composite silicon nitride\\/Parylene membrane technology is developed. More importantly, fluid dynamic performance of the filters is also studied by both

X. Yang; J. M. Yang; X. Q. Wang; E. Meng; Y. C. Tai; C. M. Ho

We derive optimalfilters on the sphere in the context of detecting compact objects embedded in a stochastic background process. The matched filter and the scale adaptiv e filter are derived on the sphere in the most general setting, allowing for directional template profiles and filters. The p erfor- mance and relative merits of the two optimalfilters are discu

Jason D. Mcewen; Michael P. Hobson; Anthony N. Lasenby

We generalize the theory of particle flow to stabilize the nonlinear filter. We have invented a new nonlinear filter that is vastly superior to the classic particlefilter and the extended Kalman filter (EKF). In particular, the computational complexity of the new filter is many orders of magnitude less than the classic particlefilter with optimal estimation accuracy for problems with dimension greater than 4. Our accuracy is typically several orders of magnitude better than the EKF for nonlinear problems. We do not resample, and we do not use any proposal density from an EKF or UKF or other filter. Moreover, our new algorithm is deterministic, and we do not use any MCMC methods; this is a radical departure from other particlefilters. 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 particlefilters, namely "particle degeneracy." In addition, we explicitly stabilize our particlefilter using negative feedback, unlike standard particlefilters, which are generally very inaccurate for plants with slow mixing or unstable dynamics. This stabilization improves performance by several orders of magnitude for difficult problems.

In order to improve tracking estimation accuracy of existing unscented Kalman particlefilter (UPF), an improved particlefilter algorithm based on iterative measurement update UKF is proposed. The algorithm uses maximum posteriori estimate of iterative unscented Kalman filter as the important density function of the particlefilter and amends the state covariance using Levenberg-Marquardt method. So the observed information of

Tracking of a ballistic target in its reentry phase by considering the radar measurements is a highly complex problem in nonlinear filtering. Kalman Filter (KF) is used to estimate the positions of the target when the measurements are corrupted with noise. If the measurements (range and bearing) are nonlinear then Unscented Kalman filter (UKF) can be used. For obtaining reliable

Sensor management in support of Level 1 data fusion (multisensor integration), or Level 2 data fusion (situation assessment) requires a computationally tractable multitarget filter. The theoretically optimal approach to this multi-target filtering is a suitable generalization of the recursive Bayes nonlinear filter. However, this optimalfilter is intractable and computationally challenging that it must usually be approximated. We report on the approximation of a multi-target non-linear filtering for Sensor Management that is based on the particlefilter implementation of Stein-Winter probability hypothesis densities (PHDs). Our main focus is on the operational utility of the implementation, and its computational efficiency and robustness for sensor management applications. We present a multitarget ParticleFilter (PF) implementation of the PHD that include clustering, regularization, and computational efficiency. We present some open problems, and suggest future developments. Sensor management demonstrations using a simulated multi-target scenario are presented.

El-Fallah, A.; Zatezalo, A.; Mahler, R.; Mehra, R. K.; Alford, M.

Based on the concept of sequential importance sampling (SIS) and the use of Bayesian theory, particlefilter is particularly useful in dealing with nonlinear and non-Gaussian problems. In this paper, a new particlefilter is proposed that uses a divided difference filter to generate the importance proposal distribution is proposed. The proposal distribution integrates the latest measurements into system state

This paper investigates an approximation scheme of the optimal nonlinear Bayesian filter based on the Gaussian mixture representation of the state probability distribution function. The resulting filter is similar to the particlefilter, but is different from it in that, the standard weight-type correction in the particlefilter is complemented by the Kalman-type correction with the associated covariance matrices in

The particlefiltering (PF) is a recursive sub-optimal Bayesian estimator. The multiple model particlefiltering (MMPF) has been proposed for tracking a maneuvering target. In a cluttered environment, probabilistic data association (PDA) is incorporated into MMPF to overcome the measurement-origin uncertainty. While the particlefiltering is fairly easy to implement, its main drawback is that it is quite computation intensive,

This report summarizes the results obtained during the contract No. F 19628-88-K-0018 entitled 'Optimal Phase-only Filters'. This research was focused on Phase-only Filters (POFs) and Binary Phase-only Filters (BPOFs). We prove in this report that the con...

Optimalfiltering equations are obtained for very general linear stochastic delay systems. Stability of the optimalfilter 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 optimalfilter is proved. Finally, the cascade of the optimalfilter and the deterministic optimal quadratic control system is shown to be asymptotically stable as well.

Particlefilter (PF) is widely used in nonlinear\\/non-Gaussion environments to solve the simultaneous localization and mapping (SLAM) problem. But the standard PF suffers a lot from the sample impoverishment after resampling. This paper introduces a particle splitting technique before the resampling process, called pre-resampling. This method splits particles with big importance weight into several particles with small importance weight. The

Expressions are derived for real filters that have a maximum correlation signal to noise ratio. Both continuous and discrete cases are treated and shown to have similar forms. The signal can be complex, and the case of a real signal is considered and related to previous results.

Juday, Richard D.; Vijaya Kumar, B. V. K.; Rajan, P. Karivaratha

This paper provides an overview of currently available methods for state estimation of linear, constrained and nonlinear systems. The following methods are discussed: Kalman filtering, extended Kalman filtering, unscented Kalman filtering, particlefiltering, and moving horizon estimation. The current research literature on particlefiltering and moving horizon estimation is reviewed, and the advantages and disadvantages of these methods are presented.

In this paper, a new extended Kalman particlefilter based information fusion is proposed for state estimation problem of nonlinear and non-Gaussian systems. It uses extended Kalman filter algorithm to update particles in particlefilter, with which the local state estimated values can be calculated. The multi-sensor information fusion filter is obtained by applying the standard linear minimum variance fusion

The generic particlefilter has been applied with success to neural network training, but the proposal distribution chosen by the generic particlefilter does not incorporate the latest observations which can deteriorate the performance of the algorithm. In this paper, we propose to use the iterated extended Kalman filter to generate proposal distribution in particlefiltering framework. The iterated extended

This paper presents a new method to draw particles for the particlefilter in the case of large state noise. The standard bootstrap filter draw particles randomly from the prior density which does not use the latest infor- mation of the observation. Some improvements consist in using extended Kalman filter or unscented Kalman filter to produce the importance distribution in

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.

A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life

The distributed SLAM system has a similar estimation performance and requires only one-fifth of the computation time compared with centralized particlefilter. However, particle impoverishment is inevitably because of the random particles prediction and resampling applied in generic particlefilter, especially in SLAM problem that involves a large number of dimensions. In this paper, particlefilter use in distributed SLAM was improved in two aspects. First, we improved the important function of the local filters in particlefilter. The adaptive values were used to replace a set of constants in the computational process of importance function, which improved the robustness of the particlefilter. 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 particlefilter to prove that improved particlefilter converges to the optimalfilter 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.

A new improved particlefilter algorithm with the simplified UT (unscented transformation) and the modified unscented Kalman filter (UKF) proposal distribution is presented. The scaling factor is added to adaptively estimate on line and to improve the filtering performance. An adaptive algorithm is developed. In the bearings-only tracking experiments, the results confirm the improved particlefilter algorithm outperforms others.

In nonlinear and non-Gaussian systems, particlefiltering is effective but it is difficult to select the importance distribution function and diverges more greatly. Aiming at this problem, the paper represents robust unscented regularized particlefiltering to improve the performance of filtering. This algorithm is more suitable for filtering calculation in nonlinear system, not only because overcomes the limitations of the

In this paper, we used particlefilter to motion estimation algorithm on real-time for mobile surveillance robot. Particlefilter based on the Monte Carlo's sampling method, be used Bayesian conditional probability model which having prior distribution probability and posterior distribution probability. By using particlefilter, it can be possible to tracking and estimating robustly for object's motion and movement. Also

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 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 pa...

Ground moving target indicator (GMTI) tracking is often carried out using extended Kalman filters, as in the variable-structure interacting multiple-model (VS-IMM) filter. In some scenarios, however, this is considered to be inadequate. It has been shown that in this case, a particlefilter can give better performance. Such a filter, the variable-structure multiple-model particlefilter (VS-MMPF), is given in the

Particlefilter has many variations, one of which is the unscented particlefilter. The unscented particlefilter uses the unscented Kalman filter to generate particles in the particlefiltering framework. This method can give better performance than the standard particlefilter in some practical problems that are raised in computer vision field. But one critical issue in the unscented particle

In order to improve tracking estimation accuracy of square-root unscented Kalman particlefilter (SRUKFPF), a new particlefilter algorithm of update SRUKF based on iterated measurements is proposed. The algorithm produces the important density function of particlefilter using maximum posteriori estimate of iterated square-root unscented Kalman filter, and amends the state covariance using Levenberg-Marquardt method, so that the observed

In the literature, there are claims stating that particlefilters cannot be used for high dimensional systems because their random measures degenerate to single particles. While this may be true for standard implementations of particlefiltering, it may not be true for alternative implementations. In this paper we build on our previous work for tracking multiple targets with multiple particle

We propose a computationally efficient particlefiltering al go- rithm that adaptively chooses between the sequential impor- tance resampling (SIR) particlefilter and the unscented par - ticle filter (UPF). The technique is based on the use of the Kullback-Leibler distance (KLD) sampling and the choice of either of the algorithms is governed by the error in estimati on. The

Bhavana B Manjunath; Antonia Papandreou-Suppappola; Chaitali Chakrabarti; Darryl Morrell

In view of the problem that face tracker based on particlefiltering using only histogram cue is frequently disturbed by background, a particle swarm optimizationparticlefiltering(PSOPF) face tracking algorithm is proposed. An AdaBoost classifier is used to initialize the target tracking and update the template. To solve the problem of degeneration, the distribution of particles is optimized by PSO.

As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian\\u000a filtering, particlefilters (PP) are favored by researchers and widely applied in many fields. Based on particlefiltering,\\u000a an improved extended Katman filter (EKF) proposal distribution is presented. Evaluation of the weights is simplified and other\\u000a improved techniques including the

The Condensation algorithm, developed for visual tracking, is a variant of particlefilter. In the sampling stage of Condensation, no use is made of the information from the current frame in the image sequence. As a consequence, the algorithm requires a large number of particles and is computationally expensive. In this paper, a Kalman particlefilter (KPF) and an unscented

As a hot research topic, particlefilter (PF), has been successfully applied into many fields. Combined with the analysis of partial stratified resampling (PSR) algorithm, two kinds of improved PF algorithm are presented. One improved PF algorithm with weights optimization is to use the optimal idea to improve the weights after implementing PSR resampling so as to enhance the performance

In order to overcome the flaw that it is hard to get the optimization importance density function in the particlefilter. The IEKF and the sequential fusion were integrated with particlefilter. Than, the particlefilter was introduced to radar\\/infrared Multi-sensor target fusion tracking. The main idea is use the system state transition matrix and the error covariance matrix which

Li Qian; Feng Jin-fu; Peng Zhi-zhuang; Lu Qing; Liang Xiao-long

Particle swarm optimization (PSO) has undergone many changes since its introduction in 1995. As researchers have learned about\\u000a the technique, they have derived new versions, developed new applications, and published theoretical studies of the effects\\u000a of the various parameters and aspects of the algorithm. This paper comprises a snapshot of particle swarming from the authors’\\u000a perspective, including variations in the

In this paper we present an approach for tracking with a high-bandwidth active sensor in very long range scenarios. We show that in these scenarios the extended Kalman filter is not desirable as it suffers from major consistency problems; and most flavors of particlefilter suffer from a loss of diversity among particles after resampling. This leads to sample impoverishment and the divergence of the filter. In the scenarios studied, this loss of diversity can be attributed to the very low process noise. However, a regularized particlefilter is shown to avoid this diversity problem while producing consistent results. The regularization is accomplished using a modified version of the Epanechnikov kernel.

A framework for positioning, navigation, and tracking problems using particlefilters (sequential Monte Carlo methods) is developed. It consists of a class of motion models and a general nonlinear measurement equation in position. A general algorithm is presented, which is parsimonious with the particle dimension. It is based on marginalization, enabling a Kalman filter to estimate all position derivatives, and

Fredrik Gustafsson; Fredrik Gunnarsson; Niclas Bergman; Urban Forssell; Jonas Jansson; Rickard Karlsson; Per-Johan Nordlund

Particlefilters have been introduced as a powerful tool to es- timate the posterior density of nonlinear systems. These fil- ters are also capable of processing data online as required in many practical applications. In this paper, we propose a novel technique for video stabilization based on the particlefilter- ing framework. Scale-invariant feature points are extracted to form a

Junlan Yang; Dan Schonfeld; Chong Chen; Magdi Mohamed

In an earlier contribution a particlefilter for underwater (UW) navigation is proposed, and applied to an experimental trajectory. This paper focuses on performance improvements and analysis. First, the Cramer Rao lower bound (CRLB) along the experimental trajectory is computed, which is only slightly lower than the particlefilter estimate after initial transients. Simple rule of thumbs for how performance

Recently, Particlefilter becomes the most popular approach in mobile robot localization and has been applied with great success to a variety of state estimation problems. In this paper, the particlefilter is applied in position tracking and global localization. Moreover, the posterior distribution of robot pose in global localization is usually multimodal due to the symmetry of the environment

In this paper, we propose a novel framework of learning that uses a particlefilter. In a real-world situation, it is difficult to express a continuous state and a continuous action. The problem is solved by using our particlefilter, which is one of the methods for dividing a continuous state and a continuous action. Our method needs only a

In this paper, we propose a novel fuzzy particlefiltering method for online estimation of nonlinear dynamic systems with fuzzy uncertainties. This approach uses a sequential fuzzy simulation to approximate the possibilities of the state intervals in the state-space, and estimates the state by fuzzy expected value operator. To solve the degeneracy problem of the fuzzy particlefilter, one corresponding

A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively

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.

R.A. Newby; M.A. Alvin; G.J. Bruck; T.E. Lippert; E.E. Smeltzer; M.E. Stampahar

This paper provides a brief history of some operational particlefilters that were used by the U. S. Coast Guard and U. S. Navy. Starting in 1974 the Coast Guard system provided Search and Rescue Planning advice for objects lost at sea. The Navy systems were used to plan searches for Soviet submarines in the Atlantic, Pacific, and Mediterranean starting in 1972. The systems operated in a sequential, Bayesian manner. A prior distribution for the target"s location and movement was produced using both objective and subjective information. Based on this distribution, the search assets available, and their detection characteristics, a near-optimal search was planned. Typically, this involved visual searches by Coast Guard aircraft and sonobuoy searches by Navy antisubmarine warfare patrol aircraft. The searches were executed, and the feedback, both detections and lack of detections, was fed into a particlefilter to produce the posterior distribution of the target"s location. This distribution was used as the prior for the next iteration of planning and search.

Richardson, Henry R.; Stone, Lawrence D.; Monach, W. Reynolds; Discenza, Joseph H.

This paper provides a brief history of some operational particlefilters that were used by the U. S. Coast Guard and U. S. Navy. Starting in 1974 the Coast Guard system provided Search and Rescue Planning advice for objects lost at sea. The Navy systems were used to plan searches for Soviet submarines in the Atlantic, Pacific, and Mediterranean starting in 1972. The systems operated in a sequential, Bayesian manner. A prior distribution for the target"s location and movement was produced using both objective and subjective information. Based on this distribution, the search assets available, and their detection characteristics, a near-optimal search was planned. Typically, this involved visual searches by Coast Guard aircraft and sonobuoy searches by Navy antisubmarine warfare patrol aircraft. The searches were executed, and the feedback, both detections and lack of detections, was fed into a particlefilter to produce the posterior distribution of the target"s location. This distribution was used as the prior for the next iteration of planning and search.

Richardson, Henry R.; Stone, Lawrence D.; Monach, W. Reynolds; Discenza, Joseph H.

Quantum mechanical systems exhibit an inherently probabilistic nature upon measurement which excludes in principle the singular direct observability case. Quantum theory of time continuous measurements and quantum filtering developed by VPB on the basis of semi-Markov independent increment models for quantum noise and quantum nondemolition (QND) observability is generalized for demolition indirect measurements of quantum unstable systems satisfying the microcausality principle. The reduced quantum feedback-controlled dynamics is described both by linear semi-Markov and nonlinear conditionally-Markov stochastic master equations. Using this scheme for diffusive and counting measurement to describe the stochastic evolution of the open quantum system under the continuous indirect observation and working in parallel with classical indeterministic control theory, we show the conditionally-Markov Bellman equations for optimal feedback control of the a posteriori stochastic quantum states conditioned upon these measurements. The resulting Bellman equation for the diffusive observation is then applied to the explicitly solvable quantum linear-quadratic-Gaussian (LQG) problem which emphasizes many similarities with the corresponding classical control problem.

This paper presents a novel method of estimating the optimal steady state Kalman filter gain of a linear discrete time-invariant system from a non-optimal Kalman filter residual sequence. The relation between the optimal residual sequence and a signal derived from the non-optimal residual sequence is described by a Moving Average (MA) model whose coefficients are expressed in terms of the state space parameters and the optimal steady state Kalman filter gain. In order to identify the MA model, a whitening filter of the derived signal, which corresponds to an AutoRegressive (AR) model of the signal, is first identified using the least-squares method. Then the inverse filter of the whitening filter, which corresponds to the MA model, is calculated. From the coefficients of the identified MA model, the optimal steady state Kalman filter gain can be obtained. Numerical example is provided to illustrate the feasibility of this approach.

In this paper, a novel algorithm called Receding Horizon Kalman ParticleFilter (RHKPF) has been proposed and is applied to our improved fingerprint-based WLAN vehicle positioning system. The RHKPF is a particlefilter that the optimal importance density is approximated by incorporating the most current measurement through a Receding Horizon Kalman Filter (RHKF), for that the RHKF is believed to

Liqiang Xu; Xingchuan Liu; Sheng Zhang; Xiaokang Lin

We address the problem of object tracking encountered in video processing. The proposed approach is mainly composed of object modeling, the improved particlefilter and mixture filtering. First, each of visual objects can be modeled by multi-part color likelihood model. To tackle self-occlusion of the tracked objects, the color distribution representing the tracked object can be updated over time. We

In order to solve the problem of degeneracy in particlefiltering algorithm, a novel proposal distribution is designed in this paper. The principal idea of the proposal distribution is to fuse the latest observations together with the previous filtering estimate and the prior model information. In that case, the one-step smoothing estimate of the state is employed. Simulation results show

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple.

Particlefilter has been widely applied into many fields in recent years. Combined with the deficiency analysis of particlefilter, an improved particlefilter based on diversity guidance is proposed. Firstly, the adaptive resampling step in particlefilter is tuned based on two diversity measures which are effective sample size and population diversity factor. Moreover, the operation of particle mutation

Aiming at the particle degeneracy caused by the introduction of model information in particle sampling process, a novel multiple model particlefiltering algorithm based on two stage prediction update is proposed. In the multiple model particlefiltering framework, the dynamic combination of the prediction and update mechanism of particlefilter and Kalman filter is realized by the reasonable arrangement of

Hu Zhen-tao; Yang Feng; Pan Quan; Li Xiao-wei; Chen Yan-jun

The movement model of maneuvering targets is analyzed, and based on the fact that the interacting multiple models in extended Kalman Filter and Unscented Kalman Filter have a low precision of tracking strong maneuvering targets, particlefilter is introduce into interacting multi-model, so that particlefiltering of every model can be realized through particlefiltering algorithm. The simulation result shows

The marginalized particlefilter is a powerful combination of the particlefilter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper outlines the marginalized particlefilter and very briefly hint at pos- sible generalizations, giving rise to a larger family of marginalized nonlinear filters. Furthermore, we analyze

Thomas B. Schon; Rickard Karlsson; Fredrik Gustafsson

In order to improve the particle degeneracy phenomenon of particlefilter, a method for particlefiltering based on unscented transformation was proposed. The spherical unscented Kalman filter was used to generate the important distribution for particlefilter. The important distribution integrated the latest observation, so it can extend the overlaps of itself and posterior probability density and well approximate the

The objective of this work is to analyze the improvement in the performance of the particlefilter by including a resample-move step or by using a modified Gaussian particlefilter. Specifically, the standard particlefilter structure is altered by the inclusion of the Markov chain Monte Carlo move step. The second choice adopted in this study uses the moments of an ensemble Kalman filter analysis to define the importance density function within the Gaussian particlefilter structure. Both variants of the standard particlefilter are used in the assimilation of densely sampled discharge records into a conceptual rainfall-runoff model. In order to quantify the obtained improvement, discharge root mean square errors are compared for different particlefilters, as well as for the ensemble Kalman filter. First, a synthetic experiment is carried out. The results indicate that the performance of the standard particlefilter can be improved by the inclusion of the resample-move step, but its effectiveness is limited to situations with limited particle impoverishment. The results also show that the modified Gaussian particlefilter outperforms the rest of the filters. Second, a real experiment is carried out in order to validate the findings from the synthetic experiment. The addition of the resample-move step does not show a considerable improvement due to performance limitations in the standard particlefilter with real data. On the other hand, when an optimal importance density function is used in the Gaussian particlefilter, the results show a considerably improved performance of the particlefilter.

Plaza Guingla, D. A.; Pauwels, V. R.; De Lannoy, G. J.; Matgen, P.; Giustarini, L.; De Keyser, R.

The paper presents a modified particle swarm optimization (PSO) algorithm for engineering optimization problems with constraints. PSO is started with a group of feasible solutions and a feasibility function is used to check if the newly explored solutions satisfy all the constraints. All the particles keep only those feasible solutions in their memory. Several engineering design optimization problems were tested

Several new algorithms are presented for the optimal approximation and design of various classes of digital filters. An iterative algorithm is developed for the efficient design of unconstrained and constrained infinite impulse response (IIR) digital filters. Both in the unconstrained and constrained cases, the numerator and denominator of the filter transfer function are designed iteratively by recourse to the Remez algorithm and to appropriate design parameters and criteria, at each iteration. This makes it possible for the algorithm to be implemented by means of a short main program which uses (at each iteration) the linear phase FIR filter design algorithm of McClellan et al. as a subroutine. The approach taken also permits the filter to be designed with a desired ripple ratio. Also, the algorithm determines automatically the minimum passband ripple corresponding to the prescribed orders and band edges of the filter. The filter is designed directly without guessing the passband ripple or stopband ripple. Another algorithm, based on similar principles, is developed for the design of a nonlinear phase finite impulse response (FIR) filter, whose transfer function optimally approximates a desired magnitude response, there being no constraints imposed on the phase response. A similar algorithm is presented for the design of two new classes of FIR digital filters, one linear phase and the other nonlinear phase. A filter of either class has significantly reduced number of multiplications compared to the one obtained by its conventional counterpart, with respect to a given frequency response. In the case of linear phase, by introducing the new class of digital filters into the design of multistage decimators and interpolators for narrow-band filter implementation, it is found that an efficient narrow-band filter requiring considerably lower multiplication rate than the conventional linear phase FIR design can be obtained. The amount of data storage required by the new class of nonlinear phase FIR filters is significantly less than its linear phase counterpart. Finally, the design of a (finite-impulse-response) FIR digital filter with some of the coefficients constrained to zero is formulated as a linear programming (LP) problem and the LP technique is then used to design this class of constrained FIR digital filters. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI.

In order to improve the real-time performance of particlefilter, this paper proposes an efficient particlefilter 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

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimalfilter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated

We consider the classical problem of phase lock on frequency modulation (FM) signals using the recent methods of particlefilters (PF). This problem is nonlinear in nature, and building an optimalfilter is impossible. State-of-the-art suboptimal phase estimators are based on phase lock loops (PLL) which are equivalent to an extended Kalman filter (EKF) realization. We show that applying PF

Particlefilters 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 particlefilter (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 Würsch, 2012). For this model, the efficient particlefilter that includes nudging the variables shows significant improvement compared to Ensemble Kalman Filter and Sequential Importance Resampling (SIR) particlefilter. 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 (Würsch and Craig 2013), which contains more realistic dynamical characteristics of convective scale phenomena. Using the efficient particlefilter and different combination of observations of the three field variables (wind, water 'height' and rain) allows the particlefilter 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 particlefilter outperforms nudging alone. References: Craig, G. C. and M. Würsch, 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. Würsch, M. and G. C. Craig, 2013: A simple dynamical model of cumulus convection for data assimilation research, submitted to Met. Zeitschrift.

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 particlefilter, 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.

The idea of particlefilter is to represent probability density function (PDF) of nonlinear\\/non-Gaussian system by a set of random samples. One of the key issue of particlefilter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particlefilter. The proposal distributions integrate the current observation, thus

In wireless communications, a joint channel coefficient and time-delay tracking technique are a critical issue. Due to the highly nonlinear nature of time delay estimation, Particlefilter (PF) and Sigma Point Particlefilter (SPPF) can be employed. The SPPF algorithm consists of a particlefilter that uses an Sigma Point Kalman filter (SPKF) to generate the importance proposal distribution. The

In order overcome the particle degradation and non- adjusted online in the traditional particlefilter algorithm, an adaptive unscented particlefilter algorithm based on predicted residual is proposed. The algorithm adopts a new proposal distribution combing the unscented kalman filter with the adaptive factor. The algorithm uses Unscented Kalman filter to generate a proposal distribution, in which the covariance of

Particlefilters for data assimilation are usually presented in terms of an Ito stochastic ordinary differential equation (SODE). The task is to estimate the state a(t) of the SODE, with additional information provided by noisy observations bn, n=1,2,..., of this state. In principle, the solution of this problem is known: the optimal estimate of the state is the expected value of the solution of the model conditioned on available observations. The conditional mean can be calculated once the conditional probability density function (pdf) pn+1=p(a(tn+1)|b0,..., bn) is known. A particlefilters approximates pn+1 by sequential Monte Carlo. A Sampling-Importance-Resampling (SIR) filter constructs, at each time tn, a prior density by following replicas of the model (called particles). The prior is updated by sampling weights determined by the observations bn+1, to yield a posterior density that approximates pn+1. Because the observations are not used when constructing the prior, particles paths are very likely to stray into regions of low probability and the number of particles required can grow catastrophically, especially if the dimension of the SODE is large. The implicit particlefilter is a new sequential Monte Carlo method to solve the data assimilation problem. The filter was devised for large dimensional, non-linear, and non-Gaussian problems in which current methods fail or yield poor results. In essence, the implicit filter reverses the standard procedure described above. It first assigns a probability to each particle and then finds a sample that assumes it. This reversed procedure focusses all particles towards the observations and, thus, generates a thin particle beam to keep the particles in the high probability domain. Because the filter produces high probability samples only, the number of particles required remains manageable. We present a new and very efficient implementation of the implicit particlefilter for use in large dimensional data assimilation problems. Our implementation relies on a clever non-linear change of variables. The change of variables reduces a data assimilation problem of arbitrary size to solving a single algebraic equation in only one variable. We demonstrate the performance of our filter by applying it to the stochastic Kuramoto-Sivashinsky (SKS) equation. This equation is known to exhibit space-time chaos. We project its solution into an N-dimensional subspace spanned by N Fourier modes to obtain an N-dimensional Ito-Galerkin approximation. We vary the viscosity and the continuity (in space) of the noise process driving the equation to generate a variety of test problems, with dimensions ranging from 32 to 512. Linear and nonlinear observation operators are considered. We also outline how to deal with observations that are sparse in space and time. The performance of the implicit filters is compared to the performance of an SIR filter. The numerical results confirm that the implicit filter gives accurate state estimates by tracking only very few, but sharply focused, particles. The implicit filter also outperforms SIR in all cases considered.

The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic\\u000a behavior such as particlefilters exist, but they are computationally too expensive when working with high-dimensional systems.\\u000a The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the\\u000a samples are not guaranteed

Andreas S. Stordal; Hans A. Karlsen; Geir Nævdal; Hans J. Skaug; Brice Vallès

In this paper, we propose two improved particlefiltering schemes for target tracking, one based on a gradient proposal and the other based on the turbo principle. We present the basic ideas and derivations and show detailed results of three tracking applications. Favorable experimental findings have shown the efficiency of our proposed schemes and their potential in other tracking scenarios.

Particlefilter (PF) is an important way for target tracking in wireless sensor network (WSN). In the paper we proposed an improved particlefilter algorithm which outperforms general PF when target suddenly changes movement direction. Our algorithm used estimated direction of motion based on the current measurements to optimize the prediction in PF. It modified the deviation of estimated mean

3D human motion analysis system is gaining more and more popularity and importance in sports training, game simulation and many other areas. Particlefilter algorithm, as a powerful optimized method, can be applied to 3D human motion analysis system with more accurate results delivered and assured. An improved (hybrid) particlefilter algorithm (IPFA) is proposed in this paper which integrates

The Minimum Euclidean Distance OptimalFilter 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 optimizesfilters 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 recent trend in automotive industry is adding electrical drive systems to conventional drives. The electrification allows an expansion of energy sources and provides great opportunities for environmental friendly mobility. The electrical powertrain and its components can also cause disturbances which couple into nearby electronic control units and communication cables. Therefore the communication can be degraded or even permanently disrupted. To minimize these interferences, different approaches are possible. One possibility is to use EMC filters. However, the diversity of filters is very large and the determination of an appropriate filter for each application is time-consuming. Therefore, the filter design is determined by using a simulation tool including an effective optimization algorithm. This method leads to improvements in terms of weight, volume and cost.

The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimalfilter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimalfilter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimalfilter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters operating in two orthogonal directions. The implementation is very simple and computationally efficient, has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation.

Fault diagnosis is a major problem in industrial systems, and is of primary interest for mobile and industrial robotics where electric motors are used. In this paper fault diagnosis with the use of the Kalman filter is compared to fault diagnosis based on particlefilter. The Kalman filter assumes linear model representation and Gaussian measurement noise whereas the particlefilter

Scope of study. Flow non-uniformity in the automotive filter has a great impact on the filter performance. Therefore, study of the flow distribution as well as the dust particle concentration in the filter housing is very important for improvement of automotive filter design. This study focuses on particle counting and sizing techniques with Laser Doppler Velocimetry (LDV) and their application

Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particlefilter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article

In machine vision, color tracking is a well known problem. The Kalman filter or particlefilter are often used to build color tracking algorithms. The Kalman filter is good in tracking a linear system, but it often misses the object when the object changes its direction suddenly. In this case, the particlefilter is used but it fails easily when

We derive a new algorithm for computing Bayes' rule using particle flow that has zero curvature. The flow is computed by solving a vector Riccati equation exactly in closed form rather than solving a PDE, with a significant reduction in computational complexity. Our theory is valid for any smooth nowhere vanishing probability densities, including highly multimodal non-Gaussian densities. We show that this new flow is similar to the extended Kalman filter in the special case of nonlinear measurements with Gaussian noise. We also outline more general particle flows, including: constant curvature, geodesic flow, non-constant curvature, piece-wise constant curvature, etc.

An ‘inconsistent’ particlefilter produces – in a statistical sense – larger estimation errors than predicted by the model on which the filter is based. Inconsistent behavior of a particlefilter can be detected online by checking whether the predicted measurements (derived from the particles that represent the one-step-ahead prediction pdf) comply in a statistical sense with the observed measurements.

Precise estimation of the position of robots, which is essential in mobile robotics, is difficult. However, particlefilter shows great promise in such area. The number of samples is closely related to the operation time in particlefiltering. The main issue in real-time situation with regard to particlefiltering is to reduce the operation time, which led to the development

We present a novel approach for improving particlefilters for multi-target tracking. The suggested approach is based on drift homotopy for stochastic differential equations. Drift homotopy is used to design a Markov Chain Monte Carlo step which is appended to the particlefilter and aims to bring the particlefilter samples closer to the observations while at the same time

This paper proposes a fast target tracking method in which particlefilter is improved using Gaussian kernel and evolutionary strategy. We use Gaussian kernel function to replace the Dirac kernel function, which can decrease the degeneracy problem of the traditional particlefilter partly. To further improve the performance of particlefilter, we introduce evolutionary strategy into the process of Gaussian

Qicong Wang; Wenxiao Jiang; Chenhui Yang; Yunqi Lei

A fuzzy system is implemented to dynamically adapt the inertia weight of the particle swarm optimization algorithm (PSO). Three benchmark functions with asymmetric initial range settings are selected as the test functions. The same fuzzy system has been applied to all three test functions with different dimensions. The experimental results illustrate that the fuzzy adaptive PSO is a promising optimization

The particle swarm optimization (PSO), new to the electromagnetics community, is a robust stochastic evolutionary computation technique based on the movement and intelligence of swarms. This paper introduces a conceptual overview and detailed explanation of the PSO algorithm, as well as how it can be used for electromagnetic optimizations. This paper also presents several results illustrating the swarm behavior in

Particlefiltering is briefly introduced first. Because the depletion of particle diversity resulted from re-sampling causes the decline of filtering precision, an improved particlefiltering algorithm based on consensus fusion sampling is proposed. After the re-sampling process, the new algorithm extracts candidate particles based on Markov Chain Monte Carlo (MCMC) principle and combines the re-sampling particles to construct a candidate

An improved Rao-Blackwellized particlefiltering (RBPF) is proposed track the mobility of mobile station (MS) in mixed line-of-sight (LOS) or non-line-of-sight (NLOS) conditions in cellular network. The algorithm first estimates the sight condition state using particlefiltering method, in which particles are sampled by the optimal trial distribution and selected by one-step backward prediction. Then, by applying decentralized extended Kalman

Particlefilters are ensemble-based state-estimation techniques that in principle approximate the correct Bayesian analysis step for general non-Gaussian probability distributions. We investigate the ensemble size necessary for the particlefilter as the state dimension increases. For the simplest particle-filter algorithm, in which the prior distribution (i.e. the distribution of the state at the present time, conditioned on previous observations) is used a the proposal distribution, simulations and asymptotic analysis (following Bengtsson, Bickel and collaborators) demonstrate that the required ensemble size scales exponentially with a certain measure of the problem size. When each component of the state vector is independent, Gaussian, and of unit variance and the observations are of each state component separately with independent, Gaussian errors, the required ensemble size scales exponentially with the state dimension and simulations show that at least 1011 members when applied to a 200-dimensional state. In more general cases, the asymptotic theory reveals that the ensemble size must scale exponentially with the variance of the observation log likelihood rather than with the state dimension per se. A proposal density sufficiently close to the correct posterior would alleviate these difficulties, although there is no theoretical guidance for what sufficiently close means as the state dimension increases. Simulations indicate that the "optimal" proposal density of Doucet, which minimizes the variance of the particle weights after resampling, also suffers from an exponential increase of the necessary ensemble size.

Snyder, C.; Bengtsson, T.; Bickel, P.; Anderson, J.

In this paper, we propose a particlefiltering approach for the problem of registering two point sets that differ by a rigid body transformation. Typically, registration algorithms compute the transformation parameters by maximizing a metric given an estimate of the correspondence between points across the two sets of interest. This can be viewed as a posterior estimation problem, in which the corresponding distribution can naturally be estimated using a particlefilter. In this work, we treat motion as a local variation in pose parameters obtained by running a few iterations of a certain local optimizer. Employing this idea, we introduce stochastic motion dynamics to widen the narrow band of convergence often found in local optimizer approaches for registration. Thus, the novelty of our method is threefold: First, we employ a particlefiltering scheme to drive the point set registration process. Second, we present a local optimizer that is motivated by the correlation measure. Third, we increase the robustness of the registration performance by introducing a dynamic model of uncertainty for the transformation parameters. In contrast with other techniques, our approach requires no annealing schedule, which results in a reduction in computational complexity (with respect to particle size) as well as maintains the temporal coherency of the state (no loss of information). Also unlike some alternative approaches for point set registration, we make no geometric assumptions on the two data sets. Experimental results are provided that demonstrate the robustness of the algorithm to initialization, noise, missing structures, and/or differing point densities in each set, on several challenging 2D and 3D registration scenarios.

Sandhu, Romeil; Dambreville, Samuel; Tannenbaum, Allen

We present an efficient particlefiltering method to perform optimal estimation in jump Markov (nonlinear) systems (JMSs). Such processes consist of a mixture of heterogeneous models and possess a natural hierarchical structure. We take advantage of these specificities in order to develop a generic filtering methodology for these models. The method relies on an original and nontrivial combination of techniques

Abstract: The Kalman filter provides an effective solution to the linear-Gaussian filtering problem. However,where there is nonlinearity, either in the model specification or the observation process, othermethods are required. We consider methods known generically as particlefilters, which include thecondensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter.

The Kalman filter provides an effective solution to the linear-Gaussian fil tering problem. How- ever, where there is nonlinearity, either in the model specification or the observation process, other methods are required. We consider methods known generically as particlefilters, which include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior

A fibrous filter is a common cleaning device often used to remove particle from industrial gas streams. The main question that often arises concerns the evolution of the pressure drop and the filtration efficiency during the filter clogging. The increase of pressure drop and filter efficiency was measured and was linked to both the clogging degree inside the filter bed

Abstract The recently developed particlefilter oers a general numerical tool to approximate the state a posteriori density in nonlinear and non-Gaussian filtering problems with arbitrary accuracy. Because the particlefilter is fairly easy to implement and tune, it has quickly become a popular tool in signal processing applications. Its main drawback is that it is quite com- puter intensive.

Thomas Schon; Fredrik Gustafsson; Per-Johan Nordlund

The particlefilter has attracted considerable attention in vi- sual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particlefilter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor per- formance in visual tracking. It

Chunhua Shen; Anton Van Den Hengel; Anthony R. Dick; Michael J. Brooks

In this paper, we present an improved particlefiltering algorithm called GMPF for nonlinear, non-Gaussian and non-stationary state estimation problems in information acquisition field. The proposed algorithm integrates various virtues of current prevalent particlefilters, and has satisfying filtering accuracy and numerical stability at acceptable computational cost. Simulation results show the feasibility and efficiency of the proposed algorithm compared with

In this paper, we propose an improved particlefilter, and apply this new algorithm to bearing-only tracking problems. The generic particlefilter (also called bootstrap filter) suffers a main drawback of not incorporating the latest observations, which is the problem we mainly focus on. An improving scheme is presented to handle this problem, and the underlying idea of the new

In this paper, a semi-supervised particlefilter approach is proposed for visual tracking. The combination of semi-supervised learning and particlefilter is very natural since the unlabelled samples are generated by particle propagation. In addition, the proposed semi-supervised particlefilter can online select different features for robust tracking. To the best knowledge of the authors, this is the first time

Many factors make the ground target tracking problem decidedly nonlinear and non-Gaussian. Because these factors can lead to a multimodal posterior density, a Bayesian filtering solution is appropriate. In the last decade, the particlefilter has emerged as a Bayesian inference technique that is both powerful and simple to implement. In this work, we demonstrate the necessity of using multiple-target particlefilters when two or more tracks are linked through measurement contention. We also develop an efficient way to implement these filters by adaptively managing the type of particlefilters, the number of particles, and the enumeration of hypotheses during data association. Using simulated data, we compare the run-time of our adaptive particlefilter algorithm to the run-times of two baseline particlefilters, to demonstrate that our design mitigates the increase in computation required when performing joint multitarget tracking.

A novel optimization-based method for designing wavelet filter banks in image fusion is proposed. The filter bank design is formulated as a nonlinear optimization problem. The objective function of the optimization problem consists of both the performance metrics of the image fusion, such as the root mean square error (RMSE), and those of individual filters. The optimization problem is solved using simulating annealing.

Minimum Euclidean Distance OptimalFilter (MEDOF) program generates filters for use in optical correlators. Analytically optimizesfilters on arbitrary spatial light modulators (SLMs) of such types as coupled, binary, fully complex, and fractional-2pi-phase. Written in C language.

Barton, R. Shane; Juday, Richard D.; Alvarez, Jennifer L.

The optimal time-dependent receiver (OTDR) is presented and shown to be superior to the conventional matched filter receiver when cyclostationary interference is present. This performance advantage is explained by viewing both the OTDR and the conventional matched filter receiver as time-dependent filters that use the spectral correlation properties of the signal. The matched filter is periodic at the baud rate

The lack of a parameterized observation model in robot localization using occupancy grids requires the application of sampling-based methods, or particlefilters. This work addresses the problem of optimal Bayesian filtering for dynamic systems with observation models that cannot be approximated properly as any parameterized distribution, which includes localization and SLAM with occupancy grids. By integrating ideas from previous works

Jose-luis Blanco; Javier Gonzalez; Juan-antonio Fernandez-madrigal

We present a design method for sparse optimal Finite Impulse Response (FIR) filters that improve the visibility of a desired stochastic signal corrupted with white Gaussian noise. We emphasize that the filters we seek are of high-order but sparse, thus significantly reducing computational complexity. An optimal FIR filter for the estimation of a desired signal corrupted with white noise can be designed by maximizing the signal-to-noise ratio (SNR) of the filter output with the constraint that the magnitude (in 2-norm) of the FIR filter coefficients are set to unity.1, 2 This optimization problem is in essence maximizing the Rayleigh quotient and is thus equivalent to finding the eigenvector with the largest eigenvalue.3 While such filters are optimal, they are rarely sparse. To ensure sparsity, one must introduce a cardinality constraint in the optimization procedure. For high order filters such constraints are computationally burdensome due to the combinatorial search space. We relax the cardinality constraint by using the 1-norm approximation of the cardinality function. This is a relaxation heuristic similar to the recent sparse filter design work of Baran, Wei, and Oppenheim.4 The advantage of this relaxation heuristic is that the solutions tend to be sparse and the optimization procedure reduces to a convex program, thus ensuring global optimality. In addition to our proposed optimization procedure for deriving sparse FIR filters, we show examples where sparse high-order filters significantly perform better than low-order filters, whereas complexity is reduced by a factor of 10.

Nodes localization in mobile sensor networks can be dealt as a problem of mobile object tracking. The particlefilters algorithm which is based on Bayesian estimation and Monte Carlo method is an effective tool to deal with these problems. The particlefilters algorithm adopts a series of weighted particles to represent the possible position of a mobile object, and then

A new particlefilter, which combines genetic evolution and kernel density estimation, is proposed for moving object tracking.\\u000a Particlefilter (PF) solves non-linear and non-Gaussian state estimation problems in Monte Carlo simulation using importance\\u000a sampling. Kernel particlefilter (KPF) improves the performance of PF by using density estimation of broader kernel. However,\\u000a it has the problem which is similar to

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), particlefilter (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.

Hirpa, F. A.; Gebremichael, M.; LEE, H.; Hopson, T. M.

In Probability Hypothesis Density (PHD) particlefilter for tracking multiple targets, a new peak extraction method was studied. Firstly, we draw the largest weight particle, when the sum of all particle weights in its neighborhood is larger than target weight, the particle is considered as a peak. Then, we do the same steps for other particles, till we find all

A number of well-motivated extensions of the ?CDM concordance cosmological model postulate the existence of a population of sources embedded in the cosmic microwave background. One such example is the signature of cosmic bubble collisions which arise in models of eternal inflation. The most unambiguous way to test these scenarios is to evaluate the full posterior probability distribution of the global parameters defining the theory; however, a direct evaluation is computationally impractical on large datasets, such as those obtained by the Wilkinson Microwave Anisotropy Probe (WMAP) and Planck. A method to approximate the full posterior has been developed recently, which requires as an input a set of candidate sources which are most likely to give the largest contribution to the likelihood. In this article, we present an improved algorithm for detecting candidate sources using optimalfilters, and apply it to detect candidate bubble collision signatures in WMAP 7-year observations. We show both theoretically and through simulations that this algorithm provides an enhancement in sensitivity over previous methods by a factor of approximately two. Moreover, no other filter-based approach can provide a superior enhancement of these signatures. Applying our algorithm to WMAP 7-year observations, we detect eight new candidate bubble collision signatures for follow-up analysis.

McEwen, J. D.; Feeney, S. M.; Johnson, M. C.; Peiris, H. V.

The pantograph device must exert enough upward force to maintain sliding contact with the catenary at all times to avoid loss of contact due to excessive transient motion. This paper presented a global-oriented particle swarm optimization (GPSO) technique with Cauchy mutation for tuning the optimal control gains of PID controller in a high-speed rail pantograph device with notch filter. The

Scope of study. Flow non-uniformity in the automotive filter has a great impact on the filter performance. Therefore, study of the flow distribution as well as the dust particle concentration in the filter housing is very important for improvement of automotive filter design. This study focuses on particle counting and sizing techniques with Laser Doppler Velocimetry (LDV) and their application to automotive air filter measurement. The Purolator X13192 filter was tested in both the SAE J726 standard test housing and a newly designed diffuser housing with water and polystyrene latex (PSL) particles. Velocity and particle number density were measured at different levels above and below the filter with variable flow rates and particle sizes. Filter local efficiency and overall efficiency were analyzed based on the particle counting data. The effect of dirt accumulation on the performance of the filter was also investigated. Findings and conclusions. The 'swept volume technique' was developed for particle counting, while a method which utilizes the Doppler signal and particle trajectory analysis was created for sizing particles from submicron to about one hundred microns. Both techniques were calibrated with PSL particles and were fairly accurate in measurement (average errors were within 20%). A variety of velocity and particle number density profiles were obtained at different levels (12.7 mm above the filter, and 64 mm below the filter). These profiles may be useful either in the industrial design of new filters or in future research as benchmarks. For particles with diameters of 0.966 ?m, the measured overall efficiency, ranging from 5% to 65% depending on the flow rate, was much higher than that widely assumed or theoretically predicted (less than 5%). However, for particles with diameters of 5.3 ?m, the measured overall efficiency, varying from 65% to 85%, was much lower than that widely assumed or theoretically predicted (more than 90%). The distribution of overall efficiency versus Stokes number in the diffuser housing was similar to the theoretically predicted pattern except for Stokes numbers below 0.1, where the measured efficiencies tend to go up as the Stokes number further decreases, while the theoretically predicted efficiencies remain unchanged. Accumulation of dirt in a filter can change the velocity and particle number density distribution, and therefore change the filter performance. Dirty filters with restrictions of 127 mm H2O (half capacity) and 254 mm H2O (full capacity) demonstrated much higher efficiencies than clean filters, and the efficiencies were less dependent on flow rate and location.

We analyze a combline filter using the Finite Element Method (FEM) with ports where the tuning screws would normally be. The filter is tuned with a circuit simulator using the multiport S-parameter data and lumped capacitors at the ports. We can then optimize the combline filter very rapidly by mapping the “coarse” circuit model to the “fine” FEM model. This

Optimal Bayesian multi-target filtering is, in general, computationally impractical due to the high dimensionality of the multi-target state. Recently Mahler, [9], introduced a filter which propagates the first moment of the multi-target posterior distribution, which he called the Probability Hypothesis Density (PHD) filter. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many

This paper adopts the turbo synchronization framework to track the time varying carrier phase offset by exploiting the sequential Monte-Carlo techniques, known as particlefiltering which gets increasing attention for solving nonlinear non-Gaussian problem recently. We show that the particlefiltering technology can be combined with the turbo receiver. The proposed scheme is lastly compared by computer simulation.

The object tracking problem in a nonlinear and\\/or non-Gaussian circumstance can be solved by particlefilter estimation based on the concept of sequential importance sampling and the use of Bayesian theory. An improved object tracking scheme is proposed, which is based on the Markov chain Monte Carlo (MCMC) particlefilter and object color distribution. This scheme is robust to clutter,

Yuan Chen; Shengsheng Yu; Jun Fan; Wenxin Chen; Hongxing Li

An evolutionary particlefiltering algorithm is proposed for blind signal detection in flat Rayleigh fading channels who's model coefficients are unknown. The sample impoverishment of state boundaries without changing longtime can be relieved. The stochastic M-algorithm (SMA) is used to estimate the signal sent in flat fading channel. The simulation shows the proposed particlefiltering algorithm upholds comparable performance with

In particlefilter algorithm, resampling is always used to release sample impoverishment phenomenon, but it weakens the diversity of samples set and cause the algorithm unrobust. Based on imitating biology evolvement regulation, paper (Mo Yi-wei, et al., 2005) brought forward the evolutionary particlefilter (EPF) algorithm. On the cost of much calculation, this method ameliorates the diversity of samples set

Wang Jian; Dai Dingzhang; Dong Huachun; Quan Taifan; Jin Yonggao

Sequential Monte Carlo based estimators, also known as particlefilters (PF), have been widely used in nonlinear and non-Gaussian estimation problems. However, efficient distribution of the limited number of random samples remains a critical issue in design of the sequential Monte Carlo based estimation algorithms. In this work, we derive a modified unscented particlefilter based on variance reduction factor

Robust and real time moving object tracking is a tricky job in computer vision problems. Particlefiltering has been proven very successful for non-Gaussian and non-linear estimation problems. In this paper, we first try to develop a color based particlefilter. In this approach, the object tracking system relies on the deterministic search of window, whose color content matches a

Extended Kalman filter (EKF) has previously been employed to extract road maps in satellite images. This filter traces a single road until a stopping criterion is satisfied. In our new approach, we have combined EKF with a special particlefilter (PF) in order to regain the trace of the road beyond obstacles, as well as to find and follow different

In this paper a new meta-heuristic search method, called Cat Swarm Optimization (CSO) algorithm is applied to determine the best optimal impulse response coefficients of FIR low pass, high pass, band pass and band stop filters, trying to meet the respective ideal frequency response characteristics. CSO is generated by observing the behaviour of cats and composed of two sub-models. In CSO, one can decide how many cats are used in the iteration. Every cat has its' own position composed of M dimensions, velocities for each dimension, a fitness value which represents the accommodation of the cat to the fitness function, and a flag to identify whether the cat is in seeking mode or tracing mode. The final solution would be the best position of one of the cats. CSO keeps the best solution until it reaches the end of the iteration. The results of the proposed CSO based approach have been compared to those of other well-known optimization methods such as Real Coded Genetic Algorithm (RGA), standard Particle Swarm Optimization (PSO) and Differential Evolution (DE). The CSO based results confirm the superiority of the proposed CSO for solving FIR filter design problems. The performances of the CSO based designed FIR filters have proven to be superior as compared to those obtained by RGA, conventional PSO and DE. The simulation results also demonstrate that the CSO is the best optimizer among other relevant techniques, not only in the convergence speed but also in the optimal performances of the designed filters. PMID:23958491

This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions

The classical approach to designing filters for systems where system equations are linear and measurement equations are nonlinear is to linearise measurement equations, and apply an extended Kalman filter (EKF). This results in suboptimal, biased, and often divergent filters. Many schemes proposed to improve the performance of the EKF concentrated on better linearisation techniques, iterative techniques and adaptive schemes. The

This paper utilizes a human-robot interface system which incorporates particlefilter (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.

This paper utilizes a human-robot interface system which incorporates particlefilter (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

A major challenge in contemporary data science is the development of statistically accurate particlefilters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particlefilters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particlefilters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particlefilter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below. PMID:24825886

A major challenge in contemporary data science is the development of statistically accurate particlefilters to capture non-Gaussian features in large-dimensional chaotic dynamical systems. Blended particlefilters that capture non-Gaussian features in an adaptively evolving low-dimensional subspace through particles interacting with evolving Gaussian statistics on the remaining portion of phase space are introduced here. These blended particlefilters are constructed in this paper through a mathematical formalism involving conditional Gaussian mixtures combined with statistically nonlinear forecast models compatible with this structure developed recently with high skill for uncertainty quantification. Stringent test cases for filtering involving the 40-dimensional Lorenz 96 model with a 5-dimensional adaptive subspace for nonlinear blended filtering in various turbulent regimes with at least nine positive Lyapunov exponents are used here. These cases demonstrate the high skill of the blended particlefilter algorithms in capturing both highly non-Gaussian dynamical features as well as crucial nonlinear statistics for accurate filtering in extreme filtering regimes with sparse infrequent high-quality observations. The formalism developed here is also useful for multiscale filtering of turbulent systems and a simple application is sketched below.

The first and foremost step in developing a chaotic communication system is to establish synchronization of the chaotic systems\\/maps at the transmitter and receiver. Extended Kalman filter (EKF) is a widely studied nonlinear observer for chaotic synchronization. Since this scheme depends on the first order Taylor series approximation of the nonlinear function, it may introduce large errors in the state

The PHD filter has attracted much international interest since its introduction in 2000. It is based on two approximations. First, it is a first-order approximation of the multitarget Bayes filter. Second, to achieve closed-form formulas for the Bayes data-update step, the predicted multitarget probability distribution must be assumed Poisson. In this paper we show how to derive an optimal PHD (OPHD) filter, given that target number does not exceed one. (That is, we restrict ourselves to the single-target detection and tracking problem.) We further show that, assuming no more than a single target, the following are identical: (1) the multitarget Bayes filter; (2) the OPHD filter; (3) the CPHD filter; and (4) the multi-hypothesis correlation (MHC) filter. We also note that all of these are generalizations of the probabilistic data association (IPDA) filter of Musicki, Evans, and Stankovic.

Local maxima in multimodality image registration based on mutual information is discussed in this paper. Particle swarm optimization (PSO) and filter preprocessing based on hamming window is used to search the registration parameters. Simulations have been done to illustrate that after low-pass filter preprocessing local maxima is eliminated to a great extent. In most case the global maxima can be found by PSO. Simulations illustrate the efficiency and accuracy of this method in registration strategy. PMID:17591238

Previous stability analysis of the particle swarm optimizer was restricted to the assumption that all parameters are nonrandom, in effect a deterministic particle swarm optimizer. We analyze the stability of the particle dynamics without this restrictive assumption using Lyapunov stability analysis and the concept of passive systems. Sufficient conditions for stability are derived, and an illustrative example is given. Simulation

Visakan Kadirkamanathan; Kirusnapillai Selvarajah; Peter J. Fleming

Particle swarm optimization (PSO) has shown to be an efficient, robust and simple optimization algorithm. Most of the PSO studies are empirical, with only a few theoret- ical analyses that concentrate on understanding particle trajectories. These theoretical studies concentrate mainly on simplified PSO systems. This paper overviews current the- oretical studies, and extend these studies to investigate particle trajectories for

We present an efficient particlefiltering algorithm for multiscale systems, which is adapted for simple atmospheric dynamics models that are inherently chaotic. Particlefilters represent the posterior conditional distribution of the state variables by a collection of particles, which evolves and adapts recursively as new information becomes available. The difference between the estimated state and the true state of the system constitutes the error in specifying or forecasting the state, which is amplified in chaotic systems that have a number of positive Lyapunov exponents. In this paper, we propose a reduced-order particlefiltering algorithm based on the homogenized multiscale filtering framework developed in Imkeller et al. "Dimensional reduction in nonlinear filtering: A homogenization approach," Ann. Appl. Probab. (to be published). In order to adapt the proposed algorithm to chaotic signals, importance sampling and control theoretic methods are employed for the construction of the proposal density for the particlefilter. Finally, we apply the general homogenized particlefiltering algorithm developed here to the Lorenz'96 [E. N. Lorenz, "Predictability: A problem partly solved," in Predictability of Weather and Climate, ECMWF, 2006 (ECMWF, 2006), pp. 40-58] atmospheric model that mimics mid-latitude atmospheric dynamics with microscopic convective processes. PMID:23278095

Lingala, Nishanth; Sri Namachchivaya, N; Perkowski, Nicolas; Yeong, Hoong C

Optimal Bayesian multi-target filtering is, in general, computationally impractical owing to the high dimensionality of the multi-target state. The probability hypothesis density (PHD) filter propagates the first moment of the multi-target posterior distribution. While this reduces the dimensionality of the problem, the PHD filter still involves intractable integrals in many cases of interest. Several authors have proposed sequential Monte Carlo

In this paper we propose a robust lane detection and tracking method by combining particlefilters with the particle swarm optimization method. This method mainly uses the particlefilters 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 particlefilter 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

In this paper we propose a robust lane detection and tracking method by combining particlefilters with the particle swarm optimization method. This method mainly uses the particlefilters 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 particlefilter 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.

The transfer function of an EMC (Electro-Magnetic Compatibility) filter is strongly disturbed in high frequency due to stray electromagnetic phenomena. On the one hand the imperfections of the components but also all magnetic couplings on the other hand. Although these effects seem to be negative, it is possible to reduce the impacts of these imperfections on the filter response, and

Thomas DE OLIVEIRA; Jean-Luc SCHANEN; Jean-Michel GUICHON; Laurent GERBAUD

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.

The particle swarm optimizer (PSO) is a stochastic, population-based optimization technique that can be applied to a wide range of problems, including neural network training. This paper presents a variation on the traditional PSO algorithm, called the cooperative particle swarm optimizer, or CPSO, employing cooperative behavior to significantly improve the performance of the original algorithm. This is achieved by using

This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide

Carlos A. Coello Coello; Gregorio Toscano Pulido; M. Salazar Lechuga

Multiobjective particle swarm optimization (MOPSO) algorithms have been widely used to solve multiobjec- tive optimization problems. Most MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary pro- cess. In this paper, we introduce a cultural framework to adapt the personalized flight parameters of the mutated particles in a MOPSO, namely momentum and personal and global acceler- ations,

In this paper, we present a computationally efficient method for adaptive tracking of physiological parameters such as heart rate and respiratory rate from the arterial blood pressure (ABP) measurement using particlefilters. A previously reported estimation and tracking method was based on approximating the nonlinear models to linear ones based on the extended Kalman filters. However, the dynamic state-space model

B. Balasingam; M. Forouzanfar; M. Bolic; H. Dajani; V. Groza; S. Rajan

In this paper, a visual object tracking algorithm based on the Kalman particlefilter (KPF) is presented. The KPF uses the Kalman filter to generate sophisticated proposal distributions which greatly improving the tracking performance. However, this improvement is at the cost of much extra computation. To accelerate the algorithm, we mend the conventional KPF by adaptively adjusting the number of

An optimal deconvolution filter design method is proposed in this paper for signal transmission systems with small perturbation of parameters. The perturbative parameters of the transmission channel and noise model are of probabilistic structures. A realizable filter is derived to minimize the mean square estimation error from the viewpoint of frequency domain. The calculus of variation technique and the spectral

The design problem of fault detection and isolation filters is formulated as a model matching problem and solved using an H2-or H?-norm optimization approach. A systematic procedure is proposed to choose appropriate filter specifications which guarantee the existence of proper and stable solutions of the model matching problem. This selection is integral part of numerically reliable computational methods to design

An algorithm based on the marginalized particlefilters (MPF) is given in details in this paper to solve the spacecraft attitude\\u000a estimation problem: attitude and gyro bias estimation using the biased gyro and vector observations. In this algorithm, by\\u000a marginalizing out the state appearing linearly in the spacecraft model, the Kalman filter is associated with each particle\\u000a in order to

\\u000a With increasing air traffic flow, the increasingly complex air traffic situation has raised possibility of conflicts, which\\u000a requires higher timeliness and accuracy for conflict detection. Based on the rapidly growing air traffic control technologies,\\u000a such as radars with excellent accuracy, an improved unscented particlefilter (MUPF) algorithm is proposed to perform real-time\\u000a aircraft status estimation. Compared to traditional particlefilter

Abstract—We study efficient importance sampling techniques for particlefiltering (PF) when either (a) the observation likelihood (OL) is frequently multimodal or heavy-tailed, or (b) the state space dimension is large or both. When the OL is multimodal, but the state transition pdf (STP) is narrow enough, the optimal importance density is usually unimodal. Under this assumption, many techniques have been

Due to the shortcoming of constructing importance density in general particlefilter, we propose an improved algorithm based on neural network to optimize the choice of importance density. It is proved to be more efficient than the general algorithm in the same sample size. This algorithm adjusts the samples drawn from prior density with general regression neural network (GRNN), and

This paper presents an approach to label and track multiple objects through both temporally and spatially significant occlusions. To this end, tracking is performed at both the region level and the object level. At the region level, a kernel based particlefilter method is used to search for optimal region tracks which limits the scope of object trajectories. At the

implicit particlefilter is applied to a stochastically forced shallow water model of nearshore flow, and found to produce reliable state estimates with tens of particles. The state vector of this model consists of a height anomaly and two horizontal velocity components at each point on a 128 × 98 regular rectangular grid, making for a state dimension O(104). The particlefilter was applied to the model with two parameter choices representing two distinct dynamical regimes, and performed well in both. Demands on computing resources were manageable. Simulations with as many as a hundred particles ran overnight on a modestly configured workstation. In this case of observations defined by a linear function of the state vector, taken every time step of the numerical model, the implicit particlefilter is equivalent to the optimal importance filter, i.e., at each step any given particle is drawn from the density of the system conditioned jointly upon observations and the state of that particle at the previous time. Even in this ideal case, the sample occasionally collapses to a single particle, and resampling is necessary. In those cases, the sample rapidly reinflates, and the analysis never loses track. In both dynamical regimes, the ensembles of particles deviated significantly from normality.

In this paper, we develop a novel solution for particlefiltering on general graphs. We provide an exact solution for particlefiltering on directed cycle-free graphs. The proposed approach relies on a partial-order relation in an antichain decomposition that forms a high-order Markov chain over the partitioned graph. We subsequently derive a closed-form sequential updating scheme for conditional density propagation using particlefiltering on directed cycle-free graphs. We also provide an approximate solution for particlefiltering on general graphs by splitting graphs with cycles into multiple directed cycle-free subgraphs. We then use the sequential updating scheme by alternating among the directed cycle-free subgraphs to obtain an estimate of the density propagation. We rely on the proposed method for particlefiltering on general graphs for two video tracking applications: 1) object tracking using high-order Markov chains; and 2) distributed multiple object tracking based on multi-object graphical interaction models. Experimental results demonstrate the improved performance of the proposed approach to particlefiltering on graphs compared with existing methods for video tracking. PMID:21118778

A novel approach to multiobjective particle swarm optimization (MOPSO) technique for solving optimal power flow (OPF) problem is proposed in this paper. The new MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. A clustering algorithm to manage the size of the Pareto-optimal set is imposed. The

Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particlefilter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with

M. Marron; J.C. Garcia; M. A. Sotelo; M. Cabello; D. Pizarro; F. Huerta; J. Cerro

This paper considers the problem of joint maneuvering target tracking and classification. Based on recently proposed Monte Carlo techniques, a multiple model (MM) particlefilter and a mixture Kalman filter (MKF) are designed for two-class identification of air targets: commercial and military aircraft. The classification task is carried out by processing radar measurements only, no class (feature) measurements are used.

This paper presents a novel pulse width switched modulated power filter compensator (MPFC) for the voltage stability enhancement, energy utilization, loss reduction, and power factor correction in a radial distribution network using the Particle Swarm Optimization (PSO) technique. The MPFC is controlled by a novel dynamic tri-loop error driven controller. The dynamic controller is regulated to minimize the switching current

This paper puts forward the mobile robot localization method based on adaptive particlefilter (ADF). This method defines\\u000a the particle number by designating sampling error boundary, and regulates the particle number dynamically by following the\\u000a uncertain intensity of robot state. When the uncertain intensity of state space is low, ADF uses fewer particles, and when\\u000a the uncertain intensity is high,

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.

In this paper, the problem of jointly estimating the number of harmonics and the fundamental frequency of periodic signals is considered. We show how this problem can be solved using a number of methods that either are or can be interpreted as filtering methods in combination with a statistical model selection criterion. The methods in question are the classical comb filtering method, a maximum likelihood method, and some filtering methods based on optimalfiltering that have recently been proposed, while the model selection criterion is derived herein from the maximum a posteriori principle. The asymptotic properties of the optimalfiltering methods are analyzed and an order-recursive efficient implementation is derived. Finally, the estimators have been compared in computer simulations that show that the optimalfiltering methods perform well under various conditions. It has previously been demonstrated that the optimalfiltering methods perform extremely well with respect to fundamental frequency estimation under adverse conditions, and this fact, combined with the new results on model order estimation and efficient implementation, suggests that these methods form an appealing alternative to classical methods for analyzing multi-pitch signals.

Abstract The fidelity of the trajectories obtained from video-based particle tracking determines the success of a variety of biophysical techniques, including in situ single cell particle tracking and in 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.

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.

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.

The paper introduces a framework that integrates analytical inference into the particlefiltering scheme for human body tracking. The analytical inference is provided by body parts detection, and is used to update subsets of state parameters representing the human pose. This reduces the degree of randomness and decreases the required number of particles. This new technique is a significant improvement

A novel Gaussian mixture sigma-point particlefilter algorithm is proposed to mitigate the sample depletion problem. The posterior state density is represented by a Gaussian mixture model that is recovered from the weighted particle set of the measurement update step by means of a weighted expectation-maximization algorithm. The simulation results demonstrate the validity of the proposed algorithm.

An improved particlefilter algorithm is proposed for WSN-aided robot localization. This algorithm introduces node reliability and proposes its expression to realize information fusion among nodes. After having computed node reliability, sensor nodes with high localization, and take effect on updating particles according to their reliability. Furthermore, a novel simulation system for WSN-robot based on USARSim is developed, which is

Hui Zhang; Yong Li; Ying Qu; Dan Hai; Huaping Zhou; Dachuan Wang

Simultaneous localization and map building (SLAM) is one of the fundamental problems in robot navigation, and FastSLAM algorithms based on Rao-Blackwellized particlefilters (RBPF) have become popular tools to solve the SLAM problems. For solving the potential limitations, which are the derivation of the Jacobian matrices, and particles impoverishment in SLAM algorithms, this paper proposes an improved algorithm based on

This paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO) with particle density. In the proposed method, density of particles around every Pareto solution is calculated and a Pareto solution with low particle density is selected as gbest which is a best position visited thus far by all of the particles. Then, it is validated through a simulation with some Multi-Objective problems comparing to the sigma method which is the conventional to select gbest.

This paper proposes a Multi-Objective Particle Swarm Optimization (MOPSO) with particle density. In the proposed method, density of particles around every Pareto solution is calculated and a Pareto solution with low particle density is selected as gbest which is a best position visited thus far by all of the particles. Then, it is validated through a simulation with a Multi-Objective 0/1 knapsack problem comparing to the sigma method which is the conventional to select gbest.

Holographic imaging filters are required to have high Bragg selectivity to obtain spatial-spectral information within a three-dimensional object. In this Letter, we present the design of holographic imaging filters formed using silicon oxide nano-particles (nano-SiO2) in PQ-PMMA polymer recording material. This combination offers greater angular and spectral selectivity and increases the diffraction efficiency of holographic filters. The holographic filters with optimized ratio of nano-SiO2 in PQ-PMMA can significantly improve the performance of Bragg selectivity and diffraction efficiency by 53% and 16%, respectively. We present experimental results and data analysis demonstrating this technique in use for holographic spatial-spectral imaging filters.

Luo, Yuan; Russo, Juan M.; Kostuk, Raymond K.; Barbastathis, George

The paper discusses computations on the performance of particlefilters and electronic air cleaners (EACs). he collection efficiency of particlefilters and ACs is calculable if certain factors can be assumed or calibrated. or fibrous particulate filters, measurement of collectio...

The paper discusses computations on the performance of particlefilters and electronic air cleaners (EACs). The collection efficiency of particlefilters and ACs is calculable if certain factors can be assumed or calibrated. For fibrous particulate filters, measurement of colle...

It is a key issue for Monte Carlo localization of mobile robot based on particlefilter in mobile robot research. However, the existed problem with the SIS (sequential importance sampling) particlefilter is the degeneracy phenomenon. Hence, two parts in particlefilter are improved. One is to combine the resampling of particlefilter with fuzzy map matching presented in advance

In this paper, frequency of distorted signal in power system has been estimated with particle extended Kalman filter. Base of particle algorithm, extended Kalman filter and particle extended Kalman filter are mentioned. For selecting state variables, a nonlinear time-variant sinusoidal signal is developed then a particle extended Kalman filter is applied to detect the frequency variations. Several tests are performed

E. M. Siavashi; S. Afsharnia; M. Tavakoli Bina; M. Karbalai Zadeh; M. R. Baradar

This paper presents a particle swarm optimizer (PSO) with passive congregation to improve the performance of standard PSO (SPSO). Passive congregation is an important biological force preserving swarm integrity. By introducing passive congregation to PSO, information can be transferred among individuals of the swarm. A particle swarm optimizer with passive congregation (PSOPC) is tested with a set of 10 benchmark

S. He; Q. H. Wu; J. Y. Wen; J. R. Saunders; R. C. Paton

The paper presents methods for the analysis of human tremor using particle swarm optimization. Two forms of human tremor are addressed: essential tremor and Parkinson's disease. Particle swarm optimization is used to evolve a neural network that distinguishes between normal subjects and those with tremor. Inputs to the neural network are normalized movement amplitudes obtained from an actigraph system. The

We suggest a new type of optimized composite filter, i.e., the asymmetric segmented phase-only filter (ASPOF), for improving the effectiveness of a VanderLugt correlator (VLC) when used for face identification. Basically, it consists in merging several reference images after application of a specific spectral optimization method. After segmentation of the spectral filter plane to several areas, each area is assigned to a single winner reference according to a new optimized criterion. The point of the paper is to show that this method offers a significant performance improvement on standard composite filters for face identification. We first briefly revisit composite filters [adapted, phase-only, inverse, compromise optimal, segmented, minimum average correlation energy, optimal trade-off maximum average correlation, and amplitude-modulated phase-only (AMPOF)], which are tools of choice for face recognition based on correlation techniques, and compare their performances with those of the ASPOF. We illustrate some of the drawbacks of current filters for several binary and grayscale image identifications. Next, we describe the optimization steps and introduce the ASPOF that can overcome these technical issues to improve the quality and the reliability of the correlation-based decision. We derive performance measures, i.e., PCE values and receiver operating characteristic curves, to confirm consistency of the results. We numerically find that this filter increases the recognition rate and decreases the false alarm rate. The results show that the discrimination of the ASPOF is comparable to that of the AMPOF, but the ASPOF is more robust than the trade-off maximum average correlation height against rotation and various types of noise sources. Our method has several features that make it amenable to experimental implementation using a VLC. PMID:22614484

In robotics, a key problem is for a robot to explore its environment and use the information gathered by its sensors to jointly produce a map of its environment, together with an estimate of its position: so-called SLAM (Simultaneous Localization and Mapping) [12]. Various filtering methods - ParticleFiltering, and derived Kalman Filter methods (Extended, Unscented) - have been applied successfully to SLAM. We present a new algorithm that adapts the Square Root Unscented Transformation [13], previously only applied to feature based maps [5], to grid mapping. We also present a new method for the so-called pose-correction step in the algorithm. Experimental results show improved computational performance on more complex grid maps compared to an existing grid based particlefiltering algorithm.

Abstract Robust real-time tracking of non-rigid objects is a challenging task. Particle ltering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents the integration of color distributions into particle ltering, which has typically been used in combination with edge-based image features. Color distributions are applied as they are robust to partial occlusion, are rotation and scale

Katja Nummiaro; Esther Koller-meier; Luc J. Van Gool

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.

Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state

Daniel Clark; Ioseba Ruiz; Yvan Petillot; Judith Bell

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.

Filtration has been identified as the most important barrier for the removal of particles and microorganisms in drinking water treatment. The objective was to address the public demand for higher quality at lower costs and improved safeguards by optimizin...

Research is underway at the NASA Johnson Space Center on the development of vision systems that recognize objects and estimate their position by processing their images. This is a crucial task in many space applications such as autonomous landing on Mars sites, satellite inspection and repair, and docking of space shuttle and space station. Currently available algorithms and hardware are too slow to be suitable for these tasks. Electronic digital hardware exhibits superior performance in computing and control; however, they take too much time to carry out important signal processing operations such as Fourier transformation of image data and calculation of correlation between two images. Fortunately, because of the inherent parallelism, optical devices can carry out these operations very fast, although they are not quite suitable for computation and control type operations. Hence, investigations are currently being conducted on the development of hybrid vision systems that utilize both optical techniques and digital processing jointly to carry out the object recognition tasks in real time. Algorithms for the design of optimalfilters for use in hybrid vision systems were developed. Specifically, an algorithm was developed for the design of real-valued frequency plane correlation filters. Furthermore, research was also conducted on designing correlation filtersoptimal in the sense of providing maximum signal-to-nose ratio when noise is present in the detectors in the correlation plane. Algorithms were developed for the design of different types of optimalfilters: complex filters, real-value filters, phase-only filters, ternary-valued filters, coupled filters. This report presents some of these algorithms in detail along with their derivations.

Air sampling for particulate radioactive material involves collecting airborne particles on a filter and then determining the amount of radioactivity collected per unit volume of air drawn through the filter. The amount of radioactivity collected is frequently determined by directly measuring the radiation emitted from the particles collected on the filter. Counting losses caused by the particle becoming buried in the filter matrix may cause concentrations of airborne particulate radioactive materials to be underestimated by as much as 50%. Furthermore, the dose calculation for inhaled radionuclides will also be affected. The present study was designed to evaluate the extent to which particle size and sampling velocity influence burial depth in glass-fiber filters. Aerosols of high-fired /sup 239/PuO/sub 2/ were collected at various sampling velocities on glass-fiber filters. The fraction of alpha counts lost due to burial was determined as the ratio of activity detected by direct alpha count to the quantity determined by photon spectrometry. The results show that burial of airborne particles collected on glass-fiber filters appears to be a weak function of sampling velocity and particle size. Counting losses ranged from 0 to 25%. A correction that assumes losses of 10 to 15% would ensure that the concentration of airborne alpha-emitting radionuclides would not be underestimated when glass-fiber filters are used. 32 references, 21 figures, 11 tables.

\\u000a A novel approach to multiobjective particle swarm optimization (MOPSO) technique for solving optimal power flow (OPF) problem\\u000a is proposed in this chapter. The new MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of\\u000a global best and local best individuals in multiobjective optimization domain. A clustering algorithm to manage the size of\\u000a the Pareto-optimal set is imposed. The

Symmetric quadrature mirror filters (QMFs) offer several advantages for wavelet-based image coding. Symmetry and odd-length contribute to efficient boundary handling and preservation of edge detail. Symmetric QMFs can be obtained by mildly relaxing the filter bank orthogonality conditions. We describe a computational algorithm for these filter banks which is also symmetric in the sense that the analysis and synthesis operations have identical implementations, up to a delay. The essence of a wavelet transform is its multiresolution decomposition, obtained by iterating the lowpass filter. This allows one to introduce a new design criterion, smoothness (good behavior) of the lowpass filter under iteration. This design constraint can be expressed solely in terms of the lowpass filter tap values (via the eigenvalue decomposition of a certain finite-dimensional matrix). Our innovation is to design near- orthogonal QMFs with linear-phase symmetry which are optimized for smoothness under iteration, not for stopband rejection. The new class of optimally smooth QMF filter banks yields high performance in a practical image compression system.

Heller, Peter N.; Shapiro, Jerome M.; Wells, Raymond O., Jr.

For solving the problems of mobile robot SLAM (Simultaneous Localization and Mapping) in unknown environments, this paper presents an optimized RBPF algorithm. The method employs the UKF algorithm instead of the EKF algorithm to estimate landmarks, so it can avoid the derivation of complicated Jacobian Matrix and reduce the error generated by linearizing the nonlinear system. Using the Euclidean distance

Simultaneous localization and mapping (SLAM) is of prime importance for navigation problem of autonomous underwater vehicle. Currently EKF-based SLAM and particlefilter-based SLAM are prevalent methods though they have their own deficiency respectively. In this paper a modified RBPF method is proposed to apply in navigation and localization for our underwater vehicle, C-RANGER. Unscented Kalman filter instead of extended Kalman

Bo He; Lili Yang; Ke Yang; Yitong Wang; Nini Yu; Chunrong Lu

ó Bayesian single-target tracking techniques can be extended to a multiple-target environment by viewing the multiple-target state as a Random Finite Set, but evaluating the multiple-target posterior distribution is currently computation- ally intractable for real-time applications. A practical alternative to the optimal Bayes multi-target lter is the PHD (Probabil- ity Hypothesis Density) lter , which propagates the rst-order moment of

Robotics researchers are often faced with real- time constraints, and for that reason algorithmic and implementation-level optimization can dramatically increase the overall performance of a robot. In this paper we illustrate how a substantial run-time gain can be achieved by taking advantage of the extended instruction sets found in modern processors, in particular the SSE1 and SSE2 instruction sets. We

In this paper, we combine a path planner based on Boundary Value Problems (BVP) and Monte Carlo Localization (MCL) to solve\\u000a the wake-up robot problem in a sparse environment. This problem is difficult since large regions of sparse environments do\\u000a not provide relevant information for the robot to recover its pose. We propose a novel method that distributes particle poses

A introduction to particlefiltering 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 particlefilter, 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 particlefilter designs with classical nonlinear filter implementations.

WLAN Indoor tracking system is presented based on the comparison between the off-line pre-stored Radio-map and new recorded signal strength in the on-line phase to estimate user's motion trajectory. Furthermore, the improved particlefilter tracking algorithm that consists of the particles-reference points (P-RPs) transferring for getting the likelihood function and velocity estimation from the ANN positioning results is also discussed

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particlefilter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy of particles and cumulation of estimation errors. In this paper, we propose an improved PF algorithm called PF-RBF. This algorithm uses the radial-basis function network (RBFN) in the sampling step for dynamically constructing the process model from observations and updating the value of each particle. With the RBFN sampling step, PF-RBF can give an accurate proposal distribution and maintain the convergence of a sensor system. Simulation results verify that PF-RBF performs better than the Unscented Kalman Filter (UKF), PF and Unscented ParticleFilter (UPF) in both robustness and accuracy whether the observation model used for the sensor system is linear or nonlinear. Moreover, the intrinsic property of PF-RBF determines that, when the particle number exceeds a certain amount, the execution time of PF-RBF is less than UPF. This makes PF-RBF a better candidate for the sensor systems which need many particles for target tracking.

This paper first analyzes the impact that inertia weight and maximum velocity have on the performance of the particle swarm optimizer, and then provides guidelines for selecting these two parameters. Analysis of experiments demonstrates the validity of these guidelines.

One of the key motivating factors for using particlefilters for prognostics is the ability to include model parameters as part of the state vector to be estimated. This performs model adaptation in conjunction with state tracking, and thus, produces a tuned model that can used for long term predictions. This feature of particlefilters works in most part due to the fact that they are not subject to the "curse of dimensionality", i.e. the exponential growth of computational complexity with state dimension. However, in practice, this property holds for "well-designed" particlefilters only as dimensionality increases. This paper explores the notion of wellness of design in the context of predicting remaining useful life for individual discharge cycles of Li-ion batteries. Prognostic metrics are used to analyze the tradeoff between different model designs and prediction performance. Results demonstrate how sensitivity analysis may be used to arrive at a well-designed prognostic model that can take advantage of the model adaptation properties of a particlefilter.

This paper proposed a multi-cue based face tracking algorithm with the help of parallel multi-core processing. Due to illumination and occlusion problems, face tracking usually does not work stably based on a single cue. Three different visual cues, color histogram, edge orientation histogram and wavelet feature, are integrated under the framework of particlefilter to improve the tracking performance considerably.

Ke-Yan Liu; Shan-Qing Li; Liang Tang; Lei Wang; Wei Liu

This work presents a discriminative training method for particlefilters in the context of multi-object tracking. We are motivated by the difficulty of hand-tuning the many model parameters for such applications and also by results in many application domains indicating that discriminative training is often superior to generative training methods. Our learning approach is tightly integrated into the actual inference

This paper describes a system which connects radar sensors directly to one single ECU (electronic control unit) via an SSC (synchronous serial channel) interface. The ECU runs a signal processing to convert the radar raw data from the sensor into a peak list. For tracking a particlefilter is used. The introduced system is directed towards pre-crash applications. Such applications

Condensation is a widely-used tracking algorithm based on particlefilters. Although some results have been achieved, it has several unpleasant behaviours. In this paper, we highlight these misbehaviours and propose two improvements. A new weight assignment, which avoids sample impoverishment, is presented. Subsequently, the prediction pro- cess is enhanced. The proposal has been successfully tested using syn- thetic data, which

Daniel Rowe; Ignasi Rius; Jordi Gonzàlez; F. Xavier Roca; Juan José Villanueva

Most of the countries have paid great attention to flood water level monitoring and tracking because flood may damages people's life and property. Since flood water level fluctuate highly nonlinear, it is very difficult to predict the flood water level. The particlefilter algorithm is well known as a very effective solution for handling nonlinear problems. Thus, in this paper,

Fazlina Ahmat Ruslan; Ramli Adnan; Abd Manan Samad

One of the particlefiltering uses is object tracking since this tech- nique permits to deal with uncertainty over time met in real time image sequences framework. This uncertainty is as m uch non- manageable that an object occlusion appears in imag es. In this paper, we propose an occlusion-handling scheme which signifi- cantly improves the tracking performance in presenc

Hand tracking is an active research topic in Human Computer Interaction (HCI). In this paper, we present an improved Unscented ParticleFilter (UPF) combined with the incremental Principle Component Analysis (IPCA) method for the visual hand tracking. The Singular Value Decomposition (SVD) approach is introduced to compute the sigma points and then to obtain the proposal distribution within the Unscented

Sensor systems are not always equipped with the ability to track targets. Sudden maneuvers of a target can have a great impact on the sensor system, which will increase the miss rate and rate of false target detection. The use of the generic particlefilter (PF) algorithm is well known for target tracking, but it can not overcome the degeneracy

This article mainly described a bearings-only passive ranging method with IRST System. Since the Kalman method has disadvantages such as slow converging speed and low tracking accuracy. We applied a new method called particlefilter with constraint between generations with the IRST system for the passive tracking. In this method, we update the distance by using the constraint between generations,

A robust visual tracking method which can be used in complex environments is presented in this paper. The color cue and the shape cue are utilized to represent the target and fused together by democratic integration method. The multi-cue object representation is incorporated into the framework of particlefilter which is a powerful probabilistic method for visual tracking. To each

In the recent years particlefiltering has been the dominant paradigm for tracking facial and body features, recogniz- ing temporal events and reasoning in uncertainty. A major problem associated with it is that its performance deterio- rates drastically when the dimensionality of the state space is high. In this paper, we address this problem when the state space can be

In this paper, an efficient hardware architecture of Sample Important Resample ParticleFilter (SIRF) is presented. This architecture carries out the sampling, weighting, and output calculations steps concurrently. The resampling step is implemented in a massively parallel form. For weight computation step, piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture

Howida Abd A. El-Halym; Imbaby I. Mahmoud; S. E.-D. Habib

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

The application of the multipulse sequences in nuclear quadrupole resonance (NQR) detection of explosive and narcotic substances has been studied. Various approaches to increase the signal to noise ratio (SNR) of signal detection are considered. We discussed two modifications of the phase-alternated multiple-pulse sequence (PAMS): the 180° pulse sequence with a preparatory pulse and the 90° pulse sequence. The advantages of optimalfiltering to detect NQR in the case of the coherent steady-state precession have been analyzed. It has been shown that this technique is effective in filtering high-frequency and low-frequency noise and increasing the reliability of NQR detection. Our analysis also shows the PAMS with 180° pulses is more effective than PSL sequence from point of view of the application of optimalfiltering procedure to the steady-state NQR signal.

Osokin, D. Ya.; Khusnutdinov, R. R.; Mozzhukhin, G. V.; Rameev, B. Z.

This paper investigates the philosophical and performance differences of particle swarm and evolutionary optimization. The method of processing employed in each technique are first reviewed followed by a summary of their philosophical differences. Comparison experiments involving four non-linear functions well studied in the evolutionary optimization literature are used to highlight some performance differences between the techniques.

To the shortcoming of general particlefilter, an improved algorithm based on neural network is proposed and is shown to be\\u000a more efficient than the general algorithm in the same sample size. The improved algorithm has mainly optimized the choice\\u000a of importance density. After receiving the samples drawn from prior density, and then adjust the samples with general regression\\u000a neural

Fast and high-quality document clustering algorithms play an important role in effectively navigating, summarizing, and organizing information. Recent studies have shown that partitional clustering algorithms are more suitable for clustering large datasets. However, the K-means algorithm, the most commonly used partitional clustering algorithm, can only generate a local optimal solution. In this paper, we present a particle swarm optimization (PSO)

A hybrid particle swarm optimizer with mass extinction, which has been suggested to be an important mechanism for evolutionary progress in the biological world, is presented to enhance the capacity in reaching an optimal solution. The tested results of three benchmark functions indicate this method improves the performance effectively.

Particle swarm optimization has become a common heuristic technique in the optimization community, with many researchers exploring the concepts, issues, and applications of the algorithm. In spite of this attention, there has as yet been no standard definition representing exactly what is involved in modern implementations of the technique. A standard is defined here which is designed to be a

As a novel optimization technique, chaos has gained much attention and some applications during the past decade. For a given energy or cost function, by following chaotic ergodic orbits, a chaotic dynamic system may eventually reach the global optimum or its good approximation with high probability. To enhance the performance of particle swarm optimization (PSO), which is an evolutionary computation

Bo Liu; Ling Wang; Yi-Hui Jin; Fang Tang; De-Xian Huang

This paper focuses on the engineering and computer science aspects of developments, applications, and resources related to particle swarm optimization. Developments in the particle swarm algorithm since its origin in 1995 are reviewed. Included are brief discussions of constriction factors, inertia weights, and tracking dynamic systems. Applications, both those already developed, and promising future application areas, are reviewed. Finally, resources

In this paper we present particle swarm optimization with Gaussian mutation combining the idea of the particle swarm with concepts from evolutionary algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal

In this paper the authors propose a method for adapting the particle swarm optimizer for dynamic environments. The process consists of causing each particle to reset its record of its best position as the environment changes, to avoid making direction and velocity decisions on the basis of outdated information. Two methods for initiating this process are examined: periodic resetting, based

This paper describes a evolutionary optimization algorithm that is a hybrid based on the particle swarm algorithm but with the addition of a standard selection mechanism from evolutionary computations. A comparison is performed between the hybrid swarm and the ordinary particle swarm that shows selection to provide an advantage for some (but not all) complex functions

This paper presents an online object tracking method, in which co-training and particlefilters algorithms cooperate and complement each other for robust and effective tracking. Under framework of particlefilters, the semi-supervised cotraining algorithm is adopted to construct, on-line update, and mutually boost two complementary object classifiers, which consequently improves discriminant ability of particles and its adaptability to appearance variants caused by illumination changing, pose verying, camera shaking, and occlusion. Meanwhile, to make sampling procedure more efficient, knowledge from coarse confidence maps and spatial-temporal constraints are introduced by importance sampling. It improves not only the accuracy and efficiency of sampling procedure, but also provides more reliable training samples for co-training. Experimental results verify the effectiveness and robustness of our method.

In this modern era, image transmission and processing plays a major role. It would be impossible to retrieve information from satellite and medical images without the help of image processing techniques. Edge enhancement is an image processing step that enhances the edge contrast of an image or video in an attempt to improve its acutance. Edges are the representations of the discontinuities of image intensity functions. For processing these discontinuities in an image, a good edge enhancement technique is essential. The proposed work uses a new idea for edge enhancement using hybridized smoothening filters and we introduce a promising technique of obtaining best hybrid filter using swarm algorithms (Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)) to search for an optimal sequence of filters from among a set of rather simple, representative image processing filters. This paper deals with the analysis of the swarm intelligence techniques through the combination of hybrid filters generated by these algorithms for image edge enhancement.

Rao, B. Tirumala; Dehuri, S.; Dileep, M.; Vindhya, A.

In this paper, we address the issue of designing a two- channel linear phase biorthogonal filter bank that maximizes the two most desired properties for the wavelet transform in image coding applications, namely, orthogonality and energy compaction. Proper cost functions are formulated for these two criteria and an efficient signal-adaptive optimization algorithm is proposed. Our algorithm is motivated by a number of interesting properties of the correlation matrix of typical image signals, and uses lifting operations to efficiently represent the degrees of freedom subject to perfect reconstruction conditions. In addition, it offers a successive tradeoff between our two optimization goals. Experimental results on the popular Daubechies 9-7 and 10-18 filter banks reveal that considerable improvements in terms of both orthogonality and energy compaction can be achieved through the proposed optimization technique.

Magnetic particle imaging (MPI) has emerged as a new imaging modality that uses the nonlinear magnetization behavior of superparamagnetic particles. Due to the need to avoid contamination of particle signals with the simultaneous excitation signal, MPI transmit systems require different design considerations from those in MRI, where excitation and detection are temporally decoupled. Specifically, higher order harmonic distortion in the transmit spectrum can feed through to and contaminate the received signal spectrum. In a prototype MPI scanner, this distortion needs to be attenuated by 90 dB at all frequencies. In this paper, we describe two methods of filtering out harmonic distortion in the transmit spectrum. The first method uses a Butterworth topology while the second a cascaded Butterworth-elliptic topology. We show that whereas the Butterworth filter alone achieves around 16 and 32 dB attenuation at the second and third harmonics, the cascaded filter can achieve around 65 and 73 dB at these harmonics. Finally, we discuss how notch placement in the stopband can also be applied to design highpass filters for MPI detection systems.

In a Luminescent Solar Concentrator, short-wavelength light is converted by a luminescent material into longwavelength light, which is light guided towards a photovoltaic cell. In principle, a Luminescent Solar Concentrator allows for high concentration, since the heat generated by the conversion process can be used to lower the entropy of light. However, less controlled loss mechanisms prevent high concentration factors in practice. One important loss mechanism is the escape of luminescent radiation into directions that do not stay inside the light guide. To reduce this amount, wavelength-selective filters can be applied that reflect the luminescent radiation back into the light guide while transmitting the incident sunlight. However, a filteroptimized for reflecting as much as possible luminescent radiation will reflect part of the incident sunlight at high angles. Depending on the luminophore properties, it may be possible to design a suitable filter. In this paper, the interdependence of the luminophore and filter properties will be clarified and quantified using simulations. Optimal luminophore-filter combinations will be discussed, as well as the feasibility to realize them in practice.

Standard particlefiltering technique have previously been applied to the problem of fiber tracking by Brun et al. (2002) and Bjornemo et al. (2002). However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particlefiltering. We formulate fiber tracking using a nonlinear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particlefiltering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particlefiltering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood’s simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets.

Two-dimensional finite impulse response (FIR) filters are an important component in many image and video processing systems. The processing of complex video applications in real time requires high computational power, which can be provided using field programmable gate arrays (FPGAs) due to their inherent parallelism. The most resource-intensive components in computing FIR filters are the multiplications of the folding operation. This work proposes two optimization techniques for high-speed implementations of the required multiplications with the least possible number of FPGA components. Both methods use integer linear programming formulations which can be optimally solved by standard solvers. In the first method, a formulation for the pipelined multiple constant multiplication problem is presented. In the second method, also multiplication structures based on look-up tables are taken into account. Due to the low coefficient word size in video processing filters of typically 8 to 12 bits, an optimal solution is found for most of the filters in the benchmark used. A complexity reduction of 8.5% for a Xilinx Virtex 6 FPGA could be achieved compared to state-of-the-art heuristics.

We derive several new algorithms for particle flow with non-zero diffusion corresponding to Bayes' rule. This is unlike all of our previous particle flows, which assumed zero diffusion for the flow corresponding to Bayes' rule. We emphasize, however, that all of our particle flows have always assumed non-zero diffusion for the dynamical model of the evolution of the state vector in time. Our new algorithm is simple and fast, and it has an especially nice intuitive formula, which is the same as Newton's method to solve the maximum likelihood estimation (MLE) problem (but for each particle rather than only the MLE), and it is also the same as the extended Kalman filter for the special case of Gaussian densities (but for each particle rather than just the point estimate). All of these new flows apply to arbitrary multimodal densities with smooth nowhere vanishing non-Gaussian densities.

We describe a general approach for the representation and recognition of 3D objects, as it applies to Automatic Target Recognition (ATR) tasks. The method is based on locally adaptive target segmentation, biologically motivated image processing and a novel view selection mechanism that develops 'visual filters' responsive to specific target classes to encode the complete viewing sphere with a small number of prototypical examples. The optimal set of visual filters is found via a cross-validation-like data reduction algorithm used to train banks of back propagation (BP) neural networks. Experimental results on synthetic and real-world imagery demonstrate the feasibility of our approach.

In rotating filtration, which is based on supercritical cylindrical Couette flow with a rotating porous inner cylinder, the motion of particles in the suspension depends on both centrifugal sedimentation and transport due to the vortical motion of Taylor vortices. We have simulated the motion of dilute, rigid, spherical particles in Taylor Couette flow using computational particle tracking in an analytic velocity field for flow just above the transition to supercritical Taylor vortex flow. Neutrally buoyant particles follow fluid streamlines closely, but not exactly due to the curvature of the velocity field very near the particle. The motion of particles with a density greater than the fluid is primarily determined by the competition between the centrifugal sedimentation related to the primary cylindrical Couette flow and the secondary radial and axial transport of the Taylor vortex flow. As a result, particles that start near the outer edge of a vortex spiral inward toward a limit cycle orbit. Likewise, particles initially near the center of a vortex spiral outward toward the same limit cycle orbit. Even when a small radially inward throughflow is imposed, particles can remain trapped in retention zones that are away from the wall of the annulus. Consequently, the dynamics of the flow field result in particles tending to be transported away from the porous inner cylinder, thus contributing to the antiplugging character of rotating filter devices.

In this paper, we present a design approach for the high-level synthesis of programmable continuous-time Gm-C and active-RC filters with optimum trade-off among dynamic range, distortion products generation, area consumption and power dissipation, thus meeting the needs of more demanding baseband filter realizations. Further, the proposed technique guarantees that under all programming configurations, transconductors (in Gm-C filters) and resistors (in active-RC filters) as well as capacitors, are related by integer ratios in order to reduce the sensitivity to mismatch of the monolithic implementation. In order to solve the aforementioned trade-off, the filter must be properly scaled at each configuration. It means that filter node impedances must be conveniently altered so that the noise contribution of each node to the filter output be as low as possible, while avoiding that peak amplitudes at such nodes be so high as to drive active circuits into saturation. Additionally, in order to not degrade the distortion performance of the filter (in particular, if it is implemented using Gm-C techniques) node impedances can not be scaled independently from each other but restrictions must be imposed according to the principle of nonlinear cancellation. Altogether, the high-level synthesis can be seen as a constrained optimization problem where some of the variables, namely, the ratios among similar components, are restricted to discrete values. The proposed approach to accomplish optimum filter scaling under all programming configurations, relies on matrix methods for network representation, which allows an easy estimation of performance features such as dynamic range and power dissipation, as well as other network properties such as sensitivity to parameter variations and non-ideal effects of integrators blocks; and the use of a simulated annealing algorithm to explore the design space defined by the transfer and group delay specifications. It must be noted that such design space also includes most common approximation methods and network synthesis approaches as optimization variables, in order to make as widespread as possible the search for optimum solutions. The proposed methodology has been partially developed in MATLAB, taking advantage of the routines available in the signal processing and control toolboxes, and C++. The validity of the methodology and companying software will be demonstrated at the Conference and reported in the paper, using as a tailoring example the design of a programmable bank of filters for a high-performance powerline modem.

Delgado-Restituto, Manuel; Fernandez-Bootello, Juan F.; Rodríguez-Vázquez, Angel

Multiobjective particle swarm optimization (MOPSO) algorithms have been widely used to solve multiobjective optimization problems. Most MOPSOs use fixed momentum and acceleration for all particles throughout the evolutionary process. In this paper, we introduce a cultural framework to adapt the personalized flight parameters of the mutated particles in a MOPSO, namely momentum and personal and global accelerations, for each individual particle based upon various types of knowledge in "belief space," specifically situational, normative, and topographical knowledge. A comprehensive comparison of the proposed algorithm with chosen state-of-the-art MOPSOs on benchmark test functions shows that the movement of the individual particle using the adapted parameters assists the MOPSO to perform efficiently and effectively in exploring solutions close to the true Pareto front while exploiting a local search to attain diverse solutions. PMID:20837447

The error model of the initial alignment of the marine strap down inertial navigation system on the swaying base is nonlinear, while the azimuth angle error is large. For this nonlinear model, a new nonlinear filter called as the cubature Gaussian Particlefilter is proposed, which is based on the cubature Kalman filter and the Gaussian Particlefilter. The cubature

Weisheng Wu; Chunlei Song; Junhou Wang; Zhenzhen Long

The aim of this study was to investigate the deposition of particles in three types of synthetic fabric filter by scanning electron microscope (SEM) and to determine the experimental adhesive force of filter cakes in fabric filters. The fabrics used were acrylic, polypropylene and polyester. The particulate matter was phosphate rock. The particles were deposited in the filters during 10

E. H. Tanabe; P. M. Barros; K. B. Rodrigues; M. L. Aguiar

The effects of particle size and flow rates on respirator filter efficiency were studied. Filter efficiency for polystyrene latex spheres was investigated, and the aerosol particle size at which maximum penetration occurred. Various flow rates were used t...

The paper discusses computations on the performance of particlefilters and electronic air cleaners (EACs). The collection efficiency of particlefilters and EACs is calculable if certain factors can be assumed or calibrated. For fibrous particulate filte...

P. A. Lawless A. S. Viner D. S. Ensor L. E. Sparks

This paper proposes a method for localizing homogeneity and estimating additive white Gaussian noise (AWGN) variance in images. The proposed method uses spatially and sparsely scattered initial seeds and utilizes particlefiltering techniques to guide their spatial movement towards homogeneous locations. This way, the proposed method avoids the need to perform the full search associated with block-based noise estimation methods. To achieve this, the paper proposes for the particlefilter a dynamic model and a homogeneity observation model based on Laplacian structure detectors. The variance of AWGN is robustly estimated from the variances of blocks in the detected homogeneous areas. A proposed adaptive trimmed-mean based robust estimator is used to account for the reduction in estimation samples from the full search approach. Our results show that the proposed method reduces the number of homogeneity measurements required by block-based methods while achieving more accuracy. PMID:21138803

Optimization of fixture layout (locator and clamp locations) is critical to reduce geometric error of the workpiece during machining process. In this paper, the application of particle swarm optimization (PSO) algorithm is presented to minimize the workpiece deformation in the machining region. A PSO based approach is developed to optimize fixture layout through integrating ANSYS parametric design language (APDL) of finite element analysis to compute the objective function for a given fixture layout. Particle library approach is used to decrease the total computation time. The computational experiment of 2D case shows that the numbers of function evaluations are decreased about 96%. Case study illustrates the effectiveness and efficiency of the PSO based optimization approach.

Robust real-time tracking of non-rigid objects is a challenging task. Color is a powerful feature for tracking deformable objects in image sequences with complex backgrounds. Color distribution is applied, as it is robust to partial occlusion, is rotation and scale invariant and computationally efficient. Particlefilter has been proven very successful for non-linear and non-Gaussian estimation tracking problems. The article

The paper proposes an edge-based multi-object tracking framework which deals with tracking multiple objects with occlusions using a variational particlefilter. Object is modelled by a mixture of a non-parametric contour model and a non-parametric edge model using kernel density estimation. Visual tracking with a mixture model is formulated as a Bayesian incomplete data problem, where measurements in an image

. Shape and size optimization problems instructural design are addressed using the particle swarm optimization algorithm (PSOA).\\u000a In our implementation of the PSOA, the social behaviour of birds is mimicked. Individual birds exchange information about\\u000a their position, velocity and fitness, and the behaviour of the flock is then influenced to increase the probability of migration\\u000a to regions of high fitness.

We present results of data assimilation of ground discharge observation and remotely sensed soil moisture observations into Sacramento Soil Moisture Accounting (SACSMA) model in a small watershed (1593 km2) in Minnesota, the Unites States. Specifically, we perform assimilation experiments with Ensemble Kalman Filter (EnKF) and ParticleFilter (PF) in order to improve streamflow forecast accuracy at six hourly time step. The EnKF updates the soil moisture states in the SACSMA from the relative errors of the model and observations, while the PF adjust the weights of the state ensemble members based on the likelihood of the forecast. Results of the improvements of each filter over the reference model (without data assimilation) will be presented. Finally, the EnKF and PF are coupled together to further improve the streamflow forecast accuracy.

Hirpa, F. A.; Gebremichael, M.; Hopson, T. M.; Wojick, R.

The particlefilter is well known as a state estimation method for nonlinear and non-Gaussian system. However, particlefilter has the inherent drawbacks such as samples less of diversity and the computational complexity depends on the number of samples used for state estimation process. In this paper, the adaptive Markov chain Monte Carlo (MCMC) particlefilter is proposed in order

In this contribution we present a sensor data fusing concept utilizing particlefilters. The investigation aims at the development of a robust and easy to extend approach, capable of combining the information of different sensors. We use the particlefilters characteristics and introduce weighting functions that are multiplied during the measurement update stage of the particlefilter implementation. The concept

Particlefilter tracking, a type of sequential Monte Carlo method, has long been considered to be a very promising but time-consuming tracking technique. Methods have been developed to include a particlefilter as part of a Variable Structure, Interactive Multiple Model (VS-IMM) structure and to integrate it into the Multiple Hypothesis Tracker (MHT) scoring structure. By integrating a particlefilter

The article presents a new application of the modified H-filter with insulating rectangular blocks using negative and positive DEP for separation of multiple particles in a continuous pressure-driven flow. The multiple insulating blocks fabricated along the main channel induce spatially nonuniform electric fields which exert differential repulsive (negative) or attractive (positive) DEP forces on particles, depending on the size and the polarizability of particles relative to their suspending medium. As a result, particles of different sizes and polarizability can be separated into different outlets of the H-filter. Numerical simulations are also performed to analyze the effects of block gap and width on electric field distribution and DEP force characteristics near the insulating blocks so as to provide design guidelines for optimal structural dimensions of the microfluidic device. The device performance is demonstrated by separating a three-sized particles mixture, including 2 ?m fluorescent particles with an attractive DEP force and both 5 and 10 ?m nonfluorescent particles with differential repulsive DEP forces. High separation rate of 99% is successfully achieved. PMID:24338796

In this paper we consider the Particle Swarm Optimization (PSO) algorithm [1], [2], in the class of Evolutionary Algorithms, for the solution of global optimization problems. We analyze a couple of issues\\u000a aiming at improving both the effectiveness and the efficiency of PSO. In particular, first we recognize that in accordance\\u000a with the results in [3], the initial points configuration

Emilio F. Campana; Giovanni Fasano; Daniele Peri; Antonio Pinto

Particle Swarm Optimization is a population based search strategy based on the idea of the simulation of bird flocks. In this paper we will introduce an application of the PSO to a management problem. The simulated stochastic 2-product-warehouse with the parameters order amount and safety stock is a capital oriented model with the goal to maximize the amount of money.

In multisensor tracking system, the center processor receives the out-of-sequence measurements (OOSMs) because of communication time delays. An improved particlefilter for filtering out-of-sequence measurements with arbitrary lag was proposed. The interpolation filter is used to generate the proposal distribution for particlefilter. The proposal distributions integrate the most current observation, thus greatly improving the filter performance. The detailed implementation

Particle swarm optimization (PSO) is one of the evolutionary algorithms which proved to be useful in solving multi-robots tasks. PSO outperforms other evolutionary algorithms, such as GA, in this area. In this paper we introduce a new modified version of PSO called area extension PSO (AEPSO). Information about the environment in extended area together with various heuristics improves the performance

The performance of Particle Swarm Optimization is greatly affected by the size and sociometry of the swarm. This research proposes a dynamic sociometry, which is shown to be more effective on some problems than the standard star and ring sociometries. The performance of various combinations of swarm size and sociometry on six different test functions is qualitatively analyzed.

Task assignment is one of the core steps to effectively exploit the capabilities of distributed or parallel computing systems. The task assignment problem is an NP-complete problem. In this paper, we present a new task assignment algorithm that is based on the principles of particle swarm optimization (PSO). PSO follows a collaborative population-based search, which models over the social behavior

This paper presents an algorithm to resolve superimposed action potentials encountered during the decomposition of electromyographic signals. The algorithm uses particle swarm optimization with a variety of features including randomization, cross-over, and multiple swarms. In a simulation study involving realistic superpositions of 2-5 motor-unit action potentials, the algorithm had an accuracy of 98%.

Cigarette butts collected from crime scenes can play an important role in forensic investigations by providing a DNA link to a victim or suspect. Microscopic particles can frequently be seen on smoked cigarette filters with stereomicroscopy. The authors are not aware of previous published attempts to identify this material. These particles were examined with transmission and scanning electron microscopy and were found to consist of two types of superficial epithelial tissue, consistent with two areas of the lip surface. The particles were often composed of several layers of non-nucleated and nucleated epithelium with the former being the most common. It was further determined that both of these cell types are easily transferred from the lip. The results of this study indicate that the most visible source of DNA obtained from cigarette butts and other objects in contact with the lip may be lip epithelial tissue. PMID:19291443

An initial version of an optimization code utilizing two-dimensional radiation transport methods has been completed. This code is capable of predicting material compositions of a beam tube-filter geometry which can be used in a boron neutron capture therapy treatment facility to improve the ratio of the average radiation dose in a brain tumor to that in the healthy tissue surrounding the tumor. The optimization algorithm employed by the code is very straightforward. After an estimate of the gradient of the dose ratio with respect to the nuclide densities in the beam tube-filter geometry is obtained, changes in the nuclide densities are made based on: (1) the magnitude and sign of the components of the dose ratio gradient, (2) the magnitude of the nuclide densities, (3) the upper and lower bound of each nuclide density, and (4) the linear constraint that the sum of the nuclide density fractions in each material zone be less than or equal to 1.0. A local optimal solution is assumed to be found when one of the following conditions is satisfied in every material zone: (1) the maximum positive component of the gradient corresponds to a nuclide at its maximum density and the sum of the density fractions equals 1.0 or, and (2) the positive and negative components of the gradient correspond to nuclides densities at their upper and lower bounds, respectively, and the remaining components of the gradient are sufficiently small. The optimization procedure has been applied to a beam tube-filter geometry coupled to a simple tumor-patient head model and an improvement of 50% in the dose ratio was obtained.

For state estimation problem, particlefilter is generally used to construct the posterior probability density function by a set of particles, which is regarded as a solution to state estimation. Many techniques have been developed to improve performance of particlefilter, at the cost of largely increased computational burden for each particle. In this paper, we propose a post-resampling based

The beam irradiation system for particle therapy can use a passive or an active beam irradiation method. In the case of an active beam irradiation, using a ridge filter would be appropriate to generate a spread-out Bragg peak (SOBP) through a large scanning area. For this study, a ridge filter was designed as an energy modulation device for a prototype active scanning system at MC-50 in Korea Institute of Radiological And Medical Science (KIRAMS). The ridge filter was designed to create a 10 mm of SOBP for a 45-MeV proton beam. To reduce the distal penumbra and the initial dose, [DM] determined the weighting factor for Bragg Peak by applying an in-house iteration code and the Minuit Fit package of Root. A single ridge bar shape and its corresponding thickness were obtained through 21 weighting factors. Also, a ridge filter was fabricated to cover a large scanning area (300 × 300 mm2) by Polymethyl Methacrylate (PMMA). The fabricated ridge filter was tested at the prototype active beamline of MC-50. The SOBP and the incident beam distribution were obtained by using HD-810 GaF chromatic film placed at a right triangle to the PMMA block. The depth dose profile for the SOBP can be obtained precisely by using the flat field correction and measuring the 2-dimensional distribution of the incoming beam. After the flat field correction is used, the experimental results show that the SOBP region matches with design requirement well, with 0.62% uniformity.

Kim, Chang Hyeuk; Han, Garam; Lee, Hwa-Ryun; Kim, Hyunyong; Jang, Hong Suk; Kim, Jeong Hwan; Park, Dong Wook; Jang, Sea Duk; Hwang, Won Taek; Kim, Geun-Beom; Yang, Tae-Keun

This paper presents an Adaptive Particle Swarm Optimization (APSO) for Unit Commitment (UC) problem. APSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable size and load demand, adaptive criteria are applied on PSO parameters and the global best particle (knowledge) based on the diversity of fitness. In this proposed method, PSO parameters are automatically adjusted using Gaussian modification. To increase the knowledge, the global best particle is updated instead of a fixed one in each generation. To avoid the method to be frozen, idle particles are reset. The real velocity is digitized (0/1) by a logistic function for binary UC. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.

This paper adresses the issue of generating a panoramic view and a panoramic depth maps using only a single camera. The proposed approach first estimates the egomotion of the camera. Based on this information, a particlefilter approximates the 3D structure of the scene. Hence, 3D scene points are modeled probabilistically. These points are accumulated in a cylindric coordinate system. The probabilistic representation of 3D points is used to handle the problem of visualizing occluding and occluded scene points in a noisy environment to get a stable data visualization. This approach can be easily extended to calibrated multi-camera applications (even with non-overlapping field of views).

With the rapid developments in computer technology, the particlefilter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter

A fibrous filter is a common cleaning device often used to remove particles from industrial gas streams. The main question which often arises concerns the evolution of the pressure drop and the filtration efficiency during the filter clogging. In the present study, the loading characteristics of HEPA filters have been studied experimentally. The increase of pressure drop and filter efficiency

D. Thomas; P. Penicot; P. Contal; D. Leclerc; J. Vendel

The particlefilter offers a general numerical tool to approximate the posterior density function for the state in nonlin- ear and non-Gaussian filtering problems. While the particlefilter is fairly easy to implement and tune, its main drawback is that it is quite computer intensive, with the computational complexity increasing quickly with the state dimension. One remedy to this problem

Thomas B. Schön; Fredrik Gustafsson; Per-johan Nordlund

This paper presents a sequential filter implementation of particle Probability Hypothesis Density (PHD) filter for multisensor multi-target tracking. The tracking system involves potentially nonlinear target dynamics described by Markov state space model and nonlinear measurements. Each sensor reports measurements to the tracking system, which performs sequential estimation of the current state using the particle PHD filter, which propagates only the

Meng Fanbin; Hao Yanling; Xia Quanxi; OuYang Taishan; Zou Wei

As evidenced by the recent works of many researchers, the particle-filtering (PF) framework has revolutionized probabilistic visual target tracking. In this paper, we present a new particlefilter tracking algorithm that incorporates the multiple-model (MM) paradigm and the technique of state partitioning with parallel filters. Traditionally, most tracking algorithms assume that a target operates according to a single dynamic model.

Yan Zhai; Mark B. Yeary; Samuel Cheng; Nasser D. Kehtarnavaz

In radar target tracking application, the observation noise is usually non-Gaussian, which is also referred to as glint noise. The performances of conventional trackers degrade severely in the presence of glint noise. An improved particlefilter, Markov chain Monte Carlo iterated extended Kalman particlefilter (MCMC-IEKPF), is applied to this problem. The tracking performance of the filter is evaluated and

The paper deals with state estimation for the track-before-detect approach using the particlefilter. The focus is aimed at the track initiation proposal density of the particlefilter which considerably affects estimate quality. The goal of the paper is to design a proposal based on a Gaussian mixture using a bank of extended Kalman filters. This leads to root mean

The multi-dimensional quality open linear dynamical system with observation and feedback along a quantum linear transmission line is studied in discrete time. The linear least squares filtering and optimal control strategies are obtained as quantum analogies of the Kalman filter and Bellman dynamical programming. The duality of quantum filtering and optimal feedback control is observed for this particular case.

A new cluster-structured Particle Swarm Optimization (PSO) with interaction and diversity of parameters is proposed in this letter. After a swarm of PSO is divided into some sub-swarms (clusters), interactions between sub-swarms and diversity of PSO parameters are added so as to improve the search ability of PSO in the proposed cluster-structured PSO. The feasibility and the advantage of the proposed cluster-structured PSO are demonstrated through numerical simulations using two typical optimization test problems.

This paper demonstrates that inverse source reconstruction can be performed using a methodology of particlefilters that relies primarily on the Bayesian approach of parameter estimation. In particular, the proposed approach is applied in the context of nearfield acoustic holography based on the equivalent source method (ESM). A state-space model is formulated in light of the ESM. The parameters to estimate are amplitudes and locations of the equivalent sources. The parameters constitute the state vector which follows a first-order Markov process with the transition matrix being the identity for every frequency-domain data frame. Filtered estimates of the state vector obtained are assigned weights adaptively. The implementation of recursive Bayesian filters involves a sequential Monte Carlo sampling procedure that treats the estimates as point masses with a discrete probability mass function (PMF) which evolves with iteration. The weight update equation governs the evolution of this PMF and depends primarily on the likelihood function and the prior distribution. It is evident from the simulation results that the inclusion of the appropriate prior distribution is crucial in the parameter estimation. PMID:23742356

Ammonium is an important constituent of fine particulate mass in the atmosphere, but can be difficult to quantify due to possible sampling artifacts. Losses of semivolatile species such as NH4NO3 can be particularly problematic. In order to evaluate ammonium losses from aerosol particles collected on filters, a series of field experiments was conducted using denuded nylon and Teflon filters at Bondville, Illinois (February 2003), San Gorgonio, California (April 2003 and July 2004), Grand Canyon National Park, Arizona (May, 2003), Brigantine, New Jersey (November 2003), and Great Smoky Mountains National Park (NP), Tennessee (July–August 2004). Samples were collected over 24-hr periods. Losses from denuded nylon filters ranged from 10% (monthly average) in Bondville, Illinois to 28% in San Gorgonio, California in summer. Losses on individual sample days ranged from 1% to 65%. Losses tended to increase with increasing diurnal temperature and relative humidity changes and with the fraction of ambient total N(--III) (particulate NH4+ plus gaseous NH3) present as gaseous NH3. The amount of ammonium lost at most sites could be explained by the amount of NH4NO3 present in the sampled aerosol. Ammonium losses at Great Smoky Mountains NP, however, significantly exceeded the amount of NH4NO3 collected. Ammoniated organic salts are suggested as additional important contributors to observed ammonium loss at this location.

Yu, Xiao-Ying; Lee, Taehyoung; Ayres, Benjamin; Kreidenweis, Sonia M.; Malm, William C.; Collett, Jeffrey L.

Standard particlefiltering technique have previously been applied to the problem of fiber tracking by Brun et al. [Brun, A., Bjornemo, M., Kikinis, R., Westin, C.F., 2002. White matter tractography using sequential importance sampling. In: Proceedings of the ISMRM Annual Meeting, p. 1131] and Bjornemo et al. [Bjornemo, M., Brun, A., Kikinis, R., Westin, C.F., 2002. Regularized stochastic white matter tractography using diffusion tensor MRI, In: Proc. MICCAI, pp. 435-442]. However, these previous attempts have not utilised the full power of the technique, and as a result the fiber paths were tracked in a goal directed way. In this paper, we provide an advanced technique by presenting a fast and novel probabilistic method for white matter fiber tracking in diffusion weighted MRI (DWI), which takes advantage of the weighting and resampling mechanism of particlefiltering. We formulate fiber tracking using a non-linear state space model which captures both smoothness regularity of the fibers and the uncertainties in the local fiber orientations due to noise and partial volume effects. Global fiber tracking is then posed as a problem of particlefiltering. To model the posterior distribution, we classify voxels of the white matter as either prolate or oblate tensors. We then construct the orientation distributions for prolate and oblate tensors separately. Finally, the importance density function for particlefiltering is modeled using the von Mises-Fisher distribution on a unit sphere. Fast and efficient sampling is achieved using Ulrich-Wood's simulation algorithm. Given a seed point, the method is able to rapidly locate the globally optimal fiber and also provides a probability map for potential connections. The proposed method is validated and compared to alternative methods both on synthetic data and real-world brain MRI datasets. PMID:18602332

While the world is stepping into the aging society, rehabilitation robots play a more and more important role in terms of both rehabilitation treatment and nursing of the patients with neurological diseases. Benefiting from the abundant contents of movement information, electroencephalography (EEG) has become a promising information source for rehabilitation robots control. Although the multiple linear regression model was used as the decoding model of EEG signals in some researches, it has been considered that it cannot reflect the nonlinear components of EEG signals. In order to overcome this shortcoming, we propose a nonlinear decoding model, the particlefilter model. Two- and three-dimensional decoding experiments were performed to test the validity of this model. In decoding accuracy, the results are comparable to those of the multiple linear regression model and previous EEG studies. In addition, the particlefilter model uses less training data and more frequency information than the multiple linear regression model, which shows the potential of nonlinear decoding models. Overall, the findings hold promise for the furtherance of EEG-based rehabilitation robots.

In this article, efficient hardware architectures for particlefilter (PF) are presented. We propose three different architectures for Sequential Importance Resampling Filter (SIRF) implementation. The first architecture is a two-step sequential PF machine, where particle sampling, weight, and output calculations are carried out in parallel during the first step followed by sequential resampling in the second step. For the weight computation step, a piecewise linear function is used instead of the classical exponential function. This decreases the complexity of the architecture without degrading the results. The second architecture speeds up the resampling step via a parallel, rather than a serial, architecture. This second architecture targets a balance between hardware resources and the speed of operation. The third architecture implements the SIRF as a distributed PF composed of several processing elements and central unit. All the proposed architectures are captured using VHDL synthesized using Xilinx environment, and verified using the ModelSim simulator. Synthesis results confirmed the resource reduction and speed up advantages of our architectures.

Abd El-Halym, Howida A.; Mahmoud, Imbaby Ismail; Habib, SED

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.

A brief account of the quantum information dynamics and dynamical programming methods for optimal control of quantum unstable systems is given to both open loop and feedback control schemes corresponding respectively to deterministic and stochastic semi-Markov dynamics of stable or unstable systems. For the quantum feedback control scheme, we exploit the separation theorem of filtering and control aspects as in the usual case of quantum stable systems with non-demolition observation. This allows us to start with the Belavkin quantum filtering equation generalized to demolition observations and derive the generalized Hamilton-Jacobi-Bellman equation using standard arguments of classical control theory. This is equivalent to a Hamilton-Jacobi equation with an extra linear dissipative term if the control is restricted to Hamiltonian terms in the filtering equation. An unstable controlled qubit is considered as an example throughout the development of the formalism. Finally, we discuss optimum observation strategies to obtain a pure quantum qubit state from a mixed one. PMID:23091216

Energetic particles (EP) ejected from a plasma carry important information about the plasma physics. To study remote plasmas in the heliosphere, space-based sensors must be used. Furthermore, only energetic neutral atoms (ENAs) can be analyzed, since charged particle trajectories are curved by the electric and magnetic fields of the heliosphere. Because low power consumption and weight are important for spacecraft, solid-state detectors are used. The challenge with solid-state detectors is their sensitivity to light; in all observational regions of interest, photon counts are several orders of magnitude higher than ENA counts. Current state of the art solid-state detectors use ultra-thin metal or carbon films to block the photons. This sets an energy threshold for the ENAs due to the fact that the ENAs have to penetrate this film. We aim to replace the thin films with carbon nanotube (CNT) mats. The CNT mats have a much lower density while maintaining extremely high photon absorption. Thus the CNT mats will act as an excellent filter for blocking the photons while minimally affecting the ENAs of interest. We will describe the fabrication of the CNT mats and their performance characterization by optical spectroscopy and energetic particle spectroscopy using alpha particles as an ENA simulant.

Deglau, David; Papadakis, Stergios; Monica, Andrew; Andrews, Bruce; Mitchell, Donald

Linear Combination of Order Statistics (LOS) filters are a special case of the Choquet integral filters. LOS are a class of nonlinear filters parameterized by a set of n weights. Different values of the weights lead to different filters. Examples include the median and other order statistic filters, local averaging filters, and trimmed average filters. Differences of LOS filters have been used in the past as target detection filters by nonlinearly comparing a small, targets size region with the surrounding region. The delta operator, proposed by Gelenbe et. al. for land mine detection, can be represented as a special case of a difference of LOS operators. Weights of LOS operators can be determined by solving an optimization problem, represented as a quadratic program. In this paper, experiments are conducted in determining optimal differences of LOS operators using the DARPA backgrounds data. The results are that the delta-operator is the solution of the optimization problem for this data set.

Hocaoglu, Ali K.; Gader, Paul D.; Gelenbe, Erol; Kocak, Taskin

A parallel Particle Swarm Optimization (PSO) algorithm is presented. Particle swarm optimization is a fairly recent addition to the family of non-gradient based, probabilistic search algorithms that is based on a simplified social model and is closely tie...

The paper provides technical analysis and implementation cost assessment of Sigma-Point Kalman Filtering and ParticleFiltering in autonomous navigation systems. As a case study, the sensor fusion-based navigation of an unmanned aerial vehicle (UAV) is examined. The UAV tracks a desirable flight trajectory by fusing measurements coming from its Inertial Measurement Unit (IMU) and measurements which are received from a

This paper addresses the problem of object tracking in diving video sequences by particlefilter. Because the diversity of motions in diving video sequences such as bouncing on the springboard, somersaulting in the air increases the difficulty to construct particle motion model, this paper presents an object tracking method in diving video sequences by particlefilter with multiple motion models.

Particlefilters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particlefilter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particlefilter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particlefiltering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particlefilter. PMID:17357741

We outline a method, using simulated annealing algorithm, to optimize the dilation factor of the wavelet function, from a number of images. The optimized dilation factor has been used to obtain wavelet matched filters (WMFs) for different fingerprint images. A single WMF instead of a bank of filters can be used for identifying each image. The filter performance was tested in terms of discrimination ratio, peak-to-correlation plane energy ratio, peak-to-sidelobe ratio and average similarity measure with digitally degraded fingerprints. The performance has been compared with that of a classical matched filter and a phase only filter. The filter performance has also been tested for noisy images.

In recent work (arXiv:1006.3100v1), we have presented a novel approach for improving particlefilters for multi-target tracking. The suggested approach was based on drift homotopy for stochastic differential equations. Drift homotopy was used to design a Markov Chain Monte Carlo step which is appended to the particlefilter and aims to bring the particlefilter samples closer to the observations.

This study covers the shear strength measurements of filter cakes from very fine fractions of different minerals (TiO2, calcite fractions, kaolin, labosil and synthetically prepared glass spheres). The dependence of the shear strength of filter cakes on the applied pressure, particle size and particle shape has been analyzed. It has been found from the laboratory scale experiments that the shear

Particle Swarm Optimization (PSO) has received increased attention in the optimization research community since its first appear- ance. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOP- SOs) can be found in the specialized literature. Unfortunately, no exper- imental comparisons have been made in order to clarify which version of MOPSO shows the best

Juan José Durillo; José García-nieto; Antonio J. Nebro; Carlos A. Coello Coello; Francisco Luna; Enrique Alba

Wet particle reduction during filter installation and start-up aligns closely with initiatives to reduce both chemical consumption and preventative maintenance time. The present study focuses on the effects of filter materials cleanliness on wet particle defectivity through evaluation of filters that have been treated with a new enhanced cleaning process focused on organic compounds reduction. Little difference in filter performance is observed between the two filter types at a size detection threshold of 60 nm, while clear differences are observed at that of 26 nm. It can be suggested that organic compounds can be identified as a potential source of wet particles. Pall recommends filters that have been treated with the special cleaning process for applications with a critical defect size of less than 60 nm. Standard filter products are capable to satisfy wet particle defect performance criteria in less critical lithography applications.

For the purpose of accumulation of particulate matter from Diesel engine exhaust gas, particlefilters are used (referred to as DPF or FAP filters in the automotive industry). However, the cost of these filters is quite high. As the emission limits become stricter, the requirements for PM collection are rising accordingly. Particulate matters are very dangerous for human health and these are not invisible for human eye. They can often cause various diseases of the respiratory tract, even what can cause lung cancer. Performed numerical simulations were used to analyze particlefilter behavior under various operating modes. The simulations were especially focused on selected critical states of particlefilter, when engine is switched to emergency regime. The aim was to prevent and avoid critical situations due the filter behavior understanding. The numerical simulations were based on experimental analysis of used diesel particlefilters.

The symmetric measurement equation (SME) approach to multiple target tracking is revisited using unscented Kalman and particlefilters. The unscented Kalman filter (UKF) promises more accurate approximation of nonlinearities and simpler implementation of the SME approach than the EKF. The particlefilter implementation offers the ability to explore the limits of the SME approach. In the first portion of this paper, experiences with SME for tracking one-dimensional motion are reviewed. The second portion of this paper discusses the challenges that arise when using the SME approach to track two-dimensional motion and introduces a new set of two-dimensional SME equations. Finally, Taylor series expansions are used to explore differences between Kalman filter-SME pairings. Using the Taylor series representation, we show how the choice of SME formulation affects the representation, and consequently approximation, of uncertainty in the Kalman filters.

This paper presents a novel method for robust object tracking in video sequences using a hybrid feature-based observation model in a particlefiltering framework. An ideal observation model should have both high ability to accurately distinguish objects from the background and high reliability to identify the detected objects. Traditional features are better at solving the former problem but weak in solving the latter one. To overcome that, we adopt a robust and dynamic feature called Grayscale Arranging Pairs (GAP), which has high discriminative ability even under conditions of severe illumination variation and dynamic background elements. Together with the GAP feature, we also adopt the color histogram feature in order to take advantage of traditional features in resolving the first problem. At the same time, an efficient and simple integration method is used to combine the GAP feature with color information. Comparative experiments demonstrate that object tracking with our integrated features performs well even when objects go across complex backgrounds.

By assuming that orientation information of brain white matter fibers can be inferred from Diffusion Weighted Magnetic Resonance Imaging (DWMRI) measurements, tractography algorithms provide an estimation of the brain connectivity in-vivo. The two key ingredients of tractography are the diffusion model (tensor, high-order tensor, Q-ball, etc.) and the way to deal with uncertainty during the tracking process (deterministic vs probabilistic). In this paper, we investigate the use of an analytical Q-ball model for the diffusion data within a well-formalized particlefiltering framework. The proposed method is validated and compared to other tracking algorithms on the MICCAI'09 contest Fiber Cup phantom and on in-vivo brain DWMRI data. PMID:21995031

ParticleFiltering (PF) is used in prognostics applications by reason of its capability of robustly predicting the future behavior of an equipment and, on this basis, its Residual Useful Life (RUL). It is a model-driven approach, as it resorts to analytical models of both the degradation process and the measurement acquisition system. This prevents its applicability to the cases, very common in industry, in which reliable models are lacking. In this work, we propose an original method to extend PF to the case in which an analytical measurement model is not available whereas, instead, a dataset containing pairs «state-measurement» is available. The dataset is used to train a bagged ensemble of Artificial Neural Networks (ANNs) which is, then, embedded in the PF as empirical measurement model.

Aerial surveillance is a main functionality of UAV, which is realized via video camera. During the operations, the mission assigned targets always are the kinetic objects, such as people or vehicles. Therefore, object tracking is taken as the key techniques for UAV sensor payload. Two difficulties for UAV object tracking are dynamic background and hardly predicting target's motion. To solve the problems, it employed the particlefilter in the research. Modeling the target by its characteristics, for instance, color features, it approximates the possibility density of target state with weighting sample sets, and the state vector contains position, motion vector and region parameters. The experiments demonstrate the effectiveness and robustness of the proposed method in UAV video tracking.

The properties of a particle composite can be controlled by organizing the particles into assemblies. The properties of the composite will depend on the structure of the particle assemblies, and for any give property there is some optimal structure. Through simulation and experiment we show that the application of heterodyned triaxial magnetic or electric fields generates structures that optimize the magnetic and dielectric properties of particle composites. We suggest that optimizing these properties optimizes other properties, such as transport properties, and we give as one example of this optimization the magnetostriction of magnetic particle composites formed in a silicone elastomer.

Intelligent Optimization Algorithm (IOA) mainly includes Immune Algorithm (IA) and Genetic Algorithm (GA). One of the most important characteristics of MRI is the complicated changes of gray level. Traditional filtering algorithms are not fit for MRI. Adaptive Template Filtering Method (ATFM) is an appropriate denoising method for MRI. However, selecting threshold for ATFM is a complicated problem which directly affects the denoising result. Threshold selection has been based on experience. Thus, it was lack of solid theoretical foundation. In this paper, 2 kinds of IOA are proposed for threshold optimization respectively. As our experiment demonstrates, they can effectively solve the problem of threshold selection and perfect ATFM. Through algorithm analysis, the performance of IA surpasses the performance of GA. As a new kind of IOA, IA exhibits its great potential in image processing. PMID:17945854

Ensemble based hydrologic data assimilation has the potential to accurately quantify the uncertainty in streamflow predictions by accounting for many sources of uncertainty within the modeling framework. The four main sources of error, input data, observation, model structure and parameter, can all be addressed directly with data assimilation. Due to recent improvements in parameter estimation using data assimilation, state-parameter estimation techniques have become a popular topic in the hydrologic data assimilation community. Several studies, using both the Ensemble Kalman Filter (EnKF) and the ParticleFilter (PF) to estimate both model states and parameters have been published in recent years. Though there is increasing interest and a growing literature in this area, relatively little research has been presented to examine the effectiveness and robustness of these methods to estimate uncertainty. This study highlights the need for state-parameter estimation studies to provide a more rigorous testing of these techniques with respect to uncertainty quantification. Using multiple calibration and validation replicates, a detailed analysis of the robustness of the methods is performed. The results from this study show the complexity of information from both the EnKF and the PF, and explain aspects of these techniques that have not been well documented in the current scientific literature.

In multiplatform cooperative engagement, sensor measurements usually cannot arrive at the fusion center according to detection time because of the delay for communicating time. To solve an out-of-sequence measurements filtering problem, to improve the tracking performance and to reduce the computation cost, an improved particlefiltering algorithm based unscented transforming was proposed. The filter has higher estimation accuracy, fewer computation

Jian-zhong Zhou; Shu-zong Wang; Ming-feng Zhan; Zhang-song Shi

State estimation is a major problem in mobile robot localization. To this end gaussian and nonparametric filters have been developed. In this paper the Extended Kalman Filter which assumes gaussian measurement noise is compared to the ParticleFilter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of

The symmetric measurement equation approach to multiple target tracking is revisited using unscented Kalman and particlefilters. The characteristics and performance of these filters are compared to the original symmetric measurement equation implementation relying upon an extended Kalman filter. Counter-intuitive results are presented and explained for two sets of symmetric measurement equations, including a previously unknown limitation of the unscented

An experimental study on filtration of submicron solid and liquid aerosol particles by using a filter media composed of agglomerates or granules of nanoparticles is described. Fumed silica nanoagglomerates, carbon black granules, silica shells, activated carbon granules, glass beads and nanoporous hydrophobic aerogel were among the granular filter media tested and compared to a commercially available HEPA fiber-based filter. Other

Jose Quevedo; Gaurav Patel; Robert Pfeffer; Rajesh Dave

Fibrous filters are the most common means used to separate liquid aerosol particles from an industrial gas stream. The pressure drop and penetration (=1-efficiency) are the most important performance criteria of the filter. In this study, experimental and modelling results describing the pressure drop and penetration evolution of a glass microfibre HEPA filter are presented. For the experimental part, the

Tom Frising; Dominique Thomas; Denis Bémer; Patrick Contal

A new optimization procedure suitable for the design of waveguide filters is presented. The filter structure consists of a frequency selective surface (FSS), placed on the transverse plane of a rectangular waveguide, so introducing a filtering behavior of the waveguide. Due to the boundary conditions imposed by the metallic waveguide walls, the FSS results to be infinite in extent, allowing

Agostino Monorchio; Giuliano Manara; Umberto Serra; Giovanni Marola; Enrico Pagana

Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c1 and c2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost.

Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients c 1 and c 2 in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost. PMID:24883357

\\u000a To track passive target efficiently and accurately, an improved particlefilter algorithm based on genetic algorithm (SGAPF)is\\u000a proposed.By incorporating the newest observation into sampling process and using genetic algorithm, the degeneracy problem\\u000a is overcome and the predication performance of particlefilter is improved. The improved algorithm guarantees the diversity\\u000a of the particles and particles are moved to the regions where

In this paper we use the recursive Bayesian estimation method to solve the tracking and identification problem of a target modeled by an equivalent magnetic spheroid. Target positions, velocity, heading, magnetic moments and size are defined as the state vector, which is estimated from noisy magnetic field measurements by a sequential Monte Carlo based method known as particlefilter. In order to improve the performance of the filter, the unscented Kalman filter is applied to generate the transition prior as the proposal distribution. A simulated experiment is given to test the performance of the unscented particlefilter, and the results show that the filter is suitable for magnetic target's track and identification.

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. The proposed new algorithm moves particles towards nearby particles of higher fitness, instead of attracting each particle towards just the best position discovered so far by any particle. This is accomplished by using the

Thanmaya Peram; Kalyan Veeramachaneni; Chilukuri K. Mohan

The design priority for manned space exploration missions is almost always placed on human safety. Proposed manned surface exploration tasks (lunar, asteroid sample returns, Mars) have the possibility of astronauts traveling several kilometers away from a home base. Deviations from preplanned paths are expected while exploring. In a time-critical emergency situation, there is a need to develop an optimal home base return path. The return path may or may not be similar to the outbound path, and what defines optimal may change with, and even within, each mission. A novel path planning algorithm and prototype program was developed using biologically inspired particle swarm optimization (PSO) that generates an optimal path of traversal while avoiding obstacles. Applications include emergency path planning on lunar, Martian, and/or asteroid surfaces, generating multiple scenarios for outbound missions, Earth-based search and rescue, as well as human manual traversal and/or path integration into robotic control systems. The strategy allows for a changing environment, and can be re-tasked at will and run in real-time situations. Given a random extraterrestrial planetary or small body surface position, the goal was to find the fastest (or shortest) path to an arbitrary position such as a safe zone or geographic objective, subject to possibly varying constraints. The problem requires a workable solution 100% of the time, though it does not require the absolute theoretical optimum. Obstacles should be avoided, but if they cannot be, then the algorithm needs to be smart enough to recognize this and deal with it. With some modifications, it works with non-stationary error topologies as well.

A general form of an optimal washout filter is derived using state-space linear optimal control theory, and this is applied to the design of washout filters of various types of moving base motion simulators, including the NASA's vertical motion simulator. Attention is given to the linear elements of a washout filter. One of the nonlinearities considered is braking which may be required near the end of the simulator excursion to prevent a crash. Although the general form of the optimal washout filter is applicable to time-variant system, the applications analyzed in the study are restricted to time-invariant cases.

Over the last years, particlefilters have been applied with great success to a variety of state estimation problems. In this paper we present a statistical approach to increasing the efficiency of particlefilters by adapting the size of sample sets during the estimation pro- cess. The key idea of the KLD-sampling method is to bound the approximation error intro-

This paper presents a new improved regularized particlefilter algorithm for SINS\\/SAR (Strap-down Inertial Navigation System \\/ Synthetic Aperture Radar) integrated navigation system. By adopting MCMC (Markov Chain Monte Carlo) move to the regularization process, a MCMC based filtering algorithm is developed through combining local resampling with MCMC move to prevent a large number of particles from being rejected. The

Particlefilter is a powerful visual tracking tool based on sequential Monte Carlo framework, and it needs large numbers of samples to properly approximate the posterior density of the state evolution. However, its efficiency will degenerate if too many samples are applied. In this paper, an improved particlefilter is proposed by integrating support vector regression into sequential Monte Carlo

Guangyu Zhu; Dawei Liang; Yang Liu; Qingming Huang; Wen Gao

Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. An improved Rao-Blackwellised particlefilter (IRBPF) algorithm is proposed for the mobile robot to SLAM, which can simultaneously localize the robot and build up the map in the structured indoor environment. Firstly, IRBPF respectively uses particlefilters (PF) to estimate the posterior probability distributions of

Particlefilter algorithm with adaptive process noise variance is proposed for target tracking applications in binary wireless sensor network (BWSN). The algorithm adopts updated variance of system noise to eliminate the cumulative effect of particlefilter prediction error. It has better tracking accuracy when target travel with constant velocity or variable velocity. The simulation results show that the algorithm is

Xiaodong Yang; Fenghong Xiang; Jianlin Mao; Ning Guo

We present a new tracking method with improved efficiency and accuracy based on the subspace representation and particlefilter. The subspace representation has been successfully adopted in tracking, e.g., the Eigen-tracking algorithm, and it has shown considerable robustness for tracking an object with changing appearance. Particlefilters are widely used for a wide range of tracking problems since they can

Shimin Yin; Jin Hee Na; Jin Young Choi; Songhwai Oh

Particlefilters have become popular tools for visual tracking since they do not require the modeling system to be Gaussian and linear. However, when applied to a high dimensional state-space, particlefilters can be inefficient because a prohibitively large number of samples may be required in order to approximate the underlying density functions with desired accuracy. In this paper, by

Track continuity is difficult to maintain when tracking beam aspect targets. The loss of Doppler discrimination allows clutter to mask the target return, making it nearly impossible to detect. In order to improve tracking performance, a combination particle\\/Kalman filter has been developed. The tracking filters obviate each other as appropriate. When a target enters a Doppler blind zone, the particle

D. A. Zaugg; A. A. Samuel; D. E. Waagen; H. A. Schmitt

Particlefilters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particlefilter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve

A fibrous filter is a common cleaning device often used to remove particles from industrial gas streams. A fundamental question concerns the evolution of the filter performance under aerosol load and the prediction of its life time. The purpose of this paper is to illuminate some of the issues involved, in presenting our experiments of solid and liquid particle filtration.

P. Penicot; D. Thomas; P. Contal; D. Leclerc; J. Vendel

Correlation methods are becoming increasingly attractive tools for image recognition and location. This renewed interest in correlation methods is spurred by the availability of high-speed image processors and the emergence of correlation filter designs that can optimize relevant figures of merit. In this paper, a new correlation filter design method is presented that allows one to optimally tradeoff among potentially

B. V. K. Vijaya Kumar; Abhijit Mahalanobis; Alex Takessian

The pseudoelectret fibers developed at the Applied Electrostatics Research Centre, University of Western Ontario, London, ON, Canada, have been used to build an unlimited-life high-efficiency filter for micron-sized particles entrained in up to 300°C hot exhaust gas. This pseudoelectret filter has considerable advantages when compared to mechanical or conventional electret-type filters. In a comparable unblinded mechanical filter, the pressure drop

Ion I. Inculet; G. S. Peter Castle; Mircea Slanina; Mihai Duca

This research investigated the performance of floating media filter in removing particles and dissolved organic matter from surface water. Pilot-scale study consists of floating plastic media pre-filter connected with either granular activated carbon (GAC) or sponge biological filter (BF) bed. In the floating plastic media filter, coagulation and flocculation processes using poly-aluminum chloride (PACl) as coagulant at an optimum dose

C. Chiemchaisri; S. Passananon; H. H. Ngo; S. Vigneswaran

We present a method for optimizing spatial filter performance by inserting volume Bragg gratings (VBGs) in front of the traditional spatial filters. The experimental results show that the cutoff frequency is modified with the insertion of VBGs. We also demonstrate the optimization of filtering performance in both the spatial and frequency domains, with detailed comparison of near-field modulation, contrast ratio, and power spectral density of output laser beams. PMID:24487893

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N^{3<\\/sup>) calculations per time iteration, where N is the number of message}

PID parameter optimization is an important problem in control field. Particle swarm optimization (PSO) is powerful stochastic evolutionary algorithm that is used to find the global optimum solution in search space. However, it has been observed that the standard PSO algorithm has premature and local convergence phenomenon when solving complex optimization problem. To resolve this problem an advanced particle swarm

Abolfazl Jalilvand; Ali Kimiyaghalam; Ahmad Ashouri; Meisam Mahdavi

The paper describes a new stochastic heuristic algorithm for global optimization. The new optimization algorithm, called intelligent-particle swarm optimization (IPSO), offers more intelligence to particles by using concepts such as: group experiences, unpleasant memories (tabu to be avoided), local landscape models based on virtual neighbors, and memetic replication of successful behavior parameters. The new individual complexity is amplified at the

Particle swarm optimization (PSO) is an alternative population-based evolutionary computation technique. It has been shown to be capable of optimizing hard mathematical problems in continuous or binary space. We present here a parallel version of the particle swarm optimization (PPSO) algorithm together with three communication strategies which can be used according to the independence of the data. The first strategy

Jui-fang Chang; Shu-chuan Chu; John F. Roddick; Jeng-shyang Pan

We tested the hypothesis that foreign particles shed from filters can accelerate the rate of protein aggregation and particle formation during agitation stress. Various types and brands of syringe filters were tested. Particle counts and size distribution (?1 µm) in buffer alone or in solutions of keratinocyte growth factor 2 (KGF-2) were determined with a micro-flow imaging. Submicron particle populations were characterized by dynamic light scattering. Loss of soluble protein during filtration or postfiltration incubation was determined by ultraviolet spectroscopy and bicinchoninic acid protein assay. There was a wide range (from essentially none to >100,000/mL) in the counts for at least 1 µm particles shed into buffer or KGF-2 solution from the different syringe filters (with or without borosilicate glass microfibers). Filtration of KGF-2 with units containing glass microfibers above the membrane resulted in 20%-80% loss of protein due to adsorption to filter components. Filtration with systems containing a membrane alone resulted in 0%-20% loss of KGF-2. Effects of 24-h postfiltration incubation were tested on KGF-2 solution filtered with polyether sulfone membrane filters. Loss of soluble protein and formation of particles during agitation were much greater than that in control, unfiltered KGF-2 solutions. Similar acceleration of protein aggregation and particle formation was observed when unfiltered KGF-2 solution was mixed with filtered buffer and agitated. Particle shedding from syringe filters--and the resulting acceleration of protein aggregation during agitation--varied greatly among the different syringe filters and individual units of a given filter type. Our results demonstrate that nanoparticles and microparticles shed from the filters can accelerate protein aggregation and particle formation, especially during agitation. PMID:22674153

This work develops an algorithm to initialize an Unscented Kalman Filter using a ParticleFilter for applications with initial non-Gaussian probability density functions. The method is applied to estimating the position of a road vehicle along a one-mile test track and 7 kilometer span of a highway using terrain-based localization where the pitch response of the vehicle is compared to

Aerosol particle formation and growth has been observed in aging, initially particle-free gases obtained from filtered mainstream cigarette smoke. The time scale of particle formation and growth was on the order of minutes and was highly dependent on cigarette tobacco type. Measurements by both ensemble and single particle light-scattering methods were consistent with scattering from an aerosol with a fixed

The spectrometric oil analysis(SOA) is an important technique for machine state monitoring, fault diagnosis and prognosis, and SOA based remaining useful life(RUL) prediction has an advantage of finding out the optimal maintenance strategy for machine system. Because the complexity of machine system, its health state degradation process can't be simply characterized by linear model, while particlefiltering(PF) possesses obvious advantages over traditional Kalman filtering for dealing nonlinear and non-Gaussian system, the PF approach was applied to state forecasting by SOA, and the RUL prediction technique based on SOA and PF algorithm is proposed. In the prediction model, according to the estimating result of system's posterior probability, its prior probability distribution is realized, and the multi-step ahead prediction model based on PF algorithm is established. Finally, the practical SOA data of some engine was analyzed and forecasted by the above method, and the forecasting result was compared with that of traditional Kalman filtering method. The result fully shows the superiority and effectivity of the PMID:24369656

This work present a multi-pathlight extinction approach to determine the oil mist filter efficiency based on measuring the concentration and size distribution of oil particles. Light extinction spectrum(LES) technique was used to retrieve the oil particle size distribution and concentration. The multi-path measuring cell was designed to measure low concentration and fine particles after filtering. The path-length of the measuring cell calibrated as 200 cm. The results of oil particle size with oil mist filtering were obtained as D32 = 0.9?m. Cv=1.6×10-8.

Pengfei, Yin; Jun, Chen; Huinan, Yang; Lili, Liu; Xiaoshu, Cai

The problem of optimal measurement of a signal in presence of noise is treated in detail by Baldinger and Franzen (Adv. Electron. Electron Phys. 8 (1956) 225), Radeka and Karlovac (Nucl. Instr. and Meth. 52 (1967) 86) and Gatti and Manfredi (La Rivista Nuovo Cimento 9(1) (1986) 1), and the filter transfer function optimizing the signal over noise ratio is well known. These calculations deals with unconstrained optimization, that is the filter transfer function may assume any value. In this paper functional analysis techniques are applied to optimize the filter transfer function in presence of linear constraints.

This paper presents a variant of particle swarm optimizers (PSOs) that we call the comprehensive learning particle swarm optimizer (CLPSO), which uses a novel learning strategy whereby all other particles' historical best information is used to update a particle's velocity. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. Experiments were conducted (using codes

Jing J. Liang; A. Kai Qin; Ponnuthurai Nagaratnam Suganthan; S. Baskar

Gradient based approaches for motion estimation (optical-flow) estimate the motion of an image sequence based on local changes in the image intensities. In order to best evaluate local changes in the intensities, specific filters are applied to the image sequence. These filters are typically composed of spatio-temporal derivatives. The design of these filters plays an important role in the estimation

Sequential importance resampling (SIR) filter, residual resampling filter (RR), and an ensemble Kalman (EnKF) filter that can handle dynamic nonlinear\\/non-Gaussian models are compared to correct erroneous model inputs and to obtain a rainfall-runoff update with a conceptual rainfall-runoff model HBV-96 for flood forecasting purposes. EnKF performs best with a low number of ensemble members. The RR filter performs best at

A matched filter method is provided for obtaining improved particle size estimates from digital in-line holograms. This improvement is relative to conventional reconstruction and pixel counting methods for particle size estimation, which is greatly limited by the CCD camera pixel size. The proposed method is based on iterative application of a sign matched filter in the Fourier domain, with sign meaning the matched filter takes values of ±1 depending on the sign of the angular spectrum of the particle aperture function. Using simulated data the method is demonstrated to work for particle diameters several times the pixel size. Holograms of piezoelectrically generated water droplets taken in the laboratory show greatly improved particle size measurements. The method is robust to additive noise and can be applied to real holograms over a wide range of matched-filterparticle sizes. PMID:22714295

The transmission fluctuation spectrometry (TFS) is a recently-developed method for real-time, online\\/inline particle analysis in two-phase flows, whereby the particle size distribution (PSD) and particle concentration can be measured simultaneously. This study presents a new technique of data processing to the fluctuating transmission signal. Instead of low-pass filters, band-pass filters are employed to improve the resolution of the measurement on

\\u000a Tracking a very actively maneuvering object is challenging due to the lack of state transition dynamics to describe the system’s\\u000a evolution. In this paper, a coarse-to-fine particlefilter algorithm is proposed for such tracking, whereby one loop of the\\u000a traditional particlefiltering approach is divided into two stages. In the coarse stage, the particles adopt a uniform distribution\\u000a which is

High Efficiency Particulate Air (HEPA) Filters used to protect personnel, the public and the environment from airborne radioactive materials are designed, manufactured and qualified in accordance with ASME AG-1 Code section FC (HEPA Filters) [1]. The qualification process requires that filters manufactured in accordance with this ASME AG-1 code section must meet several performance requirements. These requirements include performance specifications for resistance to airflow, aerosol penetration, resistance to rough handling, resistance to pressure (includes high humidity and water droplet exposure), resistance to heated air, spot flame resistance and a visual/dimensional inspection. None of these requirements evaluate the particle loading capacity of a HEPA filter design. Concerns, over the particle loading capacity, of the different designs included within the ASME AG-1 section FC code[1], have been voiced in the recent past. Additionally, the ability of a filter to maintain its integrity, if subjected to severe operating conditions such as elevated relative humidity, fog conditions or elevated temperature, after loading in use over long service intervals is also a major concern. Although currently qualified HEPA filter media are likely to have similar loading characteristics when evaluated independently, filter pleat geometry can have a significant impact on the in-situ particle loading capacity of filter packs. Aerosol particle characteristics, such as size and composition, may also have a significant impact on filter loading capacity. Test results comparing filter loading capacities for three different aerosol particles and three different filter pack configurations are reviewed. The information presented represents an empirical performance comparison among the filter designs tested. The results may serve as a basis for further discussion toward the possible development of a particle loading test to be included in the qualification requirements of ASME AG-1 Code sections FC and FK[1]. (authors)

Stillo, Andrew [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States)] [Camfil Farr, 1 North Corporate Drive, Riverdale, NJ 07457 (United States); Ricketts, Craig I. [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)] [New Mexico State University, Department of Engineering Technology and Surveying Engineering, P.O. Box 30001 MSC 3566, Las Cruces, NM 88003-8001 (United States)

This paper introduces a recursive particlefiltering algorithm designed to filter high dimensional systems with complicated non-linear and non-Gaussian effects. The method incorporates a parallel marginalization (PMMC) step in conjunction with the hybrid Monte Carlo (HMC) scheme to improve samples generated by standard particlefilters. Parallel marginalization is an efficient Markov chain Monte Carlo (MCMC) strategy that uses lower dimensional approximate marginal distributions of the target distribution to accelerate equilibration. As a validation the algorithm is tested on a 2516 dimensional, bimodal, stochastic model motivated by the Kuroshio current that runs along the Japanese coast. The results of this test indicate that the method is an attractive alternative for problems that require the generality of a particlefilter but have been inaccessible due to the limitations of standard particlefiltering strategies.

Weare, Jonathan [Courant Institute, New York University, 251 Mercer Street, New York, NY 10012 (United States)], E-mail: weare@cims.nyu.edu

Equations of filtration of suspensions to form an incompressible cake of particles on the surface of the filter with simultaneous passage of a certain share of the particles from the cake to the filter's pore space and next to the region of a filtered liquid are derived from the principles of the mechanics of multiphase media. The influence of the travel of the particles in the region of the cake and the filter on the dynamics of growth of the cake bed is investigated. An analysis of the derived dynamic filtration equations shows that allowance for the factors of travel and accumulation of particles in the cake and the filter causes their total filtration resistance, in particular the resistance in the inertial component of the filtration law, to decrease.

The present research work presents a novel control scheme for tuning PID controllers using Internal Model control with the filter time constant optimized using Bee colony Optimization technique. PID controllers are used widely in Industrial Processes. Tuning of PID controllers is accomplished using Internal Model control scheme. IMC includes tuning of filter constant ?. Compromise is made in selecting the filter constant ? since an increased value of ? results in a sluggish response whereas decreased value of filter constant leads in an aggressive action. In the present work, an attempt has been made to optimize the value of the ? by Bee colony optimization technique. Simulation results show the validity of the proposed scheme for the PID controller tuning.

We propose a multi-target tracking algorithm based on the Probabil- ity Hypothesis Density (PHD) filter and data association using graph matching. The PHD filter is used to compensate for miss-detections and to remove noise and clutter. This filter propagates the first order moment of the multi-target posterior (instead of the full posterior) to reduce the growth in complexity with the

Emilio Maggio; Elisa Piccardo; Carlo S. Regazzoni; Andrea Cavallaro

A measure of the peak-to-sidelobe performance for correlation filters is defined. The phase-only filter is then shown to be optimum with respect to the peak-to-sidelobe criterion. The phase-only filter has been previously shown to give optimum signal-to-noise performance. Thus, under the assumption of a unit modulus phase device, optimum peak-to-sidelobe and signal-to-noise performance can be obtained simultaneously.

We investigate the most appropriate way to optically characterize the materials and predict the spectral responses of metal-dielectric filters in the visible range. Special attention is given to thin silver layers that have a major impact on the filter's spectral transmittance and reflectance. Two characterization approaches are compared, based either on single layers, or on multilayer stacks, in approaching the filter design. The second approach is preferred, because it gives the best way to predict filter characteristics. Meanwhile, it provides a stack model and dispersion relations that can be used for filter design optimization. PMID:24663425

Frey, Laurent; Parrein, Pascale; Virot, Léopold; Pellé, Catherine; Raby, Jacques

Computer simulations of photorefractive thresholding filters for the reduction of artifact or dust noise demonstrate an increase in signal-to-noise ratio (SNR) of 70% to 95%, respectively, of that provided by the Wiener filter for inputs with a SNR of approximately 3. These simple, nearly optimalfilters use a spectral thresholding profile that is proportional to the envelope of the noise spectrum. Alternative nonlinear filters with either 1/ nu or constant thresholding profiles increase the SNR almost as much as the noise-envelope thresholding filter.

Fu, Jack; Khoury, Jehad; Cronin-Golomb, Mark; Woods, Charles L.

In this project, a computational modeling approach for analyzing flow and ash transport and deposition in filter vessels was developed. An Eulerian-Lagrangian formulation for studying hot-gas filtration process was established. The approach uses an Eulerian analysis of gas flows in the filter vessel, and makes use of the Lagrangian trajectory analysis for the particle transport and deposition. Particular attention was given to the Siemens-Westinghouse filter vessel at Power System Development Facility in Wilsonville in Alabama. Details of hot-gas flow in this tangential flow filter vessel are evaluated. The simulation results show that the rapidly rotation flow in the spacing between the shroud and the vessel refractory acts as cyclone that leads to the removal of a large fraction of the larger particles from the gas stream. Several alternate designs for the filter vessel are considered. These include a vessel with a short shroud, a filter vessel with no shroud and a vessel with a deflector plate. The hot-gas flow and particle transport and deposition in various vessels are evaluated. The deposition patterns in various vessels are compared. It is shown that certain filter vessel designs allow for the large particles to remain suspended in the gas stream and to deposit on the filters. The presence of the larger particles in the filter cake leads to lower mechanical strength thus allowing for the back-pulse process to more easily remove the filter cake. A laboratory-scale filter vessel for testing the cold flow condition was designed and fabricated. A laser-based flow visualization technique is used and the gas flow condition in the laboratory-scale vessel was experimental studied. A computer model for the experimental vessel was also developed and the gas flow and particle transport patterns are evaluated.

This paper addresses optimal estimation for time-varying autoregressive (TVAR) models. First, we propose a statistical model on the time evolution of the frequencies, moduli and real poles instead of a standard model on the AR coefficients, as it makes more sense from a physical viewpoint. Second, optimal estimation involves solving a complex optimalfiltering problem which does not admit any

A shroud tube was used to decrease the amount of particles toward the bag filters from whole particles entering a filter vessel. The effects of the shroud tube on the flow field and particle behavior inside the vessel were studied. The air mixed with dust particles enters the vessel through a tangential inlet duct. Some of the particles are deposited

Seok Joo Park; Ho Kyung Choi; Young Ok Park; Jae Ek Son

In this paper, a novel multiple objects detection and tracking approach based on support vector machine and particlefilter is proposed to track players in broadcast sports video. Com- pared with previous work, the contributions of this paper are focused on three aspects. First, an improved particlefilter called SVR particlefilter is proposed as the player tracker by integrating

During a January 1991 Westinghouse Internal Audit of the WIPP Radiological Air Monitoring Program, an auditor observed that on an Eberline Alpha-6A CAM filter, some particulate was deposited outside the 25 mm diameter area that the filter is planned to us...

In INS\\/GPS integration, the data fusion algorithm involves properly handling of nonlinear models. Therefore the nonlinear filtering methods have been commonly applied in the INS\\/GPS integration to estimate the state vector. The most popular and commonly used method is the Extended Kalman Filter (EKF) which approximates the nonlinear state and measurement equations using the first order Taylor series expansion. On

We assimilate satellite observations of surface chlorophyll into a three-dimensional biological ocean model in order to improve its state estimates using a particlefilter referred to as sequential importance resampling (SIR). ParticleFilters represent an alternative to other, more commonly used ensemble-based state estimation techniques like the ensemble Kalman filter (EnKF). Unlike the EnKF, ParticleFilters do not require normality assumptions about the model error structure and are thus suitable for highly nonlinear applications. However, their application in oceanographic contexts is typically hampered by the high dimensionality of the model's state space. We apply SIR to a high-dimensional model with a small ensemble size (20) and modify the standard SIR procedure to avoid complications posed by the high dimensionality of the model state. Two extensions to the SIR include a simple smoother to deal with outliers in the observations, and state-augmentation which provides the SIR with parameter memory. Our goal is to test the feasibility of biological state estimation with SIR for realistic models. For this purpose we compare the SIR results to a model simulation with optimal parameters with respect to the same set of observations. By running replicates of our main experiments, we assess the robustness of our SIR implementation. We show that SIR is suitable for satellite data assimilation into biological models and that both extensions, the smoother and state-augmentation, are required for robust results and improved fit to the observations.

This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization.\\u000a NSPSO extends the basic form of PSO by making a better use of particles’ personal bests and offspring for more effective nondomination\\u000a comparisons. Instead of a single comparison between a particle’s personal best and its offspring, NSPSO compares all particles’\\u000a personal bests and their

In this paper, a low voltage current conveyor (CCII) based multifunction filter is presented. Firstly, thanks to an optimizing heuristic, an optimal sizing of a low voltage low power CMOS current conveyor (CCII) was done. Hence, we improve static and dynamic performances of this configuration. The optimized CCII configuration has a current bandwidth of 1.103 GHz and a voltage bandwidth

Samir Ben Salem; Dorra Sellami Masmoudi; Ashwek Ben Saïd; Mourad Loulou

Translational-symmetric filters provide a foundation for various kinds of image processing. When a filtered image containing noise is observed, the original one can be reconstructed by Bayesian inference. Furthermore, hyperparameters such as the smoothness of the image and the noise level in the communication channel through which the image observed can be estimated from the observed image by setting a

The Kalman filter in question, which was implemented in the time scale algorithm TA(NIST), produces time scales with poor short-term stability. A simple modification of the error covariance matrix allows the filter to produce time scales with good stability at all averaging times, as verified by simulations of clock ensembles.

An operon is a fundamental unit of transcription and contains specific functional genes for the construction and regulation of networks at the entire genome level. The correct prediction of operons is vital for understanding gene regulations and functions in newly sequenced genomes. As experimental methods for operon detection tend to be nontrivial and time consuming, various methods for operon prediction have been proposed in the literature. In this study, a binary particle swarm optimization is used for operon prediction in bacterial genomes. The intergenic distance, participation in the same metabolic pathway, the cluster of orthologous groups, the gene length ratio and the operon length are used to design a fitness function. We trained the proper values on the Escherichia coli genome, and used the above five properties to implement feature selection. Finally, our study used the intergenic distance, metabolic pathway and the gene length ratio property to predict operons. Experimental results show that the prediction accuracy of this method reached 92.1%, 93.3% and 95.9% on the Bacillus subtilis genome, the Pseudomonas aeruginosa PA01 genome and the Staphylococcus aureus genome, respectively. This method has enabled us to predict operons with high accuracy for these three genomes, for which only limited data on the properties of the operon structure exists.

The particle swarm optimization (PSO) is a stochastic strategy that has recently found application to electromagnetic optimization problems. It is based on the behavior of insect swarms and exploits the solution space by taking into account the experience of the single particle as well as that of the entire swarm. This combined and synergic use of information yields a promising

This paper proposes an algorithm for improving adaptation and coefficients adjustment of active queue management by particle swarm optimization PID algorithm. In this algorithm, swarm particleoptimization is combined with PID algorithm, which can settle the coefficients adjustment online in PID and can adapt the variation of network traffic, so this algorithm can effectively fulfill active queue management. The simulation

Using particle swarms to track and optimize dynamic systems is described. Issues related to tracking and optimizing dynamic systems are briefly reviewed. Three kinds of dynamic systems are defined for the purposes of this paper. One of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function. Successful tracking of a 10-dimensional parabolic function with

This paper presents a novel particle swarm optimization based approach to optimally incorporate a distribution generator into a distribution system. The proposed algorithm combines particle swarm optimization with load flow algorithm to solve the problem in a single step, i.e. finding the best combination of location and size simultaneously. In the developed algorithm, the objective function to be minimized is

We address p-shift finite impulse response optimal (OFIR) and unbiased (UFIR) algorithms for predictive filtering ( p > 0), filtering ( p = 0), and smoothing filtering ( p < 0) at a discrete point n over N neighboring points. The algorithms were designed for linear time-invariant state-space signal models with white Gaussian noise. The OFIR filter self-determines the initial mean square state function by solving the discrete algebraic Riccati equation. The UFIR one represented both in the batch and iterative Kalman-like forms does not require the noise covariances and initial errors. An example of applications is given for smoothing and predictive filtering of a two-state polynomial model. Based upon this example, we show that exact optimality is redundant when N ? 1 and still a nice suboptimal estimate can fairly be provided with a UFIR filter at a much lower cost.

In this paper, we applied culture particle swarm optimization algorithm (CPSO) to optimize the parameters of SVM. Utilizing the colony aptitude of particle swarm and the ability of conserving the evolving knowledge of the culture algorithm, this CPSO algorithm constructed the population space based on particle swarm and the knowledge space. The two spaces evolved independently, at the same time,

The purpose of this study was to assess the indwelling time and retrievability of the Optease IVC filter. Between 2002 and 2009, a total of 811 Optease filters were inserted: 382 for prophylaxis in multitrauma patients and 429 for patients with venous thromboembolic (VTE) disease. In 139 patients [97 men and 42 women; mean age, 36 (range, 17-82) years], filter retrieval was attempted. They were divided into two groups to compare change in retrieval policy during the years: group A, 60 patients with filter retrievals performed before December 31 2006; and group B, 79 patients with filter retrievals from January 2007 to October 2009. A total of 128 filters were successfully removed (57 in group A, and 71 in group B). The mean filter indwelling time in the study group was 25 (range, 3-122) days. In group A the mean indwelling time was 18 (range, 7-55) days and in group B 31 days (range, 8-122). There were 11 retrieval failures: 4 for inability to engage the filter hook and 7 for inability to sheathe the filter due to intimal overgrowth. The mean indwelling time of group A retrieval failures was 16 (range, 15-18) days and in group B 54 (range, 17-122) days. Mean fluoroscopy time for successful retrieval was 3.5 (range, 1-16.6) min and for retrieval failures 25.2 (range, 7.2-62) min. Attempts to retrieve the Optease filter can be performed up to 60 days, but more failures will be encountered with this approach. PMID:20556385

Particle probability hypothesis density (PHD) filter-based visual trackers have achieved considerable success in the visual tracking field. But position measurements based on detection may not have enough ability to discriminate an object from clutter, and accurate state extraction cannot be obtained in the original PHD filtering framework, especially when targets can appear, disappear, merge, or split at any time. To

The degeneracy is the critical problem existed in particlefilter (PF). In order to solve this problem, we propose a new algorithm combined PF with unscented Kalman filter algorithm (UKF) and genetic simulated annealing algorithm (GASA) in this paper. In the new algorithm, UKF is used to generate the importance proposal distribution which can match the true posterior distribution more

Cost-reference particlefiltering (CRPF) is a methodology for recursive estimation of hidden states of dynamic systems. It is used for tracking nonlinear states when probabilistic assumptions about the state and observations noises are not made. Recently, we have proposed a CRPF algorithm for systems with conditionally linear states that combines the use of Kalman filtering for the linear states and

Passive localization from a single site is a typical nonlinear and non-Gaussian filtering and estimating problem, and usually suffers large initial estimation error and low observability. Considering the distribution of measurements is usually more peaked than the distribution of system state, an algorithm of transformed space sampling particlefilter (TSSPF) is proposed, in which a new transformed sample space is

Passive localization by a single observer, is a typical nonlinear and non-Gaussian filtering problem, and often suffers large initial estimation error, low observability and limited achievable measurements. Particlefilter provides a means to achieve the state estimation in a nonlinear and non-Gaussian system, however it may be very inefficient when applied to single observe passive localization and tracking (SOPLAT) application.

Yang Zheng-bin; Zhong Dan-xing; Guo Fu-cheng; Zhou Yi-yu

Diffuse reflectance spectrometry was used to investigate the reflectance characteristics of aerosol particles captured on bulk filters from Bermuda, Barbados, and Izaña. First derivatives of the spectra were examined for signals from two iron-oxide minerals, hematite and goethite, at 555 and 435nm, respectively, and the spectra and peaks were evaluated relative to the iron concentrations on the filters. The percent

We derive a new algorithm for particle flow with non-zero diffusion corresponding to Bayes' rule, and we report the results of Monte Carlo simulations which show that the new filter is an order of magnitude more accurate than the extended Kalman filter for a difficult nonlinear filter problem. Our new algorithm is simple and fast to compute, and it has an especially nice intuitive formula, which is the same as Newton's method to solve the maximum likelihood estimation (MLE) problem (but for each particle rather than only the MLE), and it is also the same as the extended Kalman filter for the special case of Gaussian densities (but for each particle rather than just the point estimate). All of these particle flows apply to arbitrary multimodal densities with smooth nowhere vanishing non- Gaussian densities.

This thesis considers possible solutions to sample impoverishment, a well-known failure mode of the Rao-Blackwellized particlefilter (RBPF) in simultaneous localization and mapping (SLAM) situations that arises when precise feature measurements yield a l...

The motion cueing algorithms are often applied in the motion simulators. In this paper, an optimal washout filter, taking into account the limitation of the simulator's workspace, is designed for the motion platform aiming to minimize human's perception error in order to provide realistic behavior. The filtering algorithm compares the human's perception of driving simulated vehicles realized by the motion

Edge detection and enhancement are widely used in image processing applications. In this paper we consider the problem of optimizing spatial frequency domain filters for detecting edges in digital pictures. The filter is optimum in that it produces maximum energy within a resolution interval of specified width in the vicinity of the edge. We show that, in the continuous case,

Fluorescence imaging of dynamical processes in live cells often results in a low signal-to-noise ratio. We present a novel feature-preserving non-local means approach to denoise such images to improve feature recovery and particle detection. The commonly used non-local means filter is not optimal for noisy biological images containing small features of interest because image noise prevents accurate determination of the correct coefficients for averaging, leading to over-smoothing and other artifacts. Our adaptive method addresses this problem by constructing a particle feature probability image, which is based on Haar-like feature extraction. The particle probability image is then used to improve the estimation of the correct coefficients for averaging. We show that this filter achieves higher peak signal-to-noise ratio in denoised images and has a greater capability in identifying weak particles when applied to synthetic data. We have applied this approach to live-cell images resulting in enhanced detection of end-binding-protein 1 foci on dynamically extending microtubules in photo-sensitive Drosophila tissues. We show that our feature-preserving non-local means filter can reduce the threshold of imaging conditions required to obtain meaningful data. PMID:20599512

Catfish particle swarm optimization (CatfishPSO) algorithm is a novel swarm intelligence optimization, which inspired by the behavior between sardines and catfish, i.e. the so-called catfish effect is applied to improve the performance of particle swarm optimization (PSO). In this paper, we propose an improved CatfishPSO with fuzzy adaptive (F-CatfishPSO), which a fuzzy system is implemented to dynamically adapt the inertia

Optimization problems with many objectives open new issues for multi-objective optimization algorithms and particularly Particle\\u000a Swarm Optimization. Many of the existing algorithms are able to solve problems of low number of objectives, but as soon as\\u000a we increase the number of objectives, their performances get even worse than random search methods. This paper gives an overview\\u000a on Multi-objective Particle Swarm

With the rapid developments in computer technology, the particlefilter (PF) is becoming more attractive in navigation applications. However, its large computational burden still limits its widespread use. One approach for reducing the computational burden without degrading the system estimation accuracy is to combine the PF with other filters, i.e., the extended Kalman filter (EKF) or the unscented Kalman filter (UKF). In this paper, the a posteriori estimates from an adaptive unscented Kalman filter (AUKF) are used to specify the PF importance density function for generating particles. Unlike the sequential importance sampling re-sampling (SISR) PF, the re-sampling step is not required in the algorithm, because the filter does not reuse the particles. Hence, the filter computational complexity can be reduced. Besides, the latest measurements are used to improve the proposal distribution for generating particles more intelligently. Simulations are conducted on the basis of a field-collected 3D UAV trajectory. GPS and IMU data are simulated under the assumption that a NovAtel DL-4plus GPS receiver and a Landmark™ 20 MEMS-based IMU are used. Navigation under benign and highly reflective signal environments are considered. Monte Carlo experiments are made. Numerical results show that the AUPF with 100 particles can present improved system estimation accuracy with an affordable computational burden when compared with the AEKF and AUKF algorithms.

To deal with the problem of diversity distribution of the solutions in Multi-objective Particle Swarm Optimization (MOPSO), a diversity pbest based multi-objective particle swarm optimization algorithm (dp-MOPSO) is proposed. In dp-MOPSO, an individual memory is allocated to each particle for saving the non-dominated pbest set which is found in the searching process, avoiding the loss of the information. An external

This paper is concerned with the filtering problem for both discrete-time stochastic linear (DTSL) systems and discrete-time stochastic nonlinear (DTSN) systems. In DTSL systems, an linear optimalfilter with multiple packet losses is designed based on the orthogonal principle analysis approach over unreliable wireless sensor networks (WSNs), and the experience result verifies feasibility and effectiveness of the proposed linear filter; in DTSN systems, an extended minimum variance filter with multiple packet losses is derived, and the filter is extended to the nonlinear case by the first order Taylor series approximation, which is successfully applied to unreliable WSNs. An application example is given and the corresponding simulation results show that, compared with extended Kalman filter (EKF), the proposed extended minimum variance filter is feasible and effective in WSNs.

PRT( Personal Rapid Transit ) system is a automated operation, so that it is important exactly finding position of vehicle. Many of PRT system has accepted the GPS system for a position, speed, and direction. in this paper, we propose a combination of Kalman Filter and H? Filter known as Hybrid Kalman/ H? Filter for applying to GPS navigation algorithm. For disturbance cancellation, Kalman Filter is optimal but it requires the statistical information about process and measurement noises while H? Filter only minimizes the "worst-case" error and requires that the noises are bounded. The new Hybrid Filter is expected to reduce the worst-case error and exploit the incomplete knowledge about noises to provide a better estimation. The experiment shows us the ability of Hybrid Filter in GPS navigation algorithm.

Kim, Hyunsoo; Nguyen, Hoang Hieu; Nguyen, Phi Long; Kim, Han Sil; Jang, Young Hwan; Ryu, Myungseon; Choi, Changho

A method for optimizing multipass laser amplifier output utilizes a spectral filter in early passes but not in later passes. The pulses shift position slightly for each pass through the amplifier, and the filter is placed such that early passes intersect the filter while later passes bypass it. The filter position may be adjust offline in order to adjust the number of passes in each category. The filter may be optimized for use in a cryogenic amplifier.

Backus, Sterling J. (Erie, CO) [Erie, CO; Kapteyn, Henry C. (Boulder, CO) [Boulder, CO

In the current study, abrasive wear resistance and mechanical properties of A356 composite reinforced with B4C particulates were investigated. A center particle swarm optimization algorithm (CenterPSO) is proposed to predict the optimal process conditions in fabrication of aluminum matrix composites. Unlike other ordinary particles, the center particle has no explicit velocity and is set to the center of the swarm at every iteration. Other aspects of the center particle are the same as that of the ordinary particle, such as fitness evaluation and competition for the best particle of the swarm. Because the center of the swarm is a promising position, the center particle generally gets good fitness value. More importantly, due to frequent appearance as the best particle of swarm, it often attracts other particles and guides the search direction of the whole swarm.

This paper presents a particle swarm optimization (PSO) as a tool for loss reduction study. This issue can be formulated as a nonlinear optimization problem. The proposed application consists of using a developed optimal power flow based on loss minimization function by expanding the original PSO. The study is carried out in two steps. First, by using the tangent vector

Ahmed A. A. Esmin; Germano Lambert-Torres; Antônio C. Zambroni de Souza

A concept for the optimization of nonlinear cost functionals, occurring in electrical engineering applications, using particle swarm optimization (PSO) is proposed. PSO is a stochastic optimization technique, whose stochastic behavior can be controlled very easily by one single factor. Additionally, this factor can be chosen to end up with a deterministic strategy, that does not need gradient information. The PSO

In the current application, a multiobjective optimization approach is presented for estimation of parameters of hydrologic models. The complexity of hydrologic processes demands efficient and effective tools to fully determine system characteristics. A relatively new optimization algorithm, known as particle swarm optimization (PSO) has been employed here for parameter estimation. The PSO algorithm comes from the family of evolutionary computation

An energy filter for charged particles includes a stack of micro-machined wafers including plural apertures passing through the stack of wafers, focusing electrodes bounding charged particle paths through the apertures, an entrance orifice to each of the plural apertures and an exit orifice from each of the plural apertures and apparatus for biasing the focusing electrodes with an electrostatic potential corresponding to an energy pass band of the filter.

Stalder, Roland E. (Inventor); Van Zandt, Thomas R. (Inventor); Hecht, Michael H. (Inventor); Grunthaner, Frank J. (Inventor)

An improved particle-filter algorithm is proposed to track a randomly moving object. The algorithm is implemented on a mobile robot equipped with a pan-tilt camera and 16 sonar sen- sors covering 360?. Initially, the moving object is detected through a sequence of images taken by the stationary pan-tilt camera us- ing the motion-detection algorithm. Then, the particle-filter-based tracking algorithm, which

hotmail.com Abstract - The INS\\/GPS navigation system is obvious nonlinear under the large initial condition errors.To tackle the accuracy of integrated navigation under nonlinear model, a improved particlefilter named cubature particlefilter (CPF) is applied to INS\\/GPS integrated navigation. For this, the nonlinear state model based on the platform misalignment angle and the observation model described by the velocity

Energy efficiency is a primary problem in wireless sensor networks which employ a large number of intelligent sensor nodes\\u000a to accomplish complicated tasks. Focused on the energy consumption problem in target tracking applications, this paper proposes\\u000a a dynamic energy management mechanism with an improved particlefilter prediction in wireless sensor networks. The standard\\u000a particlefilter is improved by combining the

This paper proposes a novel algorithm for delay-tolerant particlefiltering that is computationally efficient and has limited memory requirements. The algorithm estimates the informativeness of delayed (out-of-sequence) measurements (OOSMs) and immediately discards uninformative measurements. More informative measurements are then processed using the storage efficient particlefilter proposed by Orguner et al. If the measurement induces a dramatic change in the

For many dynamic estimation problems involving nonlinear and\\/or non-Gaussian models, particlefiltering offers improved performance at the expense of computational effort. This paper describes a scheme for efficiently tracking multiple targets using particlefilters. The tracking of the individual targets is made efficient through the use of Rao-Blackwellisation. The tracking of multiple targets is made practicable using Quasi-Monte Carlo integration.

Simon Maskell; Malcolm P. Rollason; Neil J. Gordon; David J. Salmond

For many dynamic estimation problems involving nonlinear and\\/or non-Gaussian models, particlefiltering offers improved performance at the expense of computational effort. This paper describes a scheme for efficiently tracking multiple targets using particlefilters. The tracking of the individual targets is made efficient through the use of Rao-Blackwellisation. The tracking of multiple targets is made practicable using Quasi-Monte Carlo integration.

Simon Maskell; Malcolm Rollason; Neil Gordon; David Salmond

A unifying algorithm has been developed to systematize the collection of compact Daubechies wavelets computable by spectral factorization of a symmetric positive polynomial. This collection comprises all classes of real and complex orthogonal and biorthogonal wavelet filters with maximal flatness for their minimal length. The main algorithm incorporates spectral factorization of the Daubechies product filter into analysis and synthesis filters. The spectral factors are found for search-optimized families by examining a desired criterion over combinatorial subsets of roots indexed by binary codes, and for constraint-selected families by imposing sufficient constraints on the roots without any optimizing search for an extremal property. Daubechies wavelet filter families have been systematized to include those constraint-selected by the principle of separably disjoint roots, and those search-optimized for time-domain regularity, frequency-domain selectivity, time-frequency uncertainty, and phase nonlinearity. The latter criterion permits construction of the least and most asymmetric and least and most symmetric real and complex orthogonal filters. Biorthogonal symmetric spline and balanced-length filters with linear phase are also computable by these methods. This systematized collection has been developed in the context of a general framework enabling evaluation of the equivalence of constraint-selected and search-optimized families with respect to the filter coefficients and roots and their characteristics. Some of the constraint-selected families have been demonstrated to be equivalent to some of the search-optimized families, thereby obviating the necessity for any search in their computation.

In this paper, optimization of diesel engine control parameters using a modified multi-objective particle swarm optimization (MOPSO) method is considered. This problem is formulized as a multi-objective optimization problem involving three optimization objectives: brake specific fuel consumption (BSFC), exhaust gas emission, and soot. A modified MOPSO is proposed with integration of particle swarm optimization (PSO) and a crossover approach. Several benchmark functions are tested, and results reveal that the modified MOPSO is more efficient than the typical MOPSO. Engine control parameter optimization with an extended PSO and the modified MOPSO is simulated, respectively. It proved the potential of the modified MOPSO for the engine control parameter optimization problem.

Wu, Dongmei; Ogawa, Masatoshi; Suzuki, Yasumasa; Ogai, Harutoshi; Kusaka, Jin

Particle transport and deposition in the Wilsonville hot gas filter vessel is studied. The filter vessel contains a total of 72 filters which are arranged in two tiers. These are modeled by six upper and one lower cylindrical effective filters. An unstructured grid of 296,781 cells generated by GAMBIT is used in the simulations. The Reynolds stress model of FLUENT (version 5.0) code is used for evaluating the gas mean velocity and root mean-square fluctuation velocities in the vessel. The particle equation of motion includes the drag, the gravitational and the lift forces. The turbulent instantaneous fluctuation velocity is simulated by a filtered Gaussian white-noise model provided by the FLUENT code. The particle deposition patterns are evaluated, and the effect of particle size is studied. In addition, the effect of turbulence dispersion, the lift force and the gravitional force are analyzed. The results show that the deposition pattern depends on particle size, and turbulence dispersion plays an important role in transport and deposition of particles in the vessel. The gravitational force affects the motion of larege particles, but has no effect on the transport and deposition of small particles.

A fast hybrid mode-matching (MM)\\/finite-element (FE) method is applied for the direct EM based optimization of advanced waffle-iron filters and coax-fed rectangular combline filters. The proposed technique, which combines the efficiency of the MM with the flexibility of the FE technique, achieves the direct EM based optimization of these components within typically an overnight run on a PC. The CAD

With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards' equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect.

Lü, Haishen; Yu, Zhongbo; Zhu, Yonghua; Drake, Sam; Hao, Zhenchun; Sudicky, Edward A.

\\u000a This paper proposes a novel population-based evolution algorithm named grouping-shuffling particle swarm optimization (GSPSO)\\u000a by hybridizing particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) for continuous optimization problems.\\u000a In the proposed algorithm, each particle automatically and periodically executes grouping and shuffling operations in its\\u000a flight learning evolutionary process. By testing on 4 benchmark functions, the numerical results demonstrate

Silver carp Hypophthalmichthys molitrix (SVC) and bighead carp H. nobilis (BHC) have impacted waters in the US since their escape. Current chemical controls for aquatic nuisance species are non-selective. Development of a bioactive micro-particle that exploits filter-feeding habits of SVC or BHC could result in a new control tool. It is not fully understood if SVC or BHC will consume bioactive micro-particles. Two discrete trials were performed to: 1) evaluate if SVC and BHC consume the candidate micro-particle formulation; 2) determine what size they consume; 3) establish methods to evaluate consumption of filter-feeders for future experiments. Both SVC and BHC were exposed to small (50-100 ?m) and large (150-200 ?m) micro-particles in two 24-h trials. Particles in water were counted electronically and manually (microscopy). Particles on gill rakers were counted manually and intestinal tracts inspected for the presence of micro-particles. In Trial 1, both manual and electronic count data confirmed reductions of both size particles; SVC appeared to remove more small particles than large; more BHC consumed particles; SVC had fewer overall particles in their gill rakers than BHC. In Trial 2, electronic counts confirmed reductions of both size particles; both SVC and BHC consumed particles, yet more SVC consumed micro-particles compared to BHC. Of the fish that ate micro-particles, SVC consumed more than BHC. It is recommended to use multiple metrics to assess consumption of candidate micro-particles by filter-feeders when attempting to distinguish differential particle consumption. This study has implications for developing micro-particles for species-specific delivery of bioactive controls to help fisheries, provides some methods for further experiments with bioactive micro-particles, and may also have applications in aquaculture.

Jensen, Nathan R.; Amberg, Jon J.; Luoma, James A.; Walleser, Liza R.; Gaikowski, Mark P.

Nuclepore filter collection with subsequent electron microscopy analysis for nanoparticles was carried out to examine the feasibility of the method to assess the nanoparticle exposure. The number distribution of nanoparticles collected on the filter surface was counted visually and converted to the distribution in the air using existing filtration models for Nuclepore filters. To search for a proper model, this paper studied the overall penetrations of three different nanoparticles (PSL, Ag and NaCl), covering a wide range of particle sizes (20–800 nm) and densities (1.05–10.5 g cm?3), through Nuclepore filters with two different pore diameters (1 and 3 ?m) and different face velocities (2–15 cm s?1). The data were compared with existing particle deposition models and modified models proposed by this study, which delivered different results because of different deposition processes considered. It was found that a parameter associated with flow condition and filter geometry (density of fluid medium, particle density, filtration face velocity, filter porosity and pore diameter) should be taken into account to verify the applicability of the models. The data of the overall penetration were in very good agreement with the properly applied models. A good agreement of filter surface collection between the validated model and the SEM analysis was obtained, indicating a correct nanoparticle number distribution in the air can be converted from the Nuclepore filter surface collection and this method can be applied for nanoparticle exposure assessment.

Chen, Sheng-Chieh; Wang, Jing; Fissan, Heinz; Pui, David Y. H.

Translational-symmetric filters provide a foundation for various kinds of image processing. When a filtered image containing noise is observed, the original one can be reconstructed by Bayesian inference. Furthermore, hyperparameters such as the smoothness of the image and the noise level in the communication channel through which the image observed can be estimated from the observed image by setting a criterion of maximizing marginalized likelihood. In this article we apply a diagonalization technique with the Fourier transform to this image reconstruction problem. This diagonalization not only reduces computational costs but also facilitates theoretical analyses of the estimation and reconstruction performances. We take as an example the Mexican-hat shaped neural cell receptive field seen in the early visual systems of animals, and we compare the reconstruction performances obtained under various hyperparameter and filter parameter conditions with each other and with the corresponding performances obtained under no-filter conditions. The results show that the using a Mexican-hat filter can reduce reconstruction error.

The increase in pressure drop across a HEPA filter has been measured as a function of the particle mass loading using two materials with different particle morphologies. The HEPA filter media chosen, is identical to the filter media used in the Airborne A...

A force balance model was developed to predict the effects of particle size, particle size distribution and surface potential on the structure of the filter cake. The model predicts that a stable filter cake is formed at low surface potentials and that the filter cake becomes unstable when the surface potential is larger than 30mV. The model predicts a minimum

GLUT4 is responsible for insulin-stimulated glucose uptake into fat cells and description of the dynamic behavior of it can give insight in some working mechanisms and structures of these cells. Quantitative analysis of the dynamical process requires tracking of hundreds of GLUT4 vesicles characterized as bright spots in noisy image sequences. In this paper, a 3D tracking algorithm built in Bayesian probabilistic framework is put forward, combined with the unique features of the TIRF microscopy. A brightness-correction procedure is firstly applied to ensure that the intensity of a vesicle is constant along time and is only affected by spatial factors. Then, tracking is formalized as a state estimation problem and a developed particlefilter integrated by a sub-optimizer that steers the particles towards a region with high likelihood is used. Once each tracked vesicle is located in image plane, the depth information of a granule can be indirectly inferred according to the exponential relationship between its intensity and its vertical position. The experimental results indicate that the vesicles are tracked well under different motion styles. More, the algorithm provides the depth information of the tracked vesicle.

This paper focuses on obtaining submerged position fixes for underwater vehicles from comparing bathymetric mea- surements with a bathymetric map. Our algorithms are tested on real data, collected by a HUGIN AUV equipped with a multibeam echo sounder (MBE). Due to our strongly non-linear and non-Gaussian problem, local linearization methods such as the extended Kalman filter (EKF), has proven unsuitable

When tracking a large number of targets, it is often computationally expensive to represent the full joint distribution over target states. In cases where the targets move independently, each target can instead be tracked with a separate filter. However, this leads to a model-data as- sociation problem. Another approach to solve the prob- lem with computational complexity is to track

Object tracking is one of the most important tasks in computer vision. The unscented particlefilter algorithm has been extensively used to tackle this problem and achieved a great success, because it uses the UKF (un- scented Kalman filter) to generate a sophisticated pro- posal distributions which incorporates the newest ob- servations into the state transition distribution and thus overcomes the

The problem of particle loading in particle-in-cell gyrokinetic simulations is addressed using a quadratic optimization algorithm. Optimized loading in configuration space dramatically reduces the short wavelength modes in the electrostatic potential that are partly responsible for the non-conservation of total energy; further, the long wavelength modes are resolved with good accuracy. As a result, the conservation of energy for the optimized loading is much better that the conservation of energy for the random loading. The method is valid for any geometry and can be coupled to optimization algorithms in velocity space.

Parallel particlefilters for evaluation of the likelihood of DSGE models are implemented and evaluated in a distributed memory message-passing context. In our paper special emphasis is put on the details of the interprocessor communication which is necessary for load balancing in the particle generation step. Parallelisation makes it possible to (i) reduce execution time, (ii) employ more accurate solution

States extraction from the particle probability hypotheses density (PHD) filter is a hotspot in multitarget multisenor tracking research. We find that some clustering algorithms are used to extract the states from the particles. Although the classical finite mixture model (FMM) clustering combined with expectation-maximum (EM) algorithm is better in comparison with other clustering algorithms, it is difficult to deal with

We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian Process Annealing ParticleFilter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker's stability and robustness. Comparing with a regular annealed particle fllter based tracker, we show that our algorithm can track

Patients at intensive care units need very many drugs applicated via a central venous katheter. Particles caused by incompatibility reactions or coming from disposible materials possibly can provoke severe complications such as embolism, anaphylactoid reactions or ARDS. The combined use of multilumen katheters and Intrapur filters brings a significant reduction of these particles, as shown by an infusion regime. PMID:2391172

The paper discusses tests of air filterparticle-size efficiency for diameters greater than 1 micrometer. valuation of air cleaner efficiencies in this size range can be quite demanding, depending on the required accuracy. uch particles have sufficient mass to require considerati...

Existing methods of improving particlefilters mainly focus on two aspects: designing a good proposal distribution before sampling and allocating particles to a high posterior area after sampling. An auxiliary particlefilter (APF) is one such simple algorithm belonging to the former aspect, which generates particles from an importance distribution depending on a more recent observation. Its weakness is that it requires a large number of particles. On the other hand, a kernel-based particlefilter (KPF), which belongs to the latter aspect, is able to greatly reduce the number of particles required and is still able to capture good characteristics of the posterior density. However, a KPF does not take the current observation into account. To utilize their respective strengths, a new algorithm is proposed in this paper with the combination of an APF and a KPF, the APF for designing good proposal density and the KPF for exploring the dominant mode of the posterior density. Experimental results in several real-tracking scenarios demonstrate that the integrated algorithm surpasses the standard particlefilter (SPF) when encountering weak dynamic models. Moreover, the proposed algorithm is also able to achieve a comparable performance with KPF whilst reducing computational cost.

We have proposed an effective method to synthesize and optimize multichannel fiber Bragg grating filters (MCFBGFs). The novel method contains two steps, i.e., the discrete layer peeling algorithm generates the excellent initial guess values and, successively, the nonlinear least squares method reconstructs and optimizes the expected fiber Bragg grating parameters from the initial guess in the previous step. Design examples

The problem of optimal measurement of a signal in presence of noise is treated in detail by Baldinger and Franzen (Adv. Electron. Electron Phys. 8 (1956) 225), Radeka and Karlovac (Nucl. Instr. and Meth. 52 (1967) 86) and Gatti and Manfredi (La Rivista Nuovo Cimento 9(1) (1986) 1), and the filter transfer function optimizing the signal over noise ratio is

We describe a general approach for the representation and recognition of 3D objects, as it applies to Automatic Target Recognition (ATR) tasks. The method is based on locally adaptive target segmentation, biologically motivated image processing and a novel view selection mechanism that develops 'visual filters' responsive to specific target classes to encode the complete viewing sphere with a small number

This study lies in that area of research that is concerned with the improvement of long-range detection techniques. By extending the well-known concept of a matched filter to include a receiver with several elements and by using a few simple examples, it ...

This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighbourhood best values, for solving multimodal optimization problems. In the proposed species- based PSO (SPSO), the swarm population is divided into species sub- populations based on their similarity. Each species is grouped around a dominating particle called the species seed. At each iteration step,

Reactive power dispatch in power systems is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. In this paper, a solution to the reactive power dispatch problem with a novel particle swarm optimization approach based on multiagent systems (MAPSO) is presented. This method integrates the multiagent system (MAS) and the particle swarm

This thesis solves the problem of finding the optimal linear noise-reduction filter for linear tomographic image reconstruction. The optimization is data dependent and results in minimizing the mean-square error of the reconstructed image. The error is defined as the difference between the result and the best possible reconstruction. Applications for the optimalfilter include reconstructions of positron emission tomographic (PET), X-ray computed tomographic, single-photon emission tomographic, and nuclear magnetic resonance imaging. Using high resolution PET as an example, the optimalfilter is derived and presented for the convolution backprojection, Moore-Penrose pseudoinverse, and the natural-pixel basis set reconstruction methods. Simulations and experimental results are presented for the convolution backprojection method.

Sun, W.Y. [Lawrence Berkeley Lab., CA (United States)]|[California Univ., Berkeley, CA (United States). Dept. of Electrical Engineering and Computer Sciences

Nasal efficiency for removing fine particles may be affected by developmental changes in nasal structure associated with age. In healthy Caucasian children (age 6-13, n=17) and adults (age 18-28, n=11) we measured the fractional deposition (DF) of fine particles (1 and 2um MMAD)...

Particlefiltering has recently been introduced to perform probabilistic tractography in conjunction with DTI and Q-Ball models to estimate the diffusion information. Particlefilters are particularly well adapted to the tractography problem as they offer a way to approximate a probability distribution over all paths originated from a specified voxel, given the diffusion information. In practice however, they often fail at consistently capturing the multi-modality of the target distribution. For brain white matter tractography, this means that multiple fiber pathways are unlikely to be tracked over extended volumes. We propose to remedy this issue by formulating the filtering distribution as an adaptive M-component non-parametric mixture model. Such a formulation preserves all the properties of a classical particlefilter while improving multi-modality capture. We apply this multi-modal particlefilter to both DTI and Q-Ball models and propose to estimate dynamically the number of modes of the filtering distribution. We show on synthetic and real data how this algorithm outperforms the previous versions proposed in the literature. PMID:24684002

Stamm, Aymeric; Commowick, Olivier; Barillot, Christian; Pérez, Patrick

The particle swarm optimizer (PSO) is a population-based optimization technique that can be applied to a wide range of problems. This paper presents a variation on the traditional PSO algorithm, called the efficient population utilization strategy for PSO (EPUS-PSO), adopting a population manager to significantly improve the efficiency of PSO. This is achieved by using variable particles in swarms to enhance the searching ability and drive particles more efficiently. Moreover, sharing principals are constructed to stop particles from falling into the local minimum and make the global optimal solution easier found by particles. Experiments were conducted on unimodal and multimodal test functions such as Quadric, Griewanks, Rastrigin, Ackley, and Weierstrass, with and without coordinate rotation. The results show good performance of the EPUS-PSO in solving most benchmark problems as compared to other recent variants of the PSO. PMID:19095550

This paper proposes a novel particle swarm optimization algorithm, rough particle swarm optimization algorithm (RPSOA), based\\u000a on the notion of rough patterns that use rough values defined with upper and lower intervals that represent a range or set\\u000a of values. In this paper, various operators and evaluation measures that can be used in RPSOA have been described and efficiently\\u000a utilized

Human-computer interaction for in-vehicle information and navigation systems is a challenging problem. In this paper, we propose a robust system for speaker localization and speech enhancement for an in-vehicle speech dialog system by using microphone arrays. The performance of traditional localization methods drastically decline in a moderately reverberant environment. In this paper, we introduce Gauss-Hermite filter to integrate the current

The implementation of a sub-optimal analog or hybrid Kalman filter is described stressing reliability, robustness, and speed requirements. The filter is designed assuming mismatched dynamics, noise, drift, and computational errors. The state estimation er...

A technique is introduced to select poly-phase codes and optimalfilters of a pulse compression system that have specific temporal and frequency characteristics. In the particular problem under study, multiple vehicles are assigned unique codes and receiver filters that have nearly orthogonal signatures. Narrowband users, that act as interference, are also present within the system. A code selection algorithm is used to select codes which have low autocorrelation sidelobes and low cross correlation peaks. Optimal mismatched filters are designed for these codes which minimize the peak values in the autocorrelation and the cross correlation functions. An adjustment to the filter design technique produces filters with nulls in their frequency response, in addition to having low correlation peaks. The method produces good codes and filters for a four-user system with length 34 four-phase codes. There is considerable improvement in cross and autocorrelation sidelobe levels over the matched filter case with only a slight decrease in the signal-to-noise ratio (SNR) of the system. The mismatched filter design also allows the design of frequency nulls at any frequency with arbitrary null attenuation, null width, and sidelobe level, at the cost of a slight decrease in processing gain.

Griep, Karl R.; Ritcey, James A.; Burlingame, John J.

An alternative to the well-established Fourier transform infrared (FT-IR) spectrometry, termed discrete frequency infrared (DFIR) spectrometry, has recently been proposed. This approach uses narrowband mid-infrared reflectance filters based on guided-mode resonance (GMR) in waveguide gratings, but filters designed and fabricated have not attained the spectral selectivity (? 32 cm?1) commonly employed for measurements of condensed matter using FT-IR spectroscopy. With the incorporation of dispersion and optical absorption of materials, we present here optimal design of double-layer surface-relief silicon nitride-based GMR filters in the mid-IR for various narrow bandwidths below 32 cm?1. Both shift of the filter resonance wavelengths arising from the dispersion effect and reduction of peak reflection efficiency and electric field enhancement due to the absorption effect show that the optical characteristics of materials must be taken into consideration rigorously for accurate design of narrowband GMR filters. By incorporating considerations for background reflections, the optimally designed GMR filters can have bandwidth narrower than the designed filter by the antireflection equivalence method based on the same index modulation magnitude, without sacrificing low sideband reflections near resonance. The reported work will enable use of GMR filters-based instrumentation for common measurements of condensed matter, including tissues and polymer samples.

Liu, Jui-Nung; Schulmerich, Matthew V.; Bhargava, Rohit; Cunningham, Brian T.

The purpose of this paper is to demonstrate the application of particle swarm optimization to a realistic multidisciplinary optimization test problem. The paper's new contributions to multidisciplinary optimization is the application of a new algorithm for dealing with the unique challenges associated with multidisciplinary optimization problems, and recommendations as to the utility of the algorithm in future multidisciplinary optimization applications. The selected example is a bi-level optimization problem that demonstrates severe numerical noise and has a combination of continuous and truly discrete design variables. The use of traditional gradient-based optimization algorithms is thus not practical. The numerical results presented indicate that the particle swarm optimization algorithm is able to reliably find the optimum design for the problem presented here. The algorithm is capable of dealing with the unique challenges posed by multidisciplinary optimization as well as the numerical noise and truly discrete variables present in the current example problem.

Sobieszczanski-Sobieski, Jaroslaw; Venter, Gerhard

In this paper, we propose a hybrid method that combines Gaussian process learning, a particlefilter, and annealing to track the 3D pose of a human subject in video sequences. Our approach, which we refer to as annealed Gaussian process guided particlefilter, comprises two steps. In the training step, we use a supervised learning method to train a Gaussian process regressor that takes the silhouette descriptor as an input and produces multiple output poses modeled by a mixture of Gaussian distributions. In the tracking step, the output pose distributions from the Gaussian process regression are combined with the annealed particlefilter to track the 3D pose in each frame of the video sequence. Our experiments show that the proposed method does not require initialization and does not lose tracking of the pose. We compare our approach with a standard annealed particlefilter using the HumanEva-I dataset and with other state of the art approaches using the HumanEva-II dataset. The evaluation results show that our approach can successfully track the 3D human pose over long video sequences and give more accurate pose tracking results than the annealed particlefilter. PMID:23846470

The particle swarm optimization (PSO) approach has been successfully applied in continuous problems in practice. However,\\u000a its application on the combinatorial search space is relatively new. The component assignment\\/sequencing problem in printed\\u000a circuit board (PCB) has been verified as NP-hard (non-deterministic polynomial time). This paper presents an adaptive particle\\u000a swarm optimization (APSO) approach to optimize the sequence of component placements

A multi-objective particle swarm optimization (MOPSO) approach is presented for generating Pareto-optimal solutions for reservoir operation problems. This method is developed by integrating Pareto dominance principles into particle swarm optimization (PSO) algorithm. In addition, a variable size external repository and an efficient elitist-mutation (EM) operator are introduced. The proposed EM-MOPSO approach is first tested for few test problems taken from

Traveling Salesman Problem (TSP) is a classical problem of optimization for researchers and its modeling is of great interest for Engineering, Operations Research and Computer Science. For solving TSP, many methods have been proposed, including heuristic ones. Our work extends the hybrid model, based on Particle Swarm Optimization, Genetic Algorithms and Fast Local Search, for the symmetric blind travelling salesman

Economic load dispatch, that optimizes the operation cost with respect to the load demands of customers, is one of the most important problems in power systems. A new hybrid particle swarm optimization (PSO) that incorporates a wavelet theory based mutation operation for solving economic load dispatch is proposed. It applies a wavelet theory to enhance PSO in exploring solution spaces

This paper presents an efficient and reliable particle swarm optimization (PSO) method for the economic load dispatch (ELD) problems. The PSO method was developed through the simulation of a simplified social system and has been found to be robust in solving continuous nonlinear optimization problems in terms of accuracy of the solution and computation time and it can out perform

M. Sudhakaran; P. Ajay-D-Vimal Raj; T. G. Palanivelu

Economic Load Dispatch (ELD) is one of an important optimization tasks which provides an economic condition for a power systems. In this paper, Particle Swarm Optimization (PSO) as an effective and reliable evolutionary based approach has been proposed to solve the constraint economic load dispatch problem. The proposed method is able to determine, the output power generation for all of

Tuning SVM hyperparameters is an important step for achieving good classification performance. In the binary case, the model selection issue is well studied. For multiclass problems, it is harder to choose appropriate values for the base binary models of a decomposition scheme. In this paper, the authors employ Particle Swarm Optimization to perform a multiclass model selection, which optimizes the

Bruno Feres de Souza; A. C. P. L. F. de Carvalho; R. Calvo; R. P. Ishii

In this paper, we present a Simple Distributed Particle Swarm Optimization (SDPSO) algorithm that can be used to track the optimal solution in a dynamic and noisy environment. The classic PSO algorithm lacks the ability to track changing optimum in a dyna...

This paper presents a method to employ particle swarms optim izers in a cooperative configuration. This is achieved by splitting the input vector into several sub-vectors, each w hich is optimized cooperatively in its own swarm. The applic ation of this technique to neural network training is investigate d, with promising results.

This paper presents a novel approach that determines the optimal location and size of capacitors on radial distribution systems to improve voltage profile and reduce the active power loss. Capacitor placement & sizing are done by loss sensitivity factors and particle swarm optimization respectively. The concept of loss sensitivity factors and can be considered as the new contribution in the

A study is presented on the application of particle swarm optimization (PSO) for estimation of parameters in chaotic systems. The parameter estimation is formulated as a nonlinear optimization problem using PSO to minimize the synchronization error for the observable states of the actual system and its mathematical model. The procedure is illustrated using a typical chaotic system of Lorenz equations.

Modeling of complex hydrologic processes has resulted in models that themselves exhibit a high degree of complexity and that require the determination of various parameters through calibration. In the current application we introduce a relatively new global optimization tool, called particle swarm optimization (PSO), that has already been applied in various other fields and has been reported to show effective

M. Kashif Gill; Yasir H. Kaheil; Abedalrazq Khalil; Mac McKee; Luis Bastidas

A new multiobjective particle swarm optimization (MOPSO) technique for environmental\\/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and

This paper proposes a new hybrid algorithm of combining the conventional Particle Swarm Optimization (PSO) algorithm with Differential Evolution (DE) strategy, named by the authors as Particle Swarm Differential Evolution Optimiza- tion (PSDEO) to enhance better balance between local and global search abilities, while solving the economic load dispatch (ELD) problems considering all practical complex constraints and higher order non-smooth

The measurement data obtained from the Coordinate Measuring Machines (CMMs) have to be further processed and analyzed to evaluate the form errors of manufactured components. Particle swarm optimization (PSO), which is based on a metaphor of social interaction, searches a space by adjusting the trajectories of individual vectors, called “particles” as they are conceptualized as moving points in multidimensional space.

Xiu-Lan Wen; Jia-Cai Huang; Dang-Hong Sheng; Feng-Lin Wang

A field-aged, passive diesel particulate filter (DPF) used in a school bus retrofit program was evaluated for emissions of particle mass and number concentration before, during, and after regeneration. For the particle mass measurements, filter samples were collected for gravimetric analysis with a partial flow sampling system, which sampled proportionally to the exhaust flow. A condensation particle counter and scanning

Teresa L. Barone; John Morse Storey; Norberto Domingo; Shannon Serre; Shawn Ryan; Emily Snyder; Abderrahmane Touati; Matthew Clayton; Tsung-Wen Chien; Hsin-Ta Hsueh; Hsin Chu; Wei-Chieh Hsu; Yueh-Yuan Tu; Hsien-Shiou Tsai; Kuo-Yi Chen; Richard Derwent; Michael Jenkin; Michael Pilling; William Carter; Ajith Kaduwela; Hanna Kierzkowska-Pawlak; Andrzej Chacuk; Andrzej Chmielewski; Anna Ostapczuk; Janusz Licki; Kenneth Casey; Richard Gates; Richard Shores; Eben Thoma; D. Harris; Tomasz Mroz; Ana Elías; Astrid Barona; Gorka Gallastegi; Naiara Rojo; Luis Gurtubay; Gabriel Ibarra-Berastegi; Amnon Bar-Ilan; Jeremiah Johnson; Allison DenBleyker; Lit-Mian Chan; Gregory Yarwood; David Hitchcock; Joseph Pinto; Katarzyna Piekarska; Andrey Zagoruiko; Bair Balzhinimaev; Sergey Vanag; Vladimir Goncharov; Sergey Lopatin; Alexander Zykov; Sergey Anichkov; Yurii Zhukov; Vassily Yankilevich; Nikolay Proskokov; Nick Hutson; Michal Glomba

In this paper, inspired by the analysis of convergence of PSO, we study the individual particle of a PSO system moving in a quantum multidimensional space and establish a quantum delta potential well model for PSO. After that, a trial method of parameter control and QDPSO is proposed. The experiment result shows much advantage of QDPSO to the traditional PSO.

This paper discusses ECG signal classification after parametrizing the ECG waveforms in the wavelet domain. Signal decomposition using perfect reconstruction quadrature mirror filter banks can provide a very parsimonious representation of ECG signals. In the current work, the filter parameters are adjusted by a numerical optimization algorithm in order to minimize a cost function associated to the filter cut-off sharpness. The goal consists of achieving a better compromise between frequency selectivity and time resolution at each decomposition level than standard orthogonal filter banks such as those of the Daubechies and Coiflet families. Our aim is to optimally decompose the signals in the wavelet domain so that they can be subsequently used as inputs for training to a neural network classifier.

Hadjiloucas, S.; Jannah, N.; Hwang, F.; Galvão, R. K. H.

Stochastic models have become the dominant means of approaching the problem of articulated 3D human body tracking, where approximate\\u000a inference is employed to tractably estimate the high-dimensional (?30D) posture space. Of these approximate inference techniques,\\u000a particlefiltering is the most commonly used approach. However filtering only takes into account past observations—almost\\u000a no body tracking research employs smoothing to improve the

This paper introduces data fusion strategies within particlefiltering in order to track people from a single camera mounted\\u000a on a mobile robot in a human environment. Various visual cues are described, relying on color, shape or motion, together with\\u000a several filtering strategies taking into account all or parts of these measurements in their importance and\\/or measurement\\u000a functions. A preliminary

Ludovic Brethes; Frédéric Lerasle; Patrick Danès; Mathias Fontmarty

This paper presents an investigation of a new method of purifying cryogenic liquid using sintered metallic wire-mesh filter,\\u000a which has the advantages of high purifying efficiency and preferred strength at absolutely low temperature. Experiments are\\u000a conducted to purify solid CO2 particles from liquid nitrogen. Temperature and pressure in the upstream and downstream of the filter, and the flow rate\\u000a of

Juan Li; Yu-mei Shi; Rong-shun Wang; Xiang-dong Li

Tapered element oscillating microbalances (TEOMs) are used in the UK Automatic Monitoring Network for the continuous measurement of ambient airborne particles. Used TEOM filters from Swansea, Cardiff and Pembroke were examined under high-resolution field emission scanning electron microscopy (FESEM). Clusters of calcium sulphate crystals, gypsum (CaSO4·2H2O) and anhydrite (CaSO4) were abundant on spring and summer filters, and not present on

T. P Jones; B. J Williamson; K. A BéruBé; R. J Richards

Abstract—This paper focuses on obtaining submerged,position fixes for underwater,vehicles from,comparing,bathymetric,mea- surements,with a bathymetric,map. Our algorithms,are tested on real data, collected by a HUGIN AUV equipped with a multibeam,echo sounder,(MBE). Due to our strongly non-linear and non-Gaussian problem, local linearization methods such as the extended Kalman filter (EKF), has proven unsuitable in many,terrain types. We therefore focus on two different recursive

In the present work we conduct a study of fiber filters produced by evaporation of silica particles upon a MM-fiber core. A band filter was designed and theoretically verified using a 2D Comsol simulation model of a 3D problem, and calculated in the frequency domain in respect to refractive index. The fiber filters were fabricated by stripping and chemically etching the middle part of an MM-fiber until the core was exposed. A mono layer of silica nano particles were evaporated on the core using an Evaporation Induced Self-Assembly (EISA) method. The experimental results indicated a broader bandwidth than indicated by the simulations which can be explained by the mismatch in the particle size distributions, uneven particle packing and finally by effects from multiple mode angles. Thus, there are several closely connected Bragg wavelengths that build up the broader bandwidth. The experimental part shows that it is possible by narrowing the particle size distributing and better control of the particle packing, the filter effectiveness can be greatly improved.

We present a wavelet transform implementation approach using a FIR filter bank that uses a Wallace Tree structure for fast multiplication. VHDL models targeted specifically for synthesize have been written for clocked data registers, adders and the multiplier. Symmetric wavelets like Biorthogonal wavelets can be implemented using this design. By changing the input filter coefficients different wavelet decompositions may be implemented. The design is mapped onto the ORCA series FPGA after synthesis and optimization for timing and area.

The concept of optimalfiltering of observations collected with a dual frequency GPS P-code receiver is investigated in comparison\\u000a to an approach for C\\/A-code units. The filter presented here uses only data gathered between one receiver and one satellite.\\u000a The estimated state vector consists of a one-way pseudorange, ionospheric influence, and ambiguity biases. Neither orbit information\\u000a nor station information is

This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on parti