WorldWideScience

Sample records for adaptive filtering system

  1. Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

    Institute of Scientific and Technical Information of China (English)

    杨勇; 缪玲娟

    2003-01-01

    A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

  2. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems.

    Science.gov (United States)

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

    2016-07-26

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

  3. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

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

  4. Adaptive digital filters

    CERN Document Server

    Kovačević, Branko; Milosavljević, Milan

    2013-01-01

    Adaptive Digital Filters” presents an important discipline applied to the domain of speech processing. The book first makes the reader acquainted with the basic terms of filtering and adaptive filtering, before introducing the field of advanced modern algorithms, some of which are contributed by the authors themselves. Working in the field of adaptive signal processing requires the use of complex mathematical tools. The book offers a detailed presentation of the mathematical models that is clear and consistent, an approach that allows everyone with a college level of mathematics knowledge to successfully follow the mathematical derivations and descriptions of algorithms.   The algorithms are presented in flow charts, which facilitates their practical implementation. The book presents many experimental results and treats the aspects of practical application of adaptive filtering in real systems, making it a valuable resource for both undergraduate and graduate students, and for all others interested in m...

  5. Modeling of memristor-based chaotic systems using nonlinear Wiener adaptive filters based on backslash operator

    International Nuclear Information System (INIS)

    Zhao, Yibo; Jiang, Yi; Feng, Jiuchao; Wu, Lifu

    2016-01-01

    Highlights: • A novel nonlinear Wiener adaptive filters based on the backslash operator are proposed. • The identification approach to the memristor-based chaotic systems using the proposed adaptive filters. • The weight update algorithm and convergence characteristics for the proposed adaptive filters are derived. - Abstract: Memristor-based chaotic systems have complex dynamical behaviors, which are characterized as nonlinear and hysteresis characteristics. Modeling and identification of their nonlinear model is an important premise for analyzing the dynamical behavior of the memristor-based chaotic systems. This paper presents a novel nonlinear Wiener adaptive filtering identification approach to the memristor-based chaotic systems. The linear part of Wiener model consists of the linear transversal adaptive filters, the nonlinear part consists of nonlinear adaptive filters based on the backslash operator for the hysteresis characteristics of the memristor. The weight update algorithms for the linear and nonlinear adaptive filters are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics. Comparing with the adaptive nonlinear polynomial filters, the proposed nonlinear adaptive filters have less identification error.

  6. Adaptive filtering prediction and control

    CERN Document Server

    Goodwin, Graham C

    2009-01-01

    Preface1. Introduction to Adaptive TechniquesPart 1. Deterministic Systems2. Models for Deterministic Dynamical Systems3. Parameter Estimation for Deterministic Systems4. Deterministic Adaptive Prediction5. Control of Linear Deterministic Systems6. Adaptive Control of Linear Deterministic SystemsPart 2. Stochastic Systems7. Optimal Filtering and Prediction8. Parameter Estimation for Stochastic Dynamic Systems9. Adaptive Filtering and Prediction10. Control of Stochastic Systems11. Adaptive Control of Stochastic SystemsAppendicesA. A Brief Review of Some Results from Systems TheoryB. A Summary o

  7. Adaptation of a Filter Assembly to Assess Microbial Bioburden of Pressurant Within a Propulsion System

    Science.gov (United States)

    Benardini, James N.; Koukol, Robert C.; Schubert, Wayne W.; Morales, Fabian; Klatte, Marlin F.

    2012-01-01

    A report describes an adaptation of a filter assembly to enable it to be used to filter out microorganisms from a propulsion system. The filter assembly has previously been used for particulates greater than 2 micrometers. Projects that utilize large volumes of nonmetallic materials of planetary protection concern pose a challenge to their bioburden budget, as a conservative specification value of 30 spores per cubic centimeter is typically used. Helium was collected utilizing an adapted filtration approach employing an existing Millipore filter assembly apparatus used by the propulsion team for particulate analysis. The filter holder on the assembly has a 47-mm diameter, and typically a 1.2-5 micrometer pore-size filter is used for particulate analysis making it compatible with commercially available sterilization filters (0.22 micrometers) that are necessary for biological sampling. This adaptation to an existing technology provides a proof-of-concept and a demonstration of successful use in a ground equipment system. This adaptation has demonstrated that the Millipore filter assembly can be utilized to filter out microorganisms from a propulsion system, whereas in previous uses the filter assembly was utilized for particulates greater than 2 micrometers.

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

    Science.gov (United States)

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

    2018-02-01

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

  9. IAE-adaptive Kalman filter for INS/GPS integrated navigation system

    Institute of Scientific and Technical Information of China (English)

    Bian Hongwei; Jin Zhihua; Tian Weifeng

    2006-01-01

    A marine INS/GPS adaptive navigation system is presented in this paper. GPS with two antenna providing vessel's altitude is selected as the auxiliary system fusing with INS to improve the performance of the hybrid system. The Kalman filter is the most frequently used algorithm in the integrated navigation system, which is capable of estimating INS errors online based on the measured errors between INS and GPS. The standard Kalman filter (SKF) assumes that the statistics of the noise on each sensor are given. As long as the noise distributions do not change, the Kalman filter will give the optimal estimation. However GPS receiver will be disturbed easily and thus temporally changing measurement noise will join into the outputs of GPS, which will lead to performance degradation of the Kalman filter. Many researchers introduce fuzzy logic control method into innovation-based adaptive estimation adaptive Kalman filtering (IAE-AKF) algorithm, and accordingly propose various adaptive Kalman filters. However how to design the fuzzy logic controller is a very complicated problem still without a convincing solution. A novel IAE-AKF is proposed herein, which is based on the maximum likelihood criterion for the proper computation of the filter innovation covariance and hence of the filter gain. The approach is direct and simple without having to establish fuzzy inference rules. After having deduced the proposed IAE-AKF algorithm theoretically in detail, the approach is tested by the simulation based on the system error model of the developed INS/GPS integrated marine navigation system. Simulation results show that the adaptive Kalman filter outperforms the SKF with higher accuracy, robustness and less computation. It is demonstrated that this proposed approach is a valid solution for the unknown changing measurement noise exited in the Kalman filter.

  10. Adaptive iterated function systems filter for images highly corrupted with fixed - Value impulse noise

    Science.gov (United States)

    Shanmugavadivu, P.; Eliahim Jeevaraj, P. S.

    2014-06-01

    The Adaptive Iterated Functions Systems (AIFS) Filter presented in this paper has an outstanding potential to attenuate the fixed-value impulse noise in images. This filter has two distinct phases namely noise detection and noise correction which uses Measure of Statistics and Iterated Function Systems (IFS) respectively. The performance of AIFS filter is assessed by three metrics namely, Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index Matrix (MSSIM) and Human Visual Perception (HVP). The quantitative measures PSNR and MSSIM endorse the merit of this filter in terms of degree of noise suppression and details/edge preservation respectively, in comparison with the high performing filters reported in the recent literature. The qualitative measure HVP confirms the noise suppression ability of the devised filter. This computationally simple noise filter broadly finds application wherein the images are highly degraded by fixed-value impulse noise.

  11. Adaptive projective filters

    International Nuclear Information System (INIS)

    Dikusar, N.D.

    1993-01-01

    The new approach to solving of the finding problem is proposed. The method is based on Discrete Projective Transformations (DPT), the List Square Fitting (LSF) and uses the information feedback in tracing for linear or quadratic track segments (TS). The fast and stable with respect to measurement errors and background points recurrent algorithm is suggested. The algorithm realizes the family of digital adaptive projective filters (APF) with known nonlinear weight functions-projective invariants. APF can be used in adequate control systems for collection, processing and compression of data, including tracking problems for the wide class of detectors. 10 refs.; 9 figs

  12. Identification of chaotic memristor systems based on piecewise adaptive Legendre filters

    International Nuclear Information System (INIS)

    Zhao, Yibo; Zhang, Xiuzai; Xu, Jin; Guo, Yecai

    2015-01-01

    Memristor is a nonlinear device, which plays an important role in the design and implementation of chaotic systems. In order to be able to understand in-depth the complex nonlinear dynamic behaviors in chaotic memristor systems, modeling or identification of its nonlinear model is very important premise. This paper presents a chaotic memristor system identification method based on piecewise adaptive Legendre filters. The threshold decomposition is carried out for the input vector, and also the input signal subintervals via decomposition satisfy the convergence condition of the adaptive Legendre filters. Then the adaptive Legendre filter structure and adaptive weight update algorithm are derived. Final computer simulation results show the effectiveness as well as fast convergence characteristics.

  13. Method and system for training dynamic nonlinear adaptive filters which have embedded memory

    Science.gov (United States)

    Rabinowitz, Matthew (Inventor)

    2002-01-01

    Described herein is a method and system for training nonlinear adaptive filters (or neural networks) which have embedded memory. Such memory can arise in a multi-layer finite impulse response (FIR) architecture, or an infinite impulse response (IIR) architecture. We focus on filter architectures with separate linear dynamic components and static nonlinear components. Such filters can be structured so as to restrict their degrees of computational freedom based on a priori knowledge about the dynamic operation to be emulated. The method is detailed for an FIR architecture which consists of linear FIR filters together with nonlinear generalized single layer subnets. For the IIR case, we extend the methodology to a general nonlinear architecture which uses feedback. For these dynamic architectures, we describe how one can apply optimization techniques which make updates closer to the Newton direction than those of a steepest descent method, such as backpropagation. We detail a novel adaptive modified Gauss-Newton optimization technique, which uses an adaptive learning rate to determine both the magnitude and direction of update steps. For a wide range of adaptive filtering applications, the new training algorithm converges faster and to a smaller value of cost than both steepest-descent methods such as backpropagation-through-time, and standard quasi-Newton methods. We apply the algorithm to modeling the inverse of a nonlinear dynamic tracking system 5, as well as a nonlinear amplifier 6.

  14. Command Filtered Adaptive Fuzzy Neural Network Backstepping Control for Marine Power System

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

    Full Text Available In order to retrain chaotic oscillation of marine power system which is excited by periodic electromagnetism perturbation, a novel command-filtered adaptive fuzzy neural network backstepping control method is designed. First, the mathematical model of marine power system is established based on the two parallel nonlinear model. Then, main results of command-filtered adaptive fuzzy neural network backstepping control law are given. And the Lyapunov stability theory is applied to prove that the system can remain closed-loop asymptotically stable with this controller. Finally, simulation results indicate that the designed controller can suppress chaotic oscillation with fast convergence speed that makes the system return to the equilibrium point quickly; meanwhile, the parameter which induces chaotic oscillation can also be discriminated.

  15. Speed Estimation of Induction Motor Using Model Reference Adaptive System with Kalman Filter

    Directory of Open Access Journals (Sweden)

    Pavel Brandstetter

    2013-01-01

    Full Text Available The paper deals with a speed estimation of the induction motor using observer with Model Reference Adaptive System and Kalman Filter. For simulation, Hardware in Loop Simulation method is used. The first part of the paper includes the mathematical description of the observer for the speed estimation of the induction motor. The second part describes Kalman filter. The third part describes Hardware in Loop Simulation method and its realization using multifunction card MF 624. In the last section of the paper, simulation results are shown for different changes of the induction motor speed which confirm high dynamic properties of the induction motor drive with sensorless control.

  16. A DSP-Based Beam Current Monitoring System for Machine Protection Using Adaptive Filtering

    International Nuclear Information System (INIS)

    J. Musson; H. Dong; R. Flood; C. Hovater; J. Hereford

    2001-01-01

    The CEBAF accelerator at Jefferson Lab is currently using an analog beam current monitoring (BCM) system for its machine protection system (MPS), which has a loss accuracy of 2 micro-amps. Recent burn-through simulations predict catastrophic beam line component failures below 1 micro-amp of loss, resulting in a blind spot for the MPS. Revised MPS requirements target an ultimate beam loss accuracy of 250 nA. A new beam current monitoring system has been developed which utilizes modern digital receiver technology and digital signal processing concepts. The receiver employs a direct-digital down converter integrated circuit, mated with a Jefferson Lab digital signal processor VME card. Adaptive filtering is used to take advantage of current-dependent burn-through rates. Benefits of such a system include elimination of DC offsets, generic algorithm development, extensive filter options, and interfaces to UNIX-based control systems

  17. Filter systems

    International Nuclear Information System (INIS)

    Vanin, V.R.

    1990-01-01

    The multidetector systems for high resolution gamma spectroscopy are presented. The observable parameters for identifying nuclides produced simultaneously in the reaction are analysed discussing the efficiency of filter systems. (M.C.K.)

  18. An adaptive three-stage extended Kalman filter for nonlinear discrete-time system in presence of unknown inputs.

    Science.gov (United States)

    Xiao, Mengli; Zhang, Yongbo; Wang, Zhihua; Fu, Huimin

    2018-04-01

    Considering the performances of conventional Kalman filter may seriously degrade when it suffers stochastic faults and unknown input, which is very common in engineering problems, a new type of adaptive three-stage extended Kalman filter (AThSEKF) is proposed to solve state and fault estimation in nonlinear discrete-time system under these conditions. The three-stage UV transformation and adaptive forgetting factor are introduced for derivation, and by comparing with the adaptive augmented state extended Kalman filter, it is proven to be uniformly asymptotically stable. Furthermore, the adaptive three-stage extended Kalman filter is applied to a two-dimensional radar tracking scenario to illustrate the effect, and the performance is compared with that of conventional three stage extended Kalman filter (ThSEKF) and the adaptive two-stage extended Kalman filter (ATEKF). The results show that the adaptive three-stage extended Kalman filter is more effective than these two filters when facing the nonlinear discrete-time systems with information of unknown inputs not perfectly known. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

  19. Adaptive filtering and change detection

    CERN Document Server

    Gustafsson, Fredrik

    2003-01-01

    Adaptive filtering is a classical branch of digital signal processing (DSP). Industrial interest in adaptive filtering grows continuously with the increase in computer performance that allows ever more conplex algorithms to be run in real-time. Change detection is a type of adaptive filtering for non-stationary signals and is also the basic tool in fault detection and diagnosis. Often considered as separate subjects Adaptive Filtering and Change Detection bridges a gap in the literature with a unified treatment of these areas, emphasizing that change detection is a natural extensi

  20. Design of Power Cable UAV Intelligent Patrol System Based on Adaptive Kalman Filter Fuzzy PID Control

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

    Full Text Available Patrol UAV has poor aerial posture stability and is largely affected by anthropic factors, which lead to some shortages such as low power cable tracking precision, captured image loss and inconvenient temperature measurement, etc. In order to solve these disadvantages, this article puts forward a power cable intelligent patrol system. The core innovation of the system is a 360° platform. This collects the position information of power cables by using far infrared sensors and carries out real-time all-direction adjustment of UAV lifting platform through the adaptive Kalman filter fuzzy PID control algorithm, so that the precise tracking of power cables is achieved. An intelligent patrol system is established to detect the faults more accurately, so that a high intelligence degree of power cable patrol system is realized.

  1. Subband Adaptive Filtering with l1-Norm Constraint for Sparse System Identification

    Directory of Open Access Journals (Sweden)

    Young-Seok Choi

    2013-01-01

    Full Text Available This paper presents a new approach of the normalized subband adaptive filter (NSAF which directly exploits the sparsity condition of an underlying system for sparse system identification. The proposed NSAF integrates a weighted l1-norm constraint into the cost function of the NSAF algorithm. To get the optimum solution of the weighted l1-norm regularized cost function, a subgradient calculus is employed, resulting in a stochastic gradient based update recursion of the weighted l1-norm regularized NSAF. The choice of distinct weighted l1-norm regularization leads to two versions of the l1-norm regularized NSAF. Numerical results clearly indicate the superior convergence of the l1-norm regularized NSAFs over the classical NSAF especially when identifying a sparse system.

  2. Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

    Directory of Open Access Journals (Sweden)

    Doo Yong Choi

    2016-04-01

    Full Text Available Rapid detection of bursts and leaks in water distribution systems (WDSs can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA systems and the establishment of district meter areas (DMAs. Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.

  3. AN ADAPTIVE OPTIMAL KALMAN FILTER FOR STOCHASTIC VIBRATION CONTROL SYSTEM WITH UNKNOWN NOISE VARIANCES

    Institute of Scientific and Technical Information of China (English)

    Li Shu; Zhuo Jiashou; Ren Qingwen

    2000-01-01

    In this paper, an optimal criterion is presented for adaptive Kalman filter in a control sys tem with unknown variances of stochastic vibration by constructing a function of noise variances and minimizing the function. We solve the model and measure variances by using DFP optimal method to guarantee the results of Kalman filter to be optimized. Finally, the control of vibration can be implemented by LQG method.

  4. Implicit LES using adaptive filtering

    Science.gov (United States)

    Sun, Guangrui; Domaradzki, Julian A.

    2018-04-01

    In implicit large eddy simulations (ILES) numerical dissipation prevents buildup of small scale energy in a manner similar to the explicit subgrid scale (SGS) models. If spectral methods are used the numerical dissipation is negligible but it can be introduced by applying a low-pass filter in the physical space, resulting in an effective ILES. In the present work we provide a comprehensive analysis of the numerical dissipation produced by different filtering operations in a turbulent channel flow simulated using a non-dissipative, pseudo-spectral Navier-Stokes solver. The amount of numerical dissipation imparted by filtering can be easily adjusted by changing how often a filter is applied. We show that when the additional numerical dissipation is close to the subgrid-scale (SGS) dissipation of an explicit LES the overall accuracy of ILES is also comparable, indicating that periodic filtering can replace explicit SGS models. A new method is proposed, which does not require any prior knowledge of a flow, to determine the filtering period adaptively. Once an optimal filtering period is found, the accuracy of ILES is significantly improved at low implementation complexity and computational cost. The method is general, performing well for different Reynolds numbers, grid resolutions, and filter shapes.

  5. Adaptive Control of Non-Minimum Phase Modal Systems Using Residual Mode Filters2. Parts 1 and 2

    Science.gov (United States)

    Balas, Mark J.; Frost, Susan

    2011-01-01

    Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will modify the adaptive controller with a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. This paper will be divided into two parts. Here in Part I we will review the basic adaptive control approach and introduce the primary ideas. In Part II, we will present the RMF methodology and complete the proofs of all our results. Also, we will apply the above theoretical results to a simple flexible structure example to illustrate the behavior with and without the residual mode filter.

  6. High performance 3D adaptive filtering for DSP based portable medical imaging systems

    Science.gov (United States)

    Bockenbach, Olivier; Ali, Murtaza; Wainwright, Ian; Nadeski, Mark

    2015-03-01

    Portable medical imaging devices have proven valuable for emergency medical services both in the field and hospital environments and are becoming more prevalent in clinical settings where the use of larger imaging machines is impractical. Despite their constraints on power, size and cost, portable imaging devices must still deliver high quality images. 3D adaptive filtering is one of the most advanced techniques aimed at noise reduction and feature enhancement, but is computationally very demanding and hence often cannot be run with sufficient performance on a portable platform. In recent years, advanced multicore digital signal processors (DSP) have been developed that attain high processing performance while maintaining low levels of power dissipation. These processors enable the implementation of complex algorithms on a portable platform. In this study, the performance of a 3D adaptive filtering algorithm on a DSP is investigated. The performance is assessed by filtering a volume of size 512x256x128 voxels sampled at a pace of 10 MVoxels/sec with an Ultrasound 3D probe. Relative performance and power is addressed between a reference PC (Quad Core CPU) and a TMS320C6678 DSP from Texas Instruments.

  7. Smart wave filtering method of a rectangular panel using Hilbert transformers and its application to an adaptive control system

    International Nuclear Information System (INIS)

    Iwamoto, Hiroyuki; Tanaka, Nobuo; Hill, Simon G

    2010-01-01

    This paper concerns the active vibration control of a rectangular panel using smart sensors from the viewpoint of an active wave control theory. The objective of this paper is to present a new type of filter which enables the measurement of the wave amplitude of a rectangular panel in real time for the application of an adaptive feedforward control system which inactivates vibration modes. Firstly, a novel wave filtering method using smart PVDF sensors is proposed. It is found that the shaping function of smart sensors is a complex function. To realize the smart sensor in a practical situation, a Hilbert transformer is utilized to implement a phase shifter of 90° for broadband frequencies. Then, from the viewpoint of a numerical analysis, the characteristics of the proposed wave filter and the performance of the adaptive feedforward control system using the wave filter are discussed. Finally, experiments implementing the active wave control theory which uses the proposed wave filter are conducted, demonstrating the validity of the proposed method in suppressing the vibration of a rectangular panel

  8. Partial update least-square adaptive filtering

    CERN Document Server

    Xie, Bei

    2014-01-01

    Adaptive filters play an important role in the fields related to digital signal processing and communication, such as system identification, noise cancellation, channel equalization, and beamforming. In practical applications, the computational complexity of an adaptive filter is an important consideration. The Least Mean Square (LMS) algorithm is widely used because of its low computational complexity (O(N)) and simplicity in implementation. The least squares algorithms, such as Recursive Least Squares (RLS), Conjugate Gradient (CG), and Euclidean Direction Search (EDS), can converge faster a

  9. Development of Tremor Suppression Control System Using Adaptive Filter and Its Application to Meal-assist Robot

    Science.gov (United States)

    Yano, Ken'ichi; Ohara, Eiichi; Horihata, Satoshi; Aoki, Takaaki; Nishimoto, Yutaka

    A robot that supports independent living by assisting with eating and other activities which use the operator's own hand would be helpful for people suffering from tremors of the hand or any other body part. The proposed system using adaptive filter estimates tremor frequencies with a time-varying property and individual differences online. In this study, the estimated frequency is used to adjusting the tremor suppression filter which insulates the voluntary motion signal from the sensor signal containing tremor components. These system are integrated into the control system of the Meal-Assist Robot. As a result, the developed system makes it possible for the person with a tremor to manipulate the supporting robot without causing operability to deteriorate and without hazards due to improper operation.

  10. An embedded implementation based on adaptive filter bank for brain-computer interface systems.

    Science.gov (United States)

    Belwafi, Kais; Romain, Olivier; Gannouni, Sofien; Ghaffari, Fakhreddine; Djemal, Ridha; Ouni, Bouraoui

    2018-07-15

    Brain-computer interface (BCI) is a new communication pathway for users with neurological deficiencies. The implementation of a BCI system requires complex electroencephalography (EEG) signal processing including filtering, feature extraction and classification algorithms. Most of current BCI systems are implemented on personal computers. Therefore, there is a great interest in implementing BCI on embedded platforms to meet system specifications in terms of time response, cost effectiveness, power consumption, and accuracy. This article presents an embedded-BCI (EBCI) system based on a Stratix-IV field programmable gate array. The proposed system relays on the weighted overlap-add (WOLA) algorithm to perform dynamic filtering of EEG-signals by analyzing the event-related desynchronization/synchronization (ERD/ERS). The EEG-signals are classified, using the linear discriminant analysis algorithm, based on their spatial features. The proposed system performs fast classification within a time delay of 0.430 s/trial, achieving an average accuracy of 76.80% according to an offline approach and 80.25% using our own recording. The estimated power consumption of the prototype is approximately 0.7 W. Results show that the proposed EBCI system reduces the overall classification error rate for the three datasets of the BCI-competition by 5% compared to other similar implementations. Moreover, experiment shows that the proposed system maintains a high accuracy rate with a short processing time, a low power consumption, and a low cost. Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity. This approach allows to develop a complete prototype of a EBCI system that achieves excellent accuracy rates. Copyright © 2018 Elsevier B.V. All rights reserved.

  11. Adaptive filtering primer with Matlab

    CERN Document Server

    Poularikas, Alexander D

    2006-01-01

    INTRODUCTIONSignal ProcessingAn ExampleOutline of the TextDISCRETE-TIME SIGNAL PROCESSINGDiscrete Time SignalsTransform-Domain Representation of Discrete-Time SignalsThe Z-TransformDiscrete-Time SystemsProblemsHints-Solutions-SuggestionsRANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSESRandom Signals and DistributionsAveragesStationary ProcessesSpecial Random Signals and Probability Density FunctionsWiener-Khinchin RelationsFiltering Random ProcessesSpecial Types of Random ProcessesNonparametric Spectra EstimationParametric Methods of power Spectral EstimationProblemsHints-Solutions-SuggestionsWIENER FILTERSThe Mean-Square ErrorThe FIR Wiener FilterThe Wiener SolutionWiener Filtering ExamplesProblemsHints-Solutions-SuggestionsEIGENVALUES OF RX - PROPERTIES OF THE ERROR SURFACEThe Eigenvalues of the Correlation MatrixGeometrical Properties of the Error SurfaceProblemsHints-Solutions-SuggestionsNEWTON AND STEEPEST-DESCENT METHODOne-Dimensional Gradient Search MethodSteepest-Descent AlgorithmProblemsHints-Sol...

  12. Adaptive Filtering Using Recurrent Neural Networks

    Science.gov (United States)

    Parlos, Alexander G.; Menon, Sunil K.; Atiya, Amir F.

    2005-01-01

    A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

  13. Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

    Directory of Open Access Journals (Sweden)

    Baofeng Wang

    Full Text Available Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

  14. Multi-vehicle detection with identity awareness using cascade Adaboost and Adaptive Kalman filter for driver assistant system.

    Science.gov (United States)

    Wang, Baofeng; Qi, Zhiquan; Chen, Sizhong; Liu, Zhaodu; Ma, Guocheng

    2017-01-01

    Vision-based vehicle detection is an important issue for advanced driver assistance systems. In this paper, we presented an improved multi-vehicle detection and tracking method using cascade Adaboost and Adaptive Kalman filter(AKF) with target identity awareness. A cascade Adaboost classifier using Haar-like features was built for vehicle detection, followed by a more comprehensive verification process which could refine the vehicle hypothesis in terms of both location and dimension. In vehicle tracking, each vehicle was tracked with independent identity by an Adaptive Kalman filter in collaboration with a data association approach. The AKF adaptively adjusted the measurement and process noise covariance through on-line stochastic modelling to compensate the dynamics changes. The data association correctly assigned different detections with tracks using global nearest neighbour(GNN) algorithm while considering the local validation. During tracking, a temporal context based track management was proposed to decide whether to initiate, maintain or terminate the tracks of different objects, thus suppressing the sparse false alarms and compensating the temporary detection failures. Finally, the proposed method was tested on various challenging real roads, and the experimental results showed that the vehicle detection performance was greatly improved with higher accuracy and robustness.

  15. Awareness, training and trust in interaction with adaptive spam filters

    NARCIS (Netherlands)

    Cramer, H.S.M.; Evers, V.; van Someren, M.W.; Wielinga, B.J.; Greenberg, S.; Hudson, S.E.; Hinckley, K.; Morris, M.R.; Olsen Jr., D.R.

    2009-01-01

    Even though adaptive (trainable) spam filters are a common example of systems that make (semi-)autonomous decisions on behalf of the user, trust in these filters has been underexplored. This paper reports a study of usage of spam filters in the daily workplace and user behaviour in training these

  16. Adaptive Filtering Algorithms and Practical Implementation

    CERN Document Server

    Diniz, Paulo S R

    2013-01-01

    In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation. The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes: Analytical and simulation examples in Chapters 4, 5, 6 and 10 Appendix E, which summarizes the analysis of set-membership algorithm Updated problems and references Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Several problems are...

  17. Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

    Directory of Open Access Journals (Sweden)

    Xin Li

    2016-02-01

    Full Text Available Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning, which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF.

  18. Scheme of adaptive polarization filtering based on Kalman model

    Institute of Scientific and Technical Information of China (English)

    Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande

    2006-01-01

    A new kind of adaptive polarization filtering algorithm in order to suppress the angle cheating interference for the active guidance radar is presented. The polarization characteristic of the interference is dynamically tracked by using Kalman estimator under variable environments with time. The polarization filter parameters are designed according to the polarization characteristic of the interference, and the polarization filtering is finished in the target cell. The system scheme of adaptive polarization filter is studied and the tracking performance of polarization filter and improvement of angle measurement precision are simulated. The research results demonstrate this technology can effectively suppress the angle cheating interference in guidance radar and is feasible in engineering.

  19. Musical noise reduction using an adaptive filter

    Science.gov (United States)

    Hanada, Takeshi; Murakami, Takahiro; Ishida, Yoshihisa; Hoya, Tetsuya

    2003-10-01

    This paper presents a method for reducing a particular noise (musical noise). The musical noise is artificially produced by Spectral Subtraction (SS), which is one of the most conventional methods for speech enhancement. The musical noise is the tin-like sound and annoying in human auditory. We know that the duration of the musical noise is considerably short in comparison with that of speech, and that the frequency components of the musical noise are random and isolated. In the ordinary SS-based methods, the musical noise is removed by the post-processing. However, the output of the ordinary post-processing is delayed since the post-processing uses the succeeding frames. In order to improve this problem, we propose a novel method using an adaptive filter. In the proposed system, the observed noisy signal is used as the input signal to the adaptive filter and the output of SS is used as the reference signal. In this paper we exploit the normalized LMS (Least Mean Square) algorithm for the adaptive filter. Simulation results show that the proposed method has improved the intelligibility of the enhanced speech in comparison with the conventional method.

  20. Bridge Performance Assessment Based on an Adaptive Neuro-Fuzzy Inference System with Wavelet Filter for the GPS Measurements

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-10-01

    Full Text Available This study describes the performance assessment of the Huangpu Bridge in Guangzhou, China based on long-term monitoring in real-time by the kinematic global positioning system (RTK-GPS technique. Wavelet transformde-noising is applied to filter the GPS measurements, while the adaptive neuro-fuzzy inference system (ANFIS time series output-only model is used to predict the deformations of GPS-bridge monitoring points. In addition, GPS and accelerometer monitoring systems are used to evaluate the bridge oscillation performance. The conclusions drawn from investigating the numerical results show that: (1the wavelet de-noising of the GPS measurements of the different recording points on the bridge is a suitable tool to efficiently eliminate the signal noise and extract the different deformation components such as: semi-static and dynamic displacements; (2 the ANFIS method with two multi-input single output model is revealed to powerfully predict GPS movement measurements and assess the bridge deformations; and (3 The installed structural health monitoring system and the applied ANFIS movement prediction performance model are solely sufficient to assure bridge safety based on the analyses of the different filtered movement components.

  1. An adaptive compensation algorithm for temperature drift of micro-electro-mechanical systems gyroscopes using a strong tracking Kalman filter.

    Science.gov (United States)

    Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

    2015-05-13

    We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS) gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF), the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to -2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

  2. An Adaptive Compensation Algorithm for Temperature Drift of Micro-Electro-Mechanical Systems Gyroscopes Using a Strong Tracking Kalman Filter

    Directory of Open Access Journals (Sweden)

    Yibo Feng

    2015-05-01

    Full Text Available We present an adaptive algorithm for a system integrated with micro-electro-mechanical systems (MEMS gyroscopes and a compass to eliminate the influence from the environment, compensate the temperature drift precisely, and improve the accuracy of the MEMS gyroscope. We use a simplified drift model and changing but appropriate model parameters to implement this algorithm. The model of MEMS gyroscope temperature drift is constructed mostly on the basis of the temperature sensitivity of the gyroscope. As the state variables of a strong tracking Kalman filter (STKF, the parameters of the temperature drift model can be calculated to adapt to the environment under the support of the compass. These parameters change intelligently with the environment to maintain the precision of the MEMS gyroscope in the changing temperature. The heading error is less than 0.6° in the static temperature experiment, and also is kept in the range from 5° to −2° in the dynamic outdoor experiment. This demonstrates that the proposed algorithm exhibits strong adaptability to a changing temperature, and performs significantly better than KF and MLR to compensate the temperature drift of a gyroscope and eliminate the influence of temperature variation.

  3. Applications of adaptive filters in active noise control

    Science.gov (United States)

    Darlington, Paul

    The active reduction of acoustic noise is achieved by the addition of a cancelling acoustic signal to the unwanted sound. Successful definition of the cancelling signal amounts to a system identification problem. Recent advances in adaptive signal processing have allowed this problem to be tackled using adaptive filters, which offer significant advantages over conventional solutions. The extension of adaptive noise cancelling techniques, which were developed in the electrical signal conditioning context, to the control of acoustic systems is studied. An analysis is presented of the behavior of the Widrow-Hoff LMS adaptive noise canceller with a linear filter in its control loop. The active control of plane waves propagating axially in a hardwalled duct is used as a motivating model problem. The model problem also motivates the study of the effects of feedback around an LMS adaptive filter. An alternative stochastic gradient algorithm for controlling adaptive filters in the presence of feedback is presented.

  4. Adaptable Iterative and Recursive Kalman Filter Schemes

    Science.gov (United States)

    Zanetti, Renato

    2014-01-01

    Nonlinear filters are often very computationally expensive and usually not suitable for real-time applications. Real-time navigation algorithms are typically based on linear estimators, such as the extended Kalman filter (EKF) and, to a much lesser extent, the unscented Kalman filter. The Iterated Kalman filter (IKF) and the Recursive Update Filter (RUF) are two algorithms that reduce the consequences of the linearization assumption of the EKF by performing N updates for each new measurement, where N is the number of recursions, a tuning parameter. This paper introduces an adaptable RUF algorithm to calculate N on the go, a similar technique can be used for the IKF as well.

  5. Adaptivni digitalni filtri / Adaptive digital filters

    Directory of Open Access Journals (Sweden)

    Dragan Petković

    2002-01-01

    Full Text Available Rad opisuje osnove funkcionisanja adaptivnih filtara. U uvodnim razmatranjima obra-đene su osnove matematičke obrade diskretnih signala i z-transformacije kod adaptivnih filtara. Izložen je Wienerov problem filtracije. Predstavljeni su CCL petlja i Widrow-Hoffov LMS algoritam i razmotrena brzina konvergencije adaptivnih filtara. Praktično je realizova-na CCL petlja sa osvrtom na brzinu konvergencije. / The paper describes the basis of adaptive filter functioning. The first considerations deal with the mathematical processing of discrete signals and the Z-transform in adaptive filters. The Wieners filter processing problem was exposed. The Correlation Canceler Loop (CCL was presented as well as the Widrow-Hoffs adaptive Least Mean Squares (LMS step-by-step procedure. The convergence rate of adaptive filters was considered as well. The CCL simulations were obtained pointing out the convergence rate.

  6. Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm

    Directory of Open Access Journals (Sweden)

    S. Radhika

    2016-04-01

    Full Text Available Maximum correntropy criterion (MCC based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.

  7. Adaptive Subband Filtering Method for MEMS Accelerometer Noise Reduction

    Directory of Open Access Journals (Sweden)

    Piotr PIETRZAK

    2008-12-01

    Full Text Available Silicon microaccelerometers can be considered as an alternative to high-priced piezoelectric sensors. Unfortunately, relatively high noise floor of commercially available MEMS (Micro-Electro-Mechanical Systems sensors limits the possibility of their usage in condition monitoring systems of rotating machines. The solution of this problem is the method of signal filtering described in the paper. It is based on adaptive subband filtering employing Adaptive Line Enhancer. For filter weights adaptation, two novel algorithms have been developed. They are based on the NLMS algorithm. Both of them significantly simplify its software and hardware implementation and accelerate the adaptation process. The paper also presents the software (Matlab and hardware (FPGA implementation of the proposed noise filter. In addition, the results of the performed tests are reported. They confirm high efficiency of the solution.

  8. Fast, accurate, and robust frequency offset estimation based on modified adaptive Kalman filter in coherent optical communication system

    Science.gov (United States)

    Yang, Yanfu; Xiang, Qian; Zhang, Qun; Zhou, Zhongqing; Jiang, Wen; He, Qianwen; Yao, Yong

    2017-09-01

    We propose a joint estimation scheme for fast, accurate, and robust frequency offset (FO) estimation along with phase estimation based on modified adaptive Kalman filter (MAKF). The scheme consists of three key modules: extend Kalman filter (EKF), lock detector, and FO cycle slip recovery. The EKF module estimates time-varying phase induced by both FO and laser phase noise. The lock detector module makes decision between acquisition mode and tracking mode and consequently sets the EKF tuning parameter in an adaptive manner. The third module can detect possible cycle slip in the case of large FO and make proper correction. Based on the simulation and experimental results, the proposed MAKF has shown excellent estimation performance featuring high accuracy, fast convergence, as well as the capability of cycle slip recovery.

  9. Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

    OpenAIRE

    Doo Yong Choi; Seong-Won Kim; Min-Ah Choi; Zong Woo Geem

    2016-01-01

    Rapid detection of bursts and leaks in water distribution systems (WDSs) can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA) systems and the establishment of district meter areas (DMAs). Nonetheless, no consideration has been given to how frequen...

  10. Speech Enhancement by Modified Convex Combination of Fractional Adaptive Filtering

    Directory of Open Access Journals (Sweden)

    M. Geravanchizadeh

    2014-12-01

    Full Text Available This paper presents new adaptive filtering techniques used in speech enhancement system. Adaptive filtering schemes are subjected to different trade-offs regarding their steady-state misadjustment, speed of convergence, and tracking performance. Fractional Least-Mean-Square (FLMS is a new adaptive algorithm which has better performance than the conventional LMS algorithm. Normalization of LMS leads to better performance of adaptive filter. Furthermore, convex combination of two adaptive filters improves its performance. In this paper, new convex combinational adaptive filtering methods in the framework of speech enhancement system are proposed. The proposed methods utilize the idea of normalization and fractional derivative, both in the design of different convex mixing strategies and their related component filters. To assess our proposed methods, simulation results of different LMS-based algorithms based on their convergence behavior (i.e., MSE plots and different objective and subjective criteria are compared. The objective and subjective evaluations include examining the results of SNR improvement, PESQ test, and listening tests for dual-channel speech enhancement. The powerful aspects of proposed methods are their low complexity, as expected with all LMS-based methods, along with a high convergence rate.

  11. On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

    Directory of Open Access Journals (Sweden)

    Mark Frogley

    2013-01-01

    Full Text Available To reduce the maintenance cost, avoid catastrophic failure, and improve the wind transmission system reliability, online condition monitoring system is critical important. In the real applications, many rotating mechanical faults, such as bearing surface defect, gear tooth crack, chipped gear tooth and so on generate impulsive signals. When there are these types of faults developing inside rotating machinery, each time the rotating components pass over the damage point, an impact force could be generated. The impact force will cause a ringing of the support structure at the structural natural frequency. By effectively detecting those periodic impulse signals, one group of rotating machine faults could be detected and diagnosed. However, in real wind turbine operations, impulsive fault signals are usually relatively weak to the background noise and vibration signals generated from other healthy components, such as shaft, blades, gears and so on. Moreover, wind turbine transmission systems work under dynamic operating conditions. This will further increase the difficulties in fault detection and diagnostics. Therefore, developing advanced signal processing methods to enhance the impulsive signals is in great needs.In this paper, an adaptive filtering technique will be applied for enhancing the fault impulse signals-to-noise ratio in wind turbine gear transmission systems. Multiple statistical features designed to quantify the impulsive signals of the processed signal are extracted for bearing fault detection. The multiple dimensional features are then transformed into one dimensional feature. A minimum error rate classifier will be designed based on the compressed feature to identify the gear transmission system with defect. Real wind turbine vibration signals will be used to demonstrate the effectiveness of the presented methodology.

  12. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin

    2011-01-01

    Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

  13. Low-power adaptive filter based on RNS components

    DEFF Research Database (Denmark)

    Bernocchi, Gian Luca; Cardarilli, Gian Carlo; Del Re, Andrea

    2007-01-01

    In this paper a low-power implementation of an adaptive FIR filter is presented. The filter is designed to meet the constraints of channel equalization for fixed wireless communications that typically requires a large number of taps, but a serial updating of the filter coefficients, based...... on the least mean squares (LMS) algorithm, is allowed. Previous work showed that the use of the residue number system (RNS) for the variable FIR filter grants advantages both in area and power consumption. On the other hand, the use of a binary serial implementation of the adaptation algorithm eliminates...... the need for complex scaling circuits in RNS. The advantages in terms of area and speed of the presented filter, with respect to its two's complement counterpart, are evaluated for implementations in standard cells....

  14. A Rapid Introduction to Adaptive Filtering

    CERN Document Server

    Vega, Leonardo Rey

    2013-01-01

    In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes with the discussion of severa...

  15. Adaptive robust Kalman filtering for precise point positioning

    International Nuclear Information System (INIS)

    Guo, Fei; Zhang, Xiaohong

    2014-01-01

    The optimality of precise point postioning (PPP) solution using a Kalman filter is closely connected to the quality of the a priori information about the process noise and the updated mesurement noise, which are sometimes difficult to obtain. Also, the estimation enviroment in the case of dynamic or kinematic applications is not always fixed but is subject to change. To overcome these problems, an adaptive robust Kalman filtering algorithm, the main feature of which introduces an equivalent covariance matrix to resist the unexpected outliers and an adaptive factor to balance the contribution of observational information and predicted information from the system dynamic model, is applied for PPP processing. The basic models of PPP including the observation model, dynamic model and stochastic model are provided first. Then an adaptive robust Kalmam filter is developed for PPP. Compared with the conventional robust estimator, only the observation with largest standardized residual will be operated by the IGG III function in each iteration to avoid reducing the contribution of the normal observations or even filter divergence. Finally, tests carried out in both static and kinematic modes have confirmed that the adaptive robust Kalman filter outperforms the classic Kalman filter by turning either the equivalent variance matrix or the adaptive factor or both of them. This becomes evident when analyzing the positioning errors in flight tests at the turns due to the target maneuvering and unknown process/measurement noises. (paper)

  16. Adaptive Control Using Residual Mode Filters Applied to Wind Turbines

    Science.gov (United States)

    Frost, Susan A.; Balas, Mark J.

    2011-01-01

    Many dynamic systems containing a large number of modes can benefit from adaptive control techniques, which are well suited to applications that have unknown parameters and poorly known operating conditions. In this paper, we focus on a model reference direct adaptive control approach that has been extended to handle adaptive rejection of persistent disturbances. We extend this adaptive control theory to accommodate problematic modal subsystems of a plant that inhibit the adaptive controller by causing the open-loop plant to be non-minimum phase. We will augment the adaptive controller using a Residual Mode Filter (RMF) to compensate for problematic modal subsystems, thereby allowing the system to satisfy the requirements for the adaptive controller to have guaranteed convergence and bounded gains. We apply these theoretical results to design an adaptive collective pitch controller for a high-fidelity simulation of a utility-scale, variable-speed wind turbine that has minimum phase zeros.

  17. Convergence Performance of Adaptive Algorithms of L-Filters

    Directory of Open Access Journals (Sweden)

    Robert Hudec

    2003-01-01

    Full Text Available This paper deals with convergence parameters determination of adaptive algorithms, which are used in adaptive L-filters design. Firstly the stability of adaptation process, convergence rate or adaptation time, and behaviour of convergence curve belong among basic properties of adaptive algorithms. L-filters with variety of adaptive algorithms were used to their determination. Convergence performances finding of adaptive filters is important mainly for their hardware applications, where filtration in real time or adaptation of coefficient filter with low capacity of input data are required.

  18. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

    Bibi, Adel Aamer

    2016-09-16

    Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations to actual translations in the image, which become unreliable in many realistic tracking scenarios including fast motion, occlusion, etc. In these cases, the traditional use of a single centered Gaussian as the target response impedes tracker performance and can lead to unrecoverable drift. To circumvent this major drawback, we propose a generic framework that can adaptively change the target response from frame to frame, so that the tracker is less sensitive to the cases where circular shifts do not reliably approximate translations. To do that, we reformulate the underlying optimization to solve for both the filter and target response jointly, where the latter is regularized by measurements made using actual translations. This joint problem has a closed form solution and thus allows for multiple templates, kernels, and multi-dimensional features. Extensive experiments on the popular OTB100 benchmark show that our target adaptive framework can be combined with many CF trackers to realize significant overall performance improvement (ranging from 3 %-13.5% in precision and 3.2 %-13% in accuracy), especially in categories where this adaptation is necessary (e.g. fast motion, motion blur, etc.). © Springer International Publishing AG 2016.

  19. Adaptive Filtering Queueing for Improving Fairness

    Directory of Open Access Journals (Sweden)

    Jui-Pin Yang

    2015-06-01

    Full Text Available In this paper, we propose a scalable and efficient Active Queue Management (AQM scheme to provide fair bandwidth sharing when traffic is congested dubbed Adaptive Filtering Queueing (AFQ. First, AFQ identifies the filtering level of an arriving packet by comparing it with a flow label selected at random from the first level to an estimated level in the filtering level table. Based on the accepted traffic estimation and the previous fair filtering level, AFQ updates the fair filtering level. Next, AFQ uses a simple packet-dropping algorithm to determine whether arriving packets are accepted or discarded. To enhance AFQ’s feasibility in high-speed networks, we propose a two-layer mapping mechanism to effectively simplify the packet comparison operations. Simulation results demonstrate that AFQ achieves optimal fairness when compared with Rotating Preference Queues (RPQ, Core-Stateless Fair Queueing (CSFQ, CHOose and Keep for responsive flows, CHOose and Kill for unresponsive flows (CHOKe and First-In First-Out (FIFO schemes under a variety of traffic conditions.

  20. Adaptive Rate Sampling and Filtering Based on Level Crossing Sampling

    Directory of Open Access Journals (Sweden)

    Saeed Mian Qaisar

    2009-01-01

    Full Text Available The recent sophistications in areas of mobile systems and sensor networks demand more and more processing resources. In order to maintain the system autonomy, energy saving is becoming one of the most difficult industrial challenges, in mobile computing. Most of efforts to achieve this goal are focused on improving the embedded systems design and the battery technology, but very few studies target to exploit the input signal time-varying nature. This paper aims to achieve power efficiency by intelligently adapting the processing activity to the input signal local characteristics. It is done by completely rethinking the processing chain, by adopting a non conventional sampling scheme and adaptive rate filtering. The proposed approach, based on the LCSS (Level Crossing Sampling Scheme presents two filtering techniques, able to adapt their sampling rate and filter order by online analyzing the input signal variations. Indeed, the principle is to intelligently exploit the signal local characteristics—which is usually never considered—to filter only the relevant signal parts, by employing the relevant order filters. This idea leads towards a drastic gain in the computational efficiency and hence in the processing power when compared to the classical techniques.

  1. Strong tracking adaptive Kalman filters for underwater vehicle dead reckoning

    Institute of Scientific and Technical Information of China (English)

    XIAO Kun; FANG Shao-ji; PANG Yong-jie

    2007-01-01

    To improve underwater vehicle dead reckoning, a developed strong tracking adaptive kalman filter is proposed. The filter is improved with an additional adaptive factor and an estimator of measurement noise covariance. Since the magnitude of fading factor is changed adaptively, the tracking ability of the filter is still enhanced in low velocity condition of underwater vehicles. The results of simulation tests prove the presented filter effective.

  2. Adaptive training of feedforward neural networks by Kalman filtering

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1995-02-01

    Adaptive training of feedforward neural networks by Kalman filtering is described. Adaptive training is particularly important in estimation by neural network in real-time environmental where the trained network is used for system estimation while the network is further trained by means of the information provided by the experienced/exercised ongoing operation. As result of this, neural network adapts itself to a changing environment to perform its mission without recourse to re-training. The performance of the training method is demonstrated by means of actual process signals from a nuclear power plant. (orig.)

  3. Combination of Adaptive Feedback Cancellation and Binaural Adaptive Filtering in Hearing Aids

    Directory of Open Access Journals (Sweden)

    Anthony Lombard

    2009-01-01

    Full Text Available We study a system combining adaptive feedback cancellation and adaptive filtering connecting inputs from both ears for signal enhancement in hearing aids. For the first time, such a binaural system is analyzed in terms of system stability, convergence of the algorithms, and possible interaction effects. As major outcomes of this study, a new stability condition adapted to the considered binaural scenario is presented, some already existing and commonly used feedback cancellation performance measures for the unilateral case are adapted to the binaural case, and possible interaction effects between the algorithms are identified. For illustration purposes, a blind source separation algorithm has been chosen as an example for adaptive binaural spatial filtering. Experimental results for binaural hearing aids confirm the theoretical findings and the validity of the new measures.

  4. Anti-clogging filter system

    Science.gov (United States)

    Brown, Erik P.

    2015-05-19

    An anti-clogging filter system for filtering a fluid containing large particles and small particles includes an enclosure with at least one individual elongated tubular filter element in the enclosure. The individual elongated tubular filter element has an internal passage, a closed end, an open end, and a filtering material in or on the individual elongated tubular filter element. The fluid travels through the open end of the elongated tubular element and through the internal passage and through the filtering material. An anti-clogging element is positioned on or adjacent the individual elongated tubular filter element and provides a fluid curtain that preferentially directs the larger particulates to one area of the filter material allowing the remainder of the filter material to remain more efficient.

  5. Normalised subband adaptive filtering with extended adaptiveness on degree of subband filters

    Science.gov (United States)

    Samuyelu, Bommu; Rajesh Kumar, Pullakura

    2017-12-01

    This paper proposes an adaptive normalised subband adaptive filtering (NSAF) to accomplish the betterment of NSAF performance. In the proposed NSAF, an extended adaptiveness is introduced from its variants in two ways. In the first way, the step-size is set adaptive, and in the second way, the selection of subbands is set adaptive. Hence, the proposed NSAF is termed here as variable step-size-based NSAF with selected subbands (VS-SNSAF). Experimental investigations are carried out to demonstrate the performance (in terms of convergence) of the VS-SNSAF against the conventional NSAF and its state-of-the-art adaptive variants. The results report the superior performance of VS-SNSAF over the traditional NSAF and its variants. It is also proved for its stability, robustness against noise and substantial computing complexity.

  6. Applying Maxi-adjustment to Adaptive Information Filtering Agents

    OpenAIRE

    Lau, Raymond; ter Hofstede, Arthur H. M.; Bruza, Peter D.

    2000-01-01

    Learning and adaptation is a fundamental property of intelligent agents. In the context of adaptive information filtering, a filtering agent's beliefs about a user's information needs have to be revised regularly with reference to the user's most current information preferences. This learning and adaptation process is essential for maintaining the agent's filtering performance. The AGM belief revision paradigm provides a rigorous foundation for modelling rational and minimal changes to an age...

  7. Multi-rate cubature Kalman filter based data fusion method with residual compensation to adapt to sampling rate discrepancy in attitude measurement system.

    Science.gov (United States)

    Guo, Xiaoting; Sun, Changku; Wang, Peng

    2017-08-01

    This paper investigates the multi-rate inertial and vision data fusion problem in nonlinear attitude measurement systems, where the sampling rate of the inertial sensor is much faster than that of the vision sensor. To fully exploit the high frequency inertial data and obtain favorable fusion results, a multi-rate CKF (Cubature Kalman Filter) algorithm with estimated residual compensation is proposed in order to adapt to the problem of sampling rate discrepancy. During inter-sampling of slow observation data, observation noise can be regarded as infinite. The Kalman gain is unknown and approaches zero. The residual is also unknown. Therefore, the filter estimated state cannot be compensated. To obtain compensation at these moments, state error and residual formulas are modified when compared with the observation data available moments. Self-propagation equation of the state error is established to propagate the quantity from the moments with observation to the moments without observation. Besides, a multiplicative adjustment factor is introduced as Kalman gain, which acts on the residual. Then the filter estimated state can be compensated even when there are no visual observation data. The proposed method is tested and verified in a practical setup. Compared with multi-rate CKF without residual compensation and single-rate CKF, a significant improvement is obtained on attitude measurement by using the proposed multi-rate CKF with inter-sampling residual compensation. The experiment results with superior precision and reliability show the effectiveness of the proposed method.

  8. Spherical Tensor Calculus for Local Adaptive Filtering

    Science.gov (United States)

    Reisert, Marco; Burkhardt, Hans

    In 3D image processing tensors play an important role. While rank-1 and rank-2 tensors are well understood and commonly used, higher rank tensors are rare. This is probably due to their cumbersome rotation behavior which prevents a computationally efficient use. In this chapter we want to introduce the notion of a spherical tensor which is based on the irreducible representations of the 3D rotation group. In fact, any ordinary cartesian tensor can be decomposed into a sum of spherical tensors, while each spherical tensor has a quite simple rotation behavior. We introduce so called tensorial harmonics that provide an orthogonal basis for spherical tensor fields of any rank. It is just a generalization of the well known spherical harmonics. Additionally we propose a spherical derivative which connects spherical tensor fields of different degree by differentiation. Based on the proposed theory we present two applications. We propose an efficient algorithm for dense tensor voting in 3D, which makes use of tensorial harmonics decomposition of the tensor-valued voting field. In this way it is possible to perform tensor voting by linear-combinations of convolutions in an efficient way. Secondly, we propose an anisotropic smoothing filter that uses a local shape and orientation adaptive filter kernel which can be computed efficiently by the use spherical derivatives.

  9. Simulation for noise cancellation using LMS adaptive filter

    Science.gov (United States)

    Lee, Jia-Haw; Ooi, Lu-Ean; Ko, Ying-Hao; Teoh, Choe-Yung

    2017-06-01

    In this paper, the fundamental algorithm of noise cancellation, Least Mean Square (LMS) algorithm is studied and enhanced with adaptive filter. The simulation of the noise cancellation using LMS adaptive filter algorithm is developed. The noise corrupted speech signal and the engine noise signal are used as inputs for LMS adaptive filter algorithm. The filtered signal is compared to the original noise-free speech signal in order to highlight the level of attenuation of the noise signal. The result shows that the noise signal is successfully canceled by the developed adaptive filter. The difference of the noise-free speech signal and filtered signal are calculated and the outcome implies that the filtered signal is approaching the noise-free speech signal upon the adaptive filtering. The frequency range of the successfully canceled noise by the LMS adaptive filter algorithm is determined by performing Fast Fourier Transform (FFT) on the signals. The LMS adaptive filter algorithm shows significant noise cancellation at lower frequency range.

  10. OPTIMIZATION OF ADVANCED FILTER SYSTEMS

    Energy Technology Data Exchange (ETDEWEB)

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

    1998-04-30

    Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and by design issues, such as cantilevered filter elements damaged by ash bridging, or excessively close packing of filtering surfaces resulting in unacceptable pressure drop or filtering surface plugging. This test experience has focused the issues and has helped to define advanced hot gas filter design concepts that offer higher reliability. Westinghouse has identified two advanced ceramic barrier filter concepts that are configured to minimize the possibility of ash bridge formation and to be robust against ash bridges should they occur. The ''inverted candle filter system'' uses arrays of thin-walled, ceramic candle-type filter elements with inside-surface filtering, and contains the filter elements in metal enclosures for complete separation from ash bridges. The ''sheet filter system'' uses ceramic, flat plate filter elements supported from vertical pipe-header arrays that provide geometry that avoids the buildup of ash bridges and allows free fall of the back-pulse released filter cake. The Optimization of Advanced Filter Systems program is being conducted to evaluate these two advanced designs and to ultimately demonstrate one of the concepts in pilot scale. In the Base Contract program, the subject of this report, Westinghouse has developed conceptual designs of the two advanced ceramic barrier filter systems to assess their performance, availability and cost potential, and to identify technical issues that may hinder the commercialization of the technologies. A plan for the Option I, bench

  11. User interaction with user-adaptive information filters

    NARCIS (Netherlands)

    Cramer, H.S.M.; Evers, V.; Someren, van M.W.; Wielinga, B.J.; Besselink, S.; Rutledge, L.W.; Stash, N.; Aroyo, L.M.; Aykin, N.M.

    2007-01-01

    User-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two case studies

  12. User Interaction with User-Adaptive Information Filters

    NARCIS (Netherlands)

    H. Cramer; V. Evers; M. van Someren; B. Wielinga; S. Besselink; L. Rutledge (Lloyd); N. Stash; L. Aroyo (Lora)

    2007-01-01

    htmlabstractUser-adaptive information filters can be a tool to achieve timely delivery of the right information to the right person, a feat critical in crisis management. This paper explores interaction issues that need to be taken into account when designing a user-adaptive information filter. Two

  13. Filter cake breaker systems

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, Marcelo H.F. [Poland Quimica Ltda., Duque de Caxias, RJ (Brazil)

    2004-07-01

    Drilling fluids filter cakes are based on a combination of properly graded dispersed particles and polysaccharide polymers. High efficiency filter cakes are formed by these combination , and their formation on wellbore walls during the drilling process has, among other roles, the task of protecting the formation from instantaneous or accumulative invasion of drilling fluid filtrate, granting stability to well and production zones. Filter cake minimizes contact between drilling fluid filtrate and water, hydrocarbons and clay existent in formations. The uniform removal of the filter cake from the entire interval is a critical factor of the completion process. The main methods used to breaking filter cake are classified into two groups, external or internal, according to their removal mechanism. The aim of this work is the presentation of these mechanisms as well their efficiency. (author)

  14. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

    Bibi, Adel Aamer; Mueller, Matthias; Ghanem, Bernard

    2016-01-01

    Most correlation filter (CF) based trackers utilize the circulant structure of the training data to learn a linear filter that best regresses this data to a hand-crafted target response. These circularly shifted patches are only approximations

  15. A New Adaptive Framework for Collaborative Filtering Prediction.

    Science.gov (United States)

    Almosallam, Ibrahim A; Shang, Yi

    2008-06-01

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

  16. Adaptive probabilistic collocation based Kalman filter for unsaturated flow problem

    Science.gov (United States)

    Man, J.; Li, W.; Zeng, L.; Wu, L.

    2015-12-01

    The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a relatively large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the Polynomial Chaos to approximate the original system. In this way, the sampling error can be reduced. However, PCKF suffers from the so called "cure of dimensionality". When the system nonlinearity is strong and number of parameters is large, PCKF is even more computationally expensive than EnKF. Motivated by recent developments in uncertainty quantification, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problem. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected. The "restart" technology is used to alleviate the inconsistency between model parameters and states. The performance of RAPCKF is tested by unsaturated flow numerical cases. It is shown that RAPCKF is more efficient than EnKF with the same computational cost. Compared with the traditional PCKF, the RAPCKF is more applicable in strongly nonlinear and high dimensional problems.

  17. Diagnostic analysis of vibration signals using adaptive digital filtering techniques

    Science.gov (United States)

    Jewell, R. E.; Jones, J. H.; Paul, J. E.

    1983-01-01

    Signal enhancement techniques are described using recently developed digital adaptive filtering equipment. Adaptive filtering concepts are not new; however, as a result of recent advances in microprocessor-based electronics, hardware has been developed that has stable characteristics and of a size exceeding 1000th order. Selected data processing examples are presented illustrating spectral line enhancement, adaptive noise cancellation, and transfer function estimation in the presence of corrupting noise.

  18. A New Adaptive H-Infinity Filtering Algorithm for the GPS/INS Integrated Navigation

    Science.gov (United States)

    Jiang, Chen; Zhang, Shu-Bi; Zhang, Qiu-Zhao

    2016-01-01

    The Kalman filter is an optimal estimator with numerous applications in technology, especially in systems with Gaussian distributed noise. Moreover, the adaptive Kalman filtering algorithms, based on the Kalman filter, can control the influence of dynamic model errors. In contrast to the adaptive Kalman filtering algorithms, the H-infinity filter is able to address the interference of the stochastic model by minimization of the worst-case estimation error. In this paper, a novel adaptive H-infinity filtering algorithm, which integrates the adaptive Kalman filter and the H-infinity filter in order to perform a comprehensive filtering algorithm, is presented. In the proposed algorithm, a robust estimation method is employed to control the influence of outliers. In order to verify the proposed algorithm, experiments with real data of the Global Positioning System (GPS) and Inertial Navigation System (INS) integrated navigation, were conducted. The experimental results have shown that the proposed algorithm has multiple advantages compared to the other filtering algorithms. PMID:27999361

  19. Restoration of nuclear medicine images using adaptive Wiener filters

    International Nuclear Information System (INIS)

    Meinel, G.

    1989-01-01

    An adaptive Wiener filter implementation for restoration of nuclear medicine images is described. These are considerably disturbed both deterministically (definition) and stochastically (Poisson's quantum noise). After introduction of an image model, description of necessary parameter approximations and information on optimum design methods the implementation is described. The filter operates adaptively as concerns the local signal-to-noise ratio and is based on a filter band concept. To verify the restoration effect size numbers are introduced and the filter is tested against these numbers. (author)

  20. Real time microcontroller implementation of an adaptive myoelectric filter.

    Science.gov (United States)

    Bagwell, P J; Chappell, P H

    1995-03-01

    This paper describes a real time digital adaptive filter for processing myoelectric signals. The filter time constant is automatically selected by the adaptation algorithm, giving a significant improvement over linear filters for estimating the muscle force and controlling a prosthetic device. Interference from mains sources often produces problems for myoelectric processing, and so 50 Hz and all harmonic frequencies are reduced by an averaging filter and differential process. This makes practical electrode placement and contact less critical and time consuming. An economic real time implementation is essential for a prosthetic controller, and this is achieved using an Intel 80C196KC microcontroller.

  1. Real Time Adaptive Stream-oriented Geo-data Filtering

    Directory of Open Access Journals (Sweden)

    A. A. Golovkov

    2016-01-01

    Full Text Available The cutting-edge engineering maintenance software systems of various objects are aimed at processing of geo-location data coming from the employees’ mobile devices in real time. To reduce the amount of transmitted data such systems, usually, use various filtration methods of geo-coordinates recorded directly on mobile devices.The paper identifies the reasons for errors of geo-data coming from different sources, and proposes an adaptive dynamic method to filter geo-location data. Compared with the static method previously described in the literature [1] the approach offers to align adaptively the filtering threshold with changing characteristics of coordinates from many sources of geo-location data.To evaluate the efficiency of the developed filter method have been involved about 400 thousand points, representing motion paths of different type (on foot, by car and high-speed train and parking (indoors, outdoors, near high-rise buildings to take data from different mobile devices. Analysis of results has shown that the benefits of the proposed method are the more precise location of long parking (up to 6 hours and coordinates when user is in motion, the capability to provide steam-oriented filtering of data from different sources that allows to use the approach in geo-information systems, providing continuous monitoring of the location in streamoriented data processing in real time. The disadvantage is a little bit more computational complexity and increasing amount of points of the final track as compared to other filtration techniques.In general, the developed approach enables a significant quality improvement of displayed paths of moving mobile objects.

  2. Hardware implementation of adaptive filtering using charge-coupled devices. [For perimeter security sensors

    Energy Technology Data Exchange (ETDEWEB)

    Donohoe, G.W.

    1977-01-01

    Sandia Laboratories' Digital Systems Division/1734, as part of its work on the Base and Installation Security Systems (BISS) program has been making use of adaptive digital filters to improve the signal-to-noise ratio of perimeter sensor signals. In particular, the Widrow-Hoff least-mean-squares algorithm has been used extensively. This non-recursive linear predictor has been successful in extracting aperiodic signals from periodic noise. The adaptive filter generates a predictor signal which is subtracted from the input signal to produce an error signal. The value of this error is fed back to the filter to improve the quality of the next prediction. Implementation of the Widrow adaptive filter using a Charge-Coupled Device tapped analog delay line, analog voltage multipliers and operational amplifiers is described. The resulting filter adapts to signals with frequency components as high as several megahertz.

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

    Science.gov (United States)

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

    2016-07-16

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

  4. Adaptive Kalman filtering for diagnosis of multiple component degradations

    International Nuclear Information System (INIS)

    Aumeier, S. E.; Alpay, B.; Lee, J. C.

    2005-01-01

    We have developed an adaptive Kalman filtering algorithm for the diagnosis of faults or degradations of multiple components in nuclear power plants. We propose to detect the presence and magnitude of the fault(s) through noisy system observations when the measurements indicate significant deviations from predictions. Our diagnostic algorithm uses the measurement residuals, i.e., the difference between the measurements and predictions, to generate a noise input to the uncertain component state in an adaptive Kalman filtering algorithm so that various postulated component transitions or degradations may be statistically represented. The diagnostic algorithm has been tested with a balance of plant (BOP) model of a boiling water reactor (BWR). We have presented a set of algorithms for the detection and diagnosis of component faults of arbitrary magnitude and type within a multi-component system. By analyzing a number of transients including the one example illustrated in the paper, we find that these algorithms are not only capable of determining the correct component fault and magnitude for single components but also they can be used to determine binary faults satisfactorily. Additional study is under way to evaluate the performance of the proposed algorithm including the sensitivity of the diagnostic time to adaptive noise matrix introduced (see equations 7 and 8 illustrated in the paper)

  5. Adaptive Filtering to Enhance Noise Immunity of Impedance and Admittance Spectroscopy: Comparison with Fourier Transformation

    Science.gov (United States)

    Stupin, Daniil D.; Koniakhin, Sergei V.; Verlov, Nikolay A.; Dubina, Michael V.

    2017-05-01

    The time-domain technique for impedance spectroscopy consists of computing the excitation voltage and current response Fourier images by fast or discrete Fourier transformation and calculating their relation. Here we propose an alternative method for excitation voltage and current response processing for deriving a system impedance spectrum based on a fast and flexible adaptive filtering method. We show the equivalence between the problem of adaptive filter learning and deriving the system impedance spectrum. To be specific, we express the impedance via the adaptive filter weight coefficients. The noise-canceling property of adaptive filtering is also justified. Using the RLC circuit as a model system, we experimentally show that adaptive filtering yields correct admittance spectra and elements ratings in the high-noise conditions when the Fourier-transform technique fails. Providing the additional sensitivity of impedance spectroscopy, adaptive filtering can be applied to otherwise impossible-to-interpret time-domain impedance data. The advantages of adaptive filtering are justified with practical living-cell impedance measurements.

  6. Design and implementation of predictive filtering system for current reference generation of active power filter

    Energy Technology Data Exchange (ETDEWEB)

    Kilic, Tomislav; Milun, Stanko; Petrovic, Goran [FESB University of Split, Faculty of Electrical Engineering, Machine Engineering and Naval Architecture, R. Boskovica bb, 21000, Split (Croatia)

    2007-02-15

    The shunt active power filters are used to attenuate the harmonic currents in power systems by injecting equal but opposite compensating currents. Successful control of the active filters requires an accurate current reference. In this paper the current reference determination based on predictive filtering structure is presented. Current reference was obtained by taking the difference of load current and its fundamental harmonic. For fundamental harmonic determination with no time delay a combination of digital predictive filter and low pass filter is used. The proposed method was implemented on a laboratory prototype of a three-phase active power filter. The algorithm for current reference determination was adapted and implemented on DSP controller. Simulation and experimental results show that the active power filter with implemented predictive filtering structure gives satisfactory performance in power system harmonic attenuation. (author)

  7. Filter indexing for spectrophotometer system

    International Nuclear Information System (INIS)

    Chamran, M.M.; Scott, L.B.; Williams, P.B.

    1982-01-01

    A spectrophotometer system has an optical system for transmitting a beam from a source at select wavelengths onto a detector. A plurality of filters are positioned in a tray. A stepper mechanism indexes the tray along a path. A microcomputer controls the stepper mechanism and the optical system. The wavelength is successively changed over a range, the tray is indexed to move a select filter into the beam at a predetermined wavelength and the changing is discontinued during indexing

  8. Superresolution restoration of an image sequence: adaptive filtering approach.

    Science.gov (United States)

    Elad, M; Feuer, A

    1999-01-01

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

  9. Filter. Remix. Make.: Cultivating Adaptability through Multimodality

    Science.gov (United States)

    Dusenberry, Lisa; Hutter, Liz; Robinson, Joy

    2015-01-01

    This article establishes traits of adaptable communicators in the 21st century, explains why adaptability should be a goal of technical communication educators, and shows how multimodal pedagogy supports adaptability. Three examples of scalable, multimodal assignments (infographics, research interviews, and software demonstrations) that evidence…

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

    Science.gov (United States)

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

    2015-01-01

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

  11. Simultaneous Learning and Filtering without Delusions: A Bayes-Optimal Derivation of Combining Predictive Inference and AdaptiveFiltering

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

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

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

    Science.gov (United States)

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

    2014-05-09

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

  13. A Nonlinear Adaptive Filter for Gyro Thermal Bias Error Cancellation

    Science.gov (United States)

    Galante, Joseph M.; Sanner, Robert M.

    2012-01-01

    Deterministic errors in angular rate gyros, such as thermal biases, can have a significant impact on spacecraft attitude knowledge. In particular, thermal biases are often the dominant error source in MEMS gyros after calibration. Filters, such as J\\,fEKFs, are commonly used to mitigate the impact of gyro errors and gyro noise on spacecraft closed loop pointing accuracy, but often have difficulty in rapidly changing thermal environments and can be computationally expensive. In this report an existing nonlinear adaptive filter is used as the basis for a new nonlinear adaptive filter designed to estimate and cancel thermal bias effects. A description of the filter is presented along with an implementation suitable for discrete-time applications. A simulation analysis demonstrates the performance of the filter in the presence of noisy measurements and provides a comparison with existing techniques.

  14. Adaptive Filtering for Non-Gaussian Processes

    DEFF Research Database (Denmark)

    Kidmose, Preben

    2000-01-01

    A new stochastic gradient robust filtering method, based on a non-linear amplitude transformation, is proposed. The method requires no a priori knowledge of the characteristics of the input signals and it is insensitive to the signals distribution and to the stationarity of the signals. A simulat...

  15. Analysis on Influence Factors of Adaptive Filter Acting on ANC

    Science.gov (United States)

    Zhang, Xiuqun; Zou, Liang; Ni, Guangkui; Wang, Xiaojun; Han, Tao; Zhao, Quanfu

    The noise problem has become more and more serious in recent years. The adaptive filter theory which is applied in ANC [1] (active noise control) has also attracted more and more attention. In this article, the basic principle and algorithm of adaptive theory are both researched. And then the influence factor that affects its covergence rate and noise reduction is also simulated.

  16. Adaptive filtering for hidden node detection and tracking in networks.

    Science.gov (United States)

    Hamilton, Franz; Setzer, Beverly; Chavez, Sergio; Tran, Hien; Lloyd, Alun L

    2017-07-01

    The identification of network connectivity from noisy time series is of great interest in the study of network dynamics. This connectivity estimation problem becomes more complicated when we consider the possibility of hidden nodes within the network. These hidden nodes act as unknown drivers on our network and their presence can lead to the identification of false connections, resulting in incorrect network inference. Detecting the parts of the network they are acting on is thus critical. Here, we propose a novel method for hidden node detection based on an adaptive filtering framework with specific application to neuronal networks. We consider the hidden node as a problem of missing variables when model fitting and show that the estimated system noise covariance provided by the adaptive filter can be used to localize the influence of the hidden nodes and distinguish the effects of different hidden nodes. Additionally, we show that the sequential nature of our algorithm allows for tracking changes in the hidden node influence over time.

  17. Slice image pretreatment for cone-beam computed tomography based on adaptive filter

    International Nuclear Information System (INIS)

    Huang Kuidong; Zhang Dinghua; Jin Yanfang

    2009-01-01

    According to the noise properties and the serial slice image characteristics in Cone-Beam Computed Tomography (CBCT) system, a slice image pretreatment for CBCT based on adaptive filter was proposed. The judging criterion for the noise is established firstly. All pixels are classified into two classes: adaptive center weighted modified trimmed mean (ACWMTM) filter is used for the pixels corrupted by Gauss noise and adaptive median (AM) filter is used for the pixels corrupted by impulse noise. In ACWMTM filtering algorithm, the estimated Gauss noise standard deviation in the current slice image with offset window is replaced by the estimated standard deviation in the adjacent slice image to the current with the corresponding window, so the filtering accuracy of the serial images is improved. The pretreatment experiment on CBCT slice images of wax model of hollow turbine blade shows that the method makes a good performance both on eliminating noises and on protecting details. (authors)

  18. A generalized adaptive mathematical morphological filter for LIDAR data

    Science.gov (United States)

    Cui, Zheng

    Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in

  19. A bag adapted for the handling of a filtering element or filter unit

    International Nuclear Information System (INIS)

    Marshall, D.A.G.

    1980-01-01

    The invention relates to a transparent, flexible, synthetic plastics bag adapted to contain a filter element or filter unit so that the latter can be inserted into or removed from a filter casing or duct while being contained in the bag. The bag has a neck portion which is capable of being removably secured in an air-tight manner on to a part of the casing, and gloves or glove portions are provided in, or are formed in, the wall of the bag to permit handles on the filter element or unit to be grasped. (author)

  20. Orthonormal filters for identification in active control systems

    International Nuclear Information System (INIS)

    Mayer, Dirk

    2015-01-01

    Many active noise and vibration control systems require models of the control paths. When the controlled system changes slightly over time, adaptive digital filters for the identification of the models are useful. This paper aims at the investigation of a special class of adaptive digital filters: orthonormal filter banks possess the robust and simple adaptation of the widely applied finite impulse response (FIR) filters, but at a lower model order, which is important when considering implementation on embedded systems. However, the filter banks require prior knowledge about the resonance frequencies and damping of the structure. This knowledge can be supposed to be of limited precision, since in many practical systems, uncertainties in the structural parameters exist. In this work, a procedure using a number of training systems to find the fixed parameters for the filter banks is applied. The effect of uncertainties in the prior knowledge on the model error is examined both with a basic example and in an experiment. Furthermore, the possibilities to compensate for the imprecise prior knowledge by a higher filter order are investigated. Also comparisons with FIR filters are implemented in order to assess the possible advantages of the orthonormal filter banks. Numerical and experimental investigations show that significantly lower computational effort can be reached by the filter banks under certain conditions. (paper)

  1. Hybrid vs Adaptive Ensemble Kalman Filtering for Storm Surge Forecasting

    Science.gov (United States)

    Altaf, M. U.; Raboudi, N.; Gharamti, M. E.; Dawson, C.; McCabe, M. F.; Hoteit, I.

    2014-12-01

    Recent storm surge events due to Hurricanes in the Gulf of Mexico have motivated the efforts to accurately forecast water levels. Toward this goal, a parallel architecture has been implemented based on a high resolution storm surge model, ADCIRC. However the accuracy of the model notably depends on the quality and the recentness of the input data (mainly winds and bathymetry), model parameters (e.g. wind and bottom drag coefficients), and the resolution of the model grid. Given all these uncertainties in the system, the challenge is to build an efficient prediction system capable of providing accurate forecasts enough ahead of time for the authorities to evacuate the areas at risk. We have developed an ensemble-based data assimilation system to frequently assimilate available data into the ADCIRC model in order to improve the accuracy of the model. In this contribution we study and analyze the performances of different ensemble Kalman filter methodologies for efficient short-range storm surge forecasting, the aim being to produce the most accurate forecasts at the lowest possible computing time. Using Hurricane Ike meteorological data to force the ADCIRC model over a domain including the Gulf of Mexico coastline, we implement and compare the forecasts of the standard EnKF, the hybrid EnKF and an adaptive EnKF. The last two schemes have been introduced as efficient tools for enhancing the behavior of the EnKF when implemented with small ensembles by exploiting information from a static background covariance matrix. Covariance inflation and localization are implemented in all these filters. Our results suggest that both the hybrid and the adaptive approach provide significantly better forecasts than those resulting from the standard EnKF, even when implemented with much smaller ensembles.

  2. Learning Motivation and Adaptive Video Caption Filtering for EFL Learners Using Handheld Devices

    Science.gov (United States)

    Hsu, Ching-Kun

    2015-01-01

    The aim of this study was to provide adaptive assistance to improve the listening comprehension of eleventh grade students. This study developed a video-based language learning system for handheld devices, using three levels of caption filtering adapted to student needs. Elementary level captioning excluded 220 English sight words (see Section 1…

  3. Interference suppression using a SAW-based adaptive filter

    Science.gov (United States)

    Saulnier, Gary J.; Grant, Calvin J.; Das, Pankaj K.

    The structure and performance of a transversal filter interference suppressor that has been constructed using a surface acoustic wave (SAW) delay line are described. The delay line operates at a center frequency of 300 MHz and has eight equally spaced taps with an intertap delay of 150 ns. In the programmable mode, the tap weights are externally controllable, and in the adaptive mode, the tap weights are adjusted using the Widrow-Hoff least-mean-squared algorithm. Experimental results are provided that illustrate the performance of the filter in both the adaptive and programmable modes. Filter responses obtained in the adaptive mode are shown, along with spectra demonstrating the corresponding interference suppression. Bit-error-rate performance results for a single-tone jammer interfering with a direct sequence spread spectrum signal are presented.

  4. Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    The alignment accuracy of the strap-down inertial navigation system (SINS) of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.

  5. Artifact removal from EEG signals using adaptive filters in cascade

    Science.gov (United States)

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

    2007-11-01

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

  6. Artifact removal from EEG signals using adaptive filters in cascade

    International Nuclear Information System (INIS)

    Garces Correa, A; Laciar, E; Patino, H D; Valentinuzzi, M E

    2007-01-01

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

  7. Artifact removal from EEG signals using adaptive filters in cascade

    Energy Technology Data Exchange (ETDEWEB)

    Garces Correa, A [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Laciar, E [Gabinete de TecnologIa Medica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Patino, H D [Instituto de Automatica, Facultad de Ingenieria, Universidad Nacional de San Juan (Argentina); Valentinuzzi, M E [Instituto Superior de Investigaciones Biologicas (INSIBIO), UNT-CONICET, Tucuman (Argentina)

    2007-11-15

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

  8. Reduced-Rank Adaptive Filtering Using Krylov Subspace

    Directory of Open Access Journals (Sweden)

    Sergueï Burykh

    2003-01-01

    Full Text Available A unified view of several recently introduced reduced-rank adaptive filters is presented. As all considered methods use Krylov subspace for rank reduction, the approach taken in this work is inspired from Krylov subspace methods for iterative solutions of linear systems. The alternative interpretation so obtained is used to study the properties of each considered technique and to relate one reduced-rank method to another as well as to algorithms used in computational linear algebra. Practical issues are discussed and low-complexity versions are also included in our study. It is believed that the insight developed in this paper can be further used to improve existing reduced-rank methods according to known results in the domain of Krylov subspace methods.

  9. Reversible wavelet filter banks with side informationless spatially adaptive low-pass filters

    Science.gov (United States)

    Abhayaratne, Charith

    2011-07-01

    Wavelet transforms that have an adaptive low-pass filter are useful in applications that require the signal singularities, sharp transitions, and image edges to be left intact in the low-pass signal. In scalable image coding, the spatial resolution scalability is achieved by reconstructing the low-pass signal subband, which corresponds to the desired resolution level, and discarding other high-frequency wavelet subbands. In such applications, it is vital to have low-pass subbands that are not affected by smoothing artifacts associated with low-pass filtering. We present the mathematical framework for achieving 1-D wavelet transforms that have a spatially adaptive low-pass filter (SALP) using the prediction-first lifting scheme. The adaptivity decisions are computed using the wavelet coefficients, and no bookkeeping is required for the perfect reconstruction. Then, 2-D wavelet transforms that have a spatially adaptive low-pass filter are designed by extending the 1-D SALP framework. Because the 2-D polyphase decompositions are used in this case, the 2-D adaptivity decisions are made nonseparable as opposed to the separable 2-D realization using 1-D transforms. We present examples using the 2-D 5/3 wavelet transform and their lossless image coding and scalable decoding performances in terms of quality and resolution scalability. The proposed 2-D-SALP scheme results in better performance compared to the existing adaptive update lifting schemes.

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

    Science.gov (United States)

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

    2016-02-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  12. BPSK Receiver Based on Recursive Adaptive Filter with Remodulation

    Directory of Open Access Journals (Sweden)

    N. Milosevic

    2011-12-01

    Full Text Available This paper proposes a new binary phase shift keying (BPSK signal receiver intended for reception under conditions of significant carrier frequency offsets. The recursive adaptive filter with least mean squares (LMS adaptation is used. The proposed receiver has a constant, defining the balance between the recursive and the nonrecursive part of the filter, whose proper choice allows a simple construction of the receiver. The correct choice of this parameter could result in unitary length of the filter. The proposed receiver has performance very close to the performance of the BPSK receiver with perfect frequency synchronization, in a wide range of frequency offsets (plus/minus quarter of the signal bandwidth. The results obtained by the software simulation are confirmed by the experimental results measured on the receiver realized with the universal software radio peripheral (USRP, with the baseband signal processing at personal computer (PC.

  13. An adaptive Kalman filter for speckle reductions in ultrasound images

    International Nuclear Information System (INIS)

    Castellini, G.; Labate, D.; Masotti, L.; Mannini, E.; Rocchi, S.

    1988-01-01

    Speckle is the term used to describe the granular appearance found in ultrasound images. The presence of speckle reduces the diagnostic potential of the echographic technique because it tends to mask small inhomogeneities of the investigated tissue. We developed a new method of speckle reductions that utilizes an adaptive one-dimensional Kalman filter based on the assumption that the observed image can be considered as a superimposition of speckle on a ''true images''. The filter adaptivity, necessary to avoid loss of resolution, has been obtained by statistical considerations on the local signal variations. The results of the applications of this particular Kalman filter, both on A-Mode and B-MODE images, show a significant speckle reduction

  14. Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

    DEFF Research Database (Denmark)

    Mahmoudi, Zeinab; Poulsen, Niels Kjølstad; Madsen, Henrik

    2017-01-01

    The purpose of this study is to develop an adaptive unscented Kalman filter (UKF) by tuning the measurement noise covariance. We use the maximum likelihood estimation (MLE) and the covariance matching (CM) method to estimate the noise covariance. The multi-step prediction errors generated...

  15. Application of adaptive Kalman filter in vehicle laser Doppler velocimetry

    Science.gov (United States)

    Fan, Zhe; Sun, Qiao; Du, Lei; Bai, Jie; Liu, Jingyun

    2018-03-01

    Due to the variation of road conditions and motor characteristics of vehicle, great root-mean-square (rms) error and outliers would be caused. Application of Kalman filter in laser Doppler velocimetry(LDV) is important to improve the velocity measurement accuracy. In this paper, the state-space model is built by using current statistical model. A strategy containing two steps is adopted to make the filter adaptive and robust. First, the acceleration variance is adaptively adjusted by using the difference of predictive observation and measured observation. Second, the outliers would be identified and the measured noise variance would be adjusted according to the orthogonal property of innovation to reduce the impaction of outliers. The laboratory rotating table experiments show that adaptive Kalman filter greatly reduces the rms error from 0.59 cm/s to 0.22 cm/s and has eliminated all the outliers. Road experiments compared with a microwave radar show that the rms error of LDV is 0.0218 m/s, and it proves that the adaptive Kalman filtering is suitable for vehicle speed signal processing.

  16. Regularized Adaptive Notch Filters for Acoustic Howling Suppression

    DEFF Research Database (Denmark)

    Gil-Cacho, Pepe; van Waterschoot, Toon; Moonen, Marc

    2009-01-01

    In this paper, a method for the suppression of acoustic howling is developed, based on adaptive notch filters (ANF) with regularization (RANF). The method features three RANFs working in parallel to achieve frequency tracking, howling detection and suppression. The ANF-based approach to howling...

  17. Attitude determination and calibration using a recursive maximum likelihood-based adaptive Kalman filter

    Science.gov (United States)

    Kelly, D. A.; Fermelia, A.; Lee, G. K. F.

    1990-01-01

    An adaptive Kalman filter design that utilizes recursive maximum likelihood parameter identification is discussed. At the center of this design is the Kalman filter itself, which has the responsibility for attitude determination. At the same time, the identification algorithm is continually identifying the system parameters. The approach is applicable to nonlinear, as well as linear systems. This adaptive Kalman filter design has much potential for real time implementation, especially considering the fast clock speeds, cache memory and internal RAM available today. The recursive maximum likelihood algorithm is discussed in detail, with special attention directed towards its unique matrix formulation. The procedure for using the algorithm is described along with comments on how this algorithm interacts with the Kalman filter.

  18. Selected annotated bibliographies for adaptive filtering of digital image data

    Science.gov (United States)

    Mayers, Margaret; Wood, Lynnette

    1988-01-01

    Digital spatial filtering is an important tool both for enhancing the information content of satellite image data and for implementing cosmetic effects which make the imagery more interpretable and appealing to the eye. Spatial filtering is a context-dependent operation that alters the gray level of a pixel by computing a weighted average formed from the gray level values of other pixels in the immediate vicinity.Traditional spatial filtering involves passing a particular filter or set of filters over an entire image. This assumes that the filter parameter values are appropriate for the entire image, which in turn is based on the assumption that the statistics of the image are constant over the image. However, the statistics of an image may vary widely over the image, requiring an adaptive or "smart" filter whose parameters change as a function of the local statistical properties of the image. Then a pixel would be averaged only with more typical members of the same population. This annotated bibliography cites some of the work done in the area of adaptive filtering. The methods usually fall into two categories, (a) those that segment the image into subregions, each assumed to have stationary statistics, and use a different filter on each subregion, and (b) those that use a two-dimensional "sliding window" to continuously estimate the filter either the spatial or frequency domain, or may utilize both domains. They may be used to deal with images degraded by space variant noise, to suppress undesirable local radiometric statistics while enforcing desirable (user-defined) statistics, to treat problems where space-variant point spread functions are involved, to segment images into regions of constant value for classification, or to "tune" images in order to remove (nonstationary) variations in illumination, noise, contrast, shadows, or haze.Since adpative filtering, like nonadaptive filtering, is used in image processing to accomplish various goals, this bibliography

  19. Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

    Czech Academy of Sciences Publication Activity Database

    Ökzan, E.; Šmídl, Václav; Saha, S.; Lundquist, C.; Gustafsson, F.

    2013-01-01

    Roč. 49, č. 6 (2013), s. 1566-1575 ISSN 0005-1098 R&D Projects: GA ČR(CZ) GAP102/11/0437 Keywords : Unknown Noise Statistics * Adaptive Filtering * Marginalized Particle Filter * Bayesian Conjugate prior Subject RIV: BC - Control Systems Theory Impact factor: 3.132, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/smidl-0393047.pdf

  20. Dynamic data filtering system and method

    Science.gov (United States)

    Bickford, Randall L; Palnitkar, Rahul M

    2014-04-29

    A computer-implemented dynamic data filtering system and method for selectively choosing operating data of a monitored asset that modifies or expands a learned scope of an empirical model of normal operation of the monitored asset while simultaneously rejecting operating data of the monitored asset that is indicative of excessive degradation or impending failure of the monitored asset, and utilizing the selectively chosen data for adaptively recalibrating the empirical model to more accurately monitor asset aging changes or operating condition changes of the monitored asset.

  1. The role of adaptive immunity as an ecological filter on the gut microbiota in zebrafish.

    Science.gov (United States)

    Stagaman, Keaton; Burns, Adam R; Guillemin, Karen; Bohannan, Brendan Jm

    2017-07-01

    All animals live in intimate association with communities of microbes, collectively referred to as their microbiota. Certain host traits can influence which microbial taxa comprise the microbiota. One potentially important trait in vertebrate animals is the adaptive immune system, which has been hypothesized to act as an ecological filter, promoting the presence of some microbial taxa over others. Here we surveyed the intestinal microbiota of 68 wild-type zebrafish, with functional adaptive immunity, and 61 rag1 - zebrafish, lacking functional B- and T-cell receptors, to test the role of adaptive immunity as an ecological filter on the intestinal microbiota. In addition, we tested the robustness of adaptive immunity's filtering effects to host-host interaction by comparing the microbiota of fish populations segregated by genotype to those containing both genotypes. The presence of adaptive immunity individualized the gut microbiota and decreased the contributions of neutral processes to gut microbiota assembly. Although mixing genotypes led to increased phylogenetic diversity in each, there was no significant effect of adaptive immunity on gut microbiota composition in either housing condition. Interestingly, the most robust effect on microbiota composition was co-housing within a tank. In all, these results suggest that adaptive immunity has a role as an ecological filter of the zebrafish gut microbiota, but it can be overwhelmed by other factors, including transmission of microbes among hosts.

  2. Fault estimation of satellite reaction wheels using covariance based adaptive unscented Kalman filter

    Science.gov (United States)

    Rahimi, Afshin; Kumar, Krishna Dev; Alighanbari, Hekmat

    2017-05-01

    Reaction wheels, as one of the most commonly used actuators in satellite attitude control systems, are prone to malfunction which could lead to catastrophic failures. Such malfunctions can be detected and addressed in time if proper analytical redundancy algorithms such as parameter estimation and control reconfiguration are employed. Major challenges in parameter estimation include speed and accuracy of the employed algorithm. This paper presents a new approach for improving parameter estimation with adaptive unscented Kalman filter. The enhancement in tracking speed of unscented Kalman filter is achieved by systematically adapting the covariance matrix to the faulty estimates using innovation and residual sequences combined with an adaptive fault annunciation scheme. The proposed approach provides the filter with the advantage of tracking sudden changes in the system non-measurable parameters accurately. Results showed successful detection of reaction wheel malfunctions without requiring a priori knowledge about system performance in the presence of abrupt, transient, intermittent, and incipient faults. Furthermore, the proposed approach resulted in superior filter performance with less mean squared errors for residuals compared to generic and adaptive unscented Kalman filters, and thus, it can be a promising method for the development of fail-safe satellites.

  3. Adaptive spatial filtering for daytime satellite quantum key distribution

    Science.gov (United States)

    Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.

    2014-11-01

    The rate of secure key generation (SKG) in quantum key distribution (QKD) is adversely affected by optical noise and loss in the quantum channel. In a free-space atmospheric channel, the scattering of sunlight into the channel can lead to quantum bit error ratios (QBERs) sufficiently large to preclude SKG. Furthermore, atmospheric turbulence limits the degree to which spatial filtering can reduce sky noise without introducing signal losses. A system simulation quantifies the potential benefit of tracking and higher-order adaptive optics (AO) technologies to SKG rates in a daytime satellite engagement scenario. The simulations are performed assuming propagation from a low-Earth orbit (LEO) satellite to a terrestrial receiver that includes an AO system comprised of a Shack-Hartmann wave-front sensor (SHWFS) and a continuous-face-sheet deformable mirror (DM). The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain waveoptics hardware emulator. Secure key generation rates are then calculated for the decoy state QKD protocol as a function of the receiver field of view (FOV) for various pointing angles. The results show that at FOVs smaller than previously considered, AO technologies can enhance SKG rates in daylight and even enable SKG where it would otherwise be prohibited as a consequence of either background optical noise or signal loss due to turbulence effects.

  4. Adaptive particle filter for localization problem in service robotics

    Directory of Open Access Journals (Sweden)

    Heilig Alexander

    2018-01-01

    Full Text Available In this paper we present a statistical approach to the likelihood computation and adaptive resampling algorithm for particle filters using low cost ultrasonic sensors in the context of service robotics. This increases the efficiency of the particle filter in the Monte Carlo Localization problem by means of preventing sample impoverishment and ensuring it converges towards the most likely particle and simultaneously keeping less likely ones by systematic resampling. Proposed algorithms were developed in the ROS framework, simulation was done in Gazebo environment. Experiments using a differential drive mobile platform with 4 ultrasonic sensors in the office environment show that our approach provides strong improvement over particle filters with fixed sample sizes.

  5. An Affine Combination of Adaptive Filters for Channels with Different Sparsity Levels

    Directory of Open Access Journals (Sweden)

    M. Butsenko

    2016-06-01

    Full Text Available In this paper we present an affine combination strategy for two adaptive filters. One filter is designed to handle sparse impulse responses and the other one performs better if impulse response is dispersive. Filter outputs are combined using an adaptive mixing parameter and the resulting output shows a better performance than each of the combining filters separately. We also demonstrate that affine combination results in faster convergence than a convex combination of two adaptive filters.

  6. Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

    Institute of Scientific and Technical Information of China (English)

    杨海; 李威; 罗成名

    2015-01-01

    Pure inertial navigation system (INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network (WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter (KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system (FIS), and the fuzzy adaptive Kalman filter (FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

  7. Blended particle filters for large-dimensional chaotic dynamical systems

    Science.gov (United States)

    Majda, Andrew J.; Qi, Di; Sapsis, Themistoklis P.

    2014-01-01

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

  8. Model Adaptation for Prognostics in a Particle Filtering Framework

    Directory of Open Access Journals (Sweden)

    Bhaskar Saha

    2011-01-01

    Full Text Available One of the key motivating factors for using particle filters 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 particle filters 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” particle filters 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 and Li-Polymer 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 particle filter.

  9. Model Adaptation for Prognostics in a Particle Filtering Framework

    Science.gov (United States)

    Saha, Bhaskar; Goebel, Kai Frank

    2011-01-01

    One of the key motivating factors for using particle filters 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 particle filters 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" particle filters 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 particle filter.

  10. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    Soury, Hamza

    2014-01-06

    An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.

  11. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    Soury, Hamza; Abed-Meraim, Karim; Alouini, Mohamed-Slim

    2014-01-01

    An impulsive noise environment is used in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction. The minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each algorithm is discussed.

  12. Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression

    International Nuclear Information System (INIS)

    Riaz, Nadeem; Wiersma, Rodney; Mao Weihua; Xing Lei; Shanker, Piyush; Gudmundsson, Olafur; Widrow, Bernard

    2009-01-01

    Intra-fraction tumor tracking methods can improve radiation delivery during radiotherapy sessions. Image acquisition for tumor tracking and subsequent adjustment of the treatment beam with gating or beam tracking introduces time latency and necessitates predicting the future position of the tumor. This study evaluates the use of multi-dimensional linear adaptive filters and support vector regression to predict the motion of lung tumors tracked at 30 Hz. We expand on the prior work of other groups who have looked at adaptive filters by using a general framework of a multiple-input single-output (MISO) adaptive system that uses multiple correlated signals to predict the motion of a tumor. We compare the performance of these two novel methods to conventional methods like linear regression and single-input, single-output adaptive filters. At 400 ms latency the average root-mean-square-errors (RMSEs) for the 14 treatment sessions studied using no prediction, linear regression, single-output adaptive filter, MISO and support vector regression are 2.58, 1.60, 1.58, 1.71 and 1.26 mm, respectively. At 1 s, the RMSEs are 4.40, 2.61, 3.34, 2.66 and 1.93 mm, respectively. We find that support vector regression most accurately predicts the future tumor position of the methods studied and can provide a RMSE of less than 2 mm at 1 s latency. Also, a multi-dimensional adaptive filter framework provides improved performance over single-dimension adaptive filters. Work is underway to combine these two frameworks to improve performance.

  13. Notch filters for port-Hamiltonian systems

    NARCIS (Netherlands)

    Dirksz, D.A.; Scherpen, J.M.A.; van der Schaft, A.J.; Steinbuch, M.

    2012-01-01

    In this paper a standard notch filter is modeled in the port-Hamiltonian framework. By having such a port-Hamiltonian description it is proven that the notch filter is a passive system. The notch filter can then be interconnected with another (nonlinear) port-Hamiltonian system, while preserving the

  14. Improved pulsed photoacoustic detection by means of an adapted filter

    Science.gov (United States)

    González, M.; Santiago, G.; Peuriot, A.; Slezak, V.; Mosquera, C.

    2005-06-01

    We present a numerical and experimental study of two adapted filters devised to the quantitative analysis of weak photoacoustic signals. The first one is a simple convolution-type one and the other is based on neural networks of the multilayer perceptron type. The theoretical signal used as one of the inputs in both filters is derived from the solution of the transient response of the acoustic cell modeled with a simple transmission-line analogue. The filters were tested numerically by using the theoretical signal corrupted with white noise. After 500 iterations it was possible to define an average error for the returned value of each filter. Since the neural network outperformed the convolution-type, we assessed its performance by measuring SF6 traces diluted in N2 and excited by tuned TEA CO2 laser. The results show the use of the neural network filter allows recovering a signal with poor signal-to-noise ratio without resorting to extensive averaging, thus reducing the acquisition time while improving the precision of the measurement.

  15. Multi-Sensor Optimal Data Fusion Based on the Adaptive Fading Unscented Kalman Filter.

    Science.gov (United States)

    Gao, Bingbing; Hu, Gaoge; Gao, Shesheng; Zhong, Yongmin; Gu, Chengfan

    2018-02-06

    This paper presents a new optimal data fusion methodology based on the adaptive fading unscented Kalman filter for multi-sensor nonlinear stochastic systems. This methodology has a two-level fusion structure: at the bottom level, an adaptive fading unscented Kalman filter based on the Mahalanobis distance is developed and serves as local filters to improve the adaptability and robustness of local state estimations against process-modeling error; at the top level, an unscented transformation-based multi-sensor optimal data fusion for the case of N local filters is established according to the principle of linear minimum variance to calculate globally optimal state estimation by fusion of local estimations. The proposed methodology effectively refrains from the influence of process-modeling error on the fusion solution, leading to improved adaptability and robustness of data fusion for multi-sensor nonlinear stochastic systems. It also achieves globally optimal fusion results based on the principle of linear minimum variance. Simulation and experimental results demonstrate the efficacy of the proposed methodology for INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integrated navigation.

  16. Robust adaptive extended Kalman filtering for real time MR-thermometry guided HIFU interventions.

    Science.gov (United States)

    Roujol, Sébastien; de Senneville, Baudouin Denis; Hey, Silke; Moonen, Chrit; Ries, Mario

    2012-03-01

    Real time magnetic resonance (MR) thermometry is gaining clinical importance for monitoring and guiding high intensity focused ultrasound (HIFU) ablations of tumorous tissue. The temperature information can be employed to adjust the position and the power of the HIFU system in real time and to determine the therapy endpoint. The requirement to resolve both physiological motion of mobile organs and the rapid temperature variations induced by state-of-the-art high-power HIFU systems require fast MRI-acquisition schemes, which are generally hampered by low signal-to-noise ratios (SNRs). This directly limits the precision of real time MR-thermometry and thus in many cases the feasibility of sophisticated control algorithms. To overcome these limitations, temporal filtering of the temperature has been suggested in the past, which has generally an adverse impact on the accuracy and latency of the filtered data. Here, we propose a novel filter that aims to improve the precision of MR-thermometry while monitoring and adapting its impact on the accuracy. For this, an adaptive extended Kalman filter using a model describing the heat transfer for acoustic heating in biological tissues was employed together with an additional outlier rejection to address the problem of sparse artifacted temperature points. The filter was compared to an efficient matched FIR filter and outperformed the latter in all tested cases. The filter was first evaluated on simulated data and provided in the worst case (with an approximate configuration of the model) a substantial improvement of the accuracy by a factor 3 and 15 during heat up and cool down periods, respectively. The robustness of the filter was then evaluated during HIFU experiments on a phantom and in vivo in porcine kidney. The presence of strong temperature artifacts did not affect the thermal dose measurement using our filter whereas a high measurement variation of 70% was observed with the FIR filter.

  17. Adaptive multiresolution Hermite-Binomial filters for image edge and texture analysis

    NARCIS (Netherlands)

    Gu, Y.H.; Katsaggelos, A.K.

    1994-01-01

    A new multiresolution image analysis approach using adaptive Hermite-Binomial filters is presented in this paper. According to the local image structural and textural properties, the analysis filter kernels are made adaptive both in their scales and orders. Applications of such an adaptive filtering

  18. Unified Digital Periodic Signal Filters for Power Converter Systems

    DEFF Research Database (Denmark)

    Yang, Yongheng; Xin, Zhen; Zhou, Keliang

    2017-01-01

    Periodic signal controllers like repetitive and resonant controllers have demonstrated much potential in the control of power electronic converters, where periodic signals (e.g., ac voltages and currents) can be precisely regulated to follow references. Beyond the control of periodic signals, ac...... signal processing (e.g., in synchronization and pre-filtering) is also very important for power converter systems. Hence, this paper serves to unify digital periodic signal filters so as to maximize their roles in power converter systems (e.g., enhance the control of ac signals). The unified digital...... periodic signal filters behave like a comb filter, but it can also be configured to selectively filter out the harmonics of interest (e.g., the odd-order harmonics in single-phase power converter systems). Moreover, a virtual variable-sampling-frequency unit delay that enables frequency adaptive periodic...

  19. Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

    Directory of Open Access Journals (Sweden)

    Samuel Boudet

    2014-01-01

    Full Text Available Muscle artifacts constitute one of the major problems in electroencephalogram (EEG examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

  20. Adaptive anisotropic diffusion filtering of Monte Carlo dose distributions

    International Nuclear Information System (INIS)

    Miao Binhe; Jeraj, Robert; Bao Shanglian; Mackie, Thomas R

    2003-01-01

    The Monte Carlo method is the most accurate method for radiotherapy dose calculations, if used correctly. However, any Monte Carlo dose calculation is burdened with statistical noise. In this paper, denoising of Monte Carlo dose distributions with a three-dimensional adaptive anisotropic diffusion method was investigated. The standard anisotropic diffusion method was extended by changing the filtering parameters adaptively according to the local statistical noise. Smoothing of dose distributions with different noise levels in an inhomogeneous phantom, a conventional and an IMRT treatment case is shown. The resultant dose distributions were analysed using several evaluating criteria. It is shown that the adaptive anisotropic diffusion method can reduce statistical noise significantly (two to five times, corresponding to the reduction of simulation time by a factor of up to 20), while preserving important gradients of the dose distribution well. The choice of free parameters of the method was found to be fairly robust

  1. Adaptive filters and internal models: multilevel description of cerebellar function.

    Science.gov (United States)

    Porrill, John; Dean, Paul; Anderson, Sean R

    2013-11-01

    Cerebellar function is increasingly discussed in terms of engineering schemes for motor control and signal processing that involve internal models. To address the relation between the cerebellum and internal models, we adopt the chip metaphor that has been used to represent the combination of a homogeneous cerebellar cortical microcircuit with individual microzones having unique external connections. This metaphor indicates that identifying the function of a particular cerebellar chip requires knowledge of both the general microcircuit algorithm and the chip's individual connections. Here we use a popular candidate algorithm as embodied in the adaptive filter, which learns to decorrelate its inputs from a reference ('teaching', 'error') signal. This algorithm is computationally powerful enough to be used in a very wide variety of engineering applications. However, the crucial issue is whether the external connectivity required by such applications can be implemented biologically. We argue that some applications appear to be in principle biologically implausible: these include the Smith predictor and Kalman filter (for state estimation), and the feedback-error-learning scheme for adaptive inverse control. However, even for plausible schemes, such as forward models for noise cancellation and novelty-detection, and the recurrent architecture for adaptive inverse control, there is unlikely to be a simple mapping between microzone function and internal model structure. This initial analysis suggests that cerebellar involvement in particular behaviours is therefore unlikely to have a neat classification into categories such as 'forward model'. It is more likely that cerebellar microzones learn a task-specific adaptive-filter operation which combines a number of signal-processing roles. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Instantaneous Attitude Determination Based on Original Multi-antenna Observations Using Adaptively Robust Kalman Filtering

    Directory of Open Access Journals (Sweden)

    GAN Yu

    2015-09-01

    Full Text Available Attitude determination directly by carrier phase observation makes optimal use of observation and attitude constraints. The phase models based on misalignment angle and multiplicative quaternion error are derived. The state models for attitude estimation with and without external angular rate sensors are both erected. The attitude errors are estimated by adaptively robust filtering, in which the adaptive factors of ambiguity and attitude error are decided respectively following the idea of multi adaptive factor filtering. The factor of attitude is determined by a three-section function containing Ratio. Adaptively robust filtering makes the best use of constraint and historical information, fusing them in the calculation of float solution. As the accuracy of float solution and the structure of covariance matrix are improved greatly, the fix solution can be searched efficiently using LAMBDA (least-squares ambiguity decorrelation adjustment method merely, perfectly fulfilling the real-time requirement. Field test of a ship-based three-antenna attitude system is used to validate the proposed method. It is showed that direct attitude determination based on adaptively robust filtering has obvious advantages in efficiency and reliability.

  3. Filtering and control of wireless networked systems

    CERN Document Server

    Zhang, Dan; Yu, Li

    2017-01-01

    This self-contained book, written by leading experts, offers a cutting-edge, in-depth overview of the filtering and control of wireless networked systems. It addresses the energy constraint and filter/controller gain variation problems, and presents both the centralized and the distributed solutions. The first two chapters provide an introduction to networked control systems and basic information on system analysis. Chapters (3–6) then discuss the centralized filtering of wireless networked systems, presenting different approaches to deal with energy efficiency and filter/controller gain variation problems. The next part (chapters 7–10) explores the distributed filtering of wireless networked systems, addressing the main problems of energy constraint and filter gain variation. The final part (chapters 11–14) focuses on the distributed control of wireless networked systems. systems for communication and control applications, the bo...

  4. Computationally efficient video restoration for Nyquist sampled imaging sensors combining an affine-motion-based temporal Kalman filter and adaptive Wiener filter.

    Science.gov (United States)

    Rucci, Michael; Hardie, Russell C; Barnard, Kenneth J

    2014-05-01

    In this paper, we present a computationally efficient video restoration algorithm to address both blur and noise for a Nyquist sampled imaging system. The proposed method utilizes a temporal Kalman filter followed by a correlation-model based spatial adaptive Wiener filter (AWF). The Kalman filter employs an affine background motion model and novel process-noise variance estimate. We also propose and demonstrate a new multidelay temporal Kalman filter designed to more robustly treat local motion. The AWF is a spatial operation that performs deconvolution and adapts to the spatially varying residual noise left in the Kalman filter stage. In image areas where the temporal Kalman filter is able to provide significant noise reduction, the AWF can be aggressive in its deconvolution. In other areas, where less noise reduction is achieved with the Kalman filter, the AWF balances the deconvolution with spatial noise reduction. In this way, the Kalman filter and AWF work together effectively, but without the computational burden of full joint spatiotemporal processing. We also propose a novel hybrid system that combines a temporal Kalman filter and BM3D processing. To illustrate the efficacy of the proposed methods, we test the algorithms on both simulated imagery and video collected with a visible camera.

  5. Statistical-uncertainty-based adaptive filtering of lidar signals

    International Nuclear Information System (INIS)

    Fuehrer, P. L.; Friehe, C. A.; Hristov, T. S.; Cooper, D. I.; Eichinger, W. E.

    2000-01-01

    An adaptive filter signal processing technique is developed to overcome the problem of Raman lidar water-vapor mixing ratio (the ratio of the water-vapor density to the dry-air density) with a highly variable statistical uncertainty that increases with decreasing photomultiplier-tube signal strength and masks the true desired water-vapor structure. The technique, applied to horizontal scans, assumes only statistical horizontal homogeneity. The result is a variable spatial resolution water-vapor signal with a constant variance out to a range limit set by a specified signal-to-noise ratio. The technique was applied to Raman water-vapor lidar data obtained at a coastal pier site together with in situ instruments located 320 m from the lidar. The micrometerological humidity data were used to calibrate the ratio of the lidar gains of the H 2 O and the N 2 photomultiplier tubes and set the water-vapor mixing ratio variance for the adaptive filter. For the coastal experiment the effective limit of the lidar range was found to be approximately 200 m for a maximum noise-to-signal variance ratio of 0.1 with the implemented data-reduction procedure. The technique can be adapted to off-horizontal scans with a small reduction in the constraints and is also applicable to other remote-sensing devices that exhibit the same inherent range-dependent signal-to-noise ratio problem. (c) 2000 Optical Society of America

  6. Estimation, filtering and adaptative control of a waste water processing process; Estimation, filtrage et commande adaptive d`un procede de traitement des eaux usees

    Energy Technology Data Exchange (ETDEWEB)

    Ben Youssef, C; Dahhou, B; Roux, G [Centre National de la Recherche Scientifique (CNRS), 31 - Toulouse (France); Rols, J L [Institut National des Sciences Appliquees (INSA), 31 - Toulouse (France)

    1996-12-31

    Controlling the process of a fixed bed bioreactor imply solving filtering and adaptative control problems. Estimation processes have been developed for unmeasurable parameters. An adaptative non linear control has been built, instead of conventional approaches trying to linearize the system and apply a linear control system. (D.L.) 10 refs.

  7. Manipulation Robustness of Collaborative Filtering Systems

    OpenAIRE

    Benjamin Van Roy; Xiang Yan

    2009-01-01

    A collaborative filtering system recommends to users products that similar users like. Collaborative filtering systems influence purchase decisions, and hence have become targets of manipulation by unscrupulous vendors. We provide theoretical and empirical results demonstrating that while common nearest neighbor algorithms, which are widely used in commercial systems, can be highly susceptible to manipulation, two classes of collaborative filtering algorithms which we refer to as linear and a...

  8. Neural network training by Kalman filtering in process system monitoring

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1996-03-01

    Kalman filtering approach for neural network training is described. Its extended form is used as an adaptive filter in a nonlinear environment of the form a feedforward neural network. Kalman filtering approach generally provides fast training as well as avoiding excessive learning which results in enhanced generalization capability. The network is used in a process monitoring application where the inputs are measurement signals. Since the measurement errors are also modelled in Kalman filter the approach yields accurate training with the implication of accurate neural network model representing the input and output relationships in the application. As the process of concern is a dynamic system, the input source of information to neural network is time dependent so that the training algorithm presents an adaptive form for real-time operation for the monitoring task. (orig.)

  9. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon

    2013-10-01

    A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

  10. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon; Hoteit, Ibrahim; Cornuelle, Bruce D.; Luo, Xiaodong; Subramanian, Aneesh C.

    2013-01-01

    A new hybrid ensemble Kalman filter/four-dimensional variational data assimilation (EnKF/4D-VAR) approach is introduced to mitigate background covariance limitations in the EnKF. The work is based on the adaptive EnKF (AEnKF) method, which bears a strong resemblance to the hybrid EnKF/three-dimensional variational data assimilation (3D-VAR) method. In the AEnKF, the representativeness of the EnKF ensemble is regularly enhanced with new members generated after back projection of the EnKF analysis residuals to state space using a 3D-VAR [or optimal interpolation (OI)] scheme with a preselected background covariance matrix. The idea here is to reformulate the transformation of the residuals as a 4D-VAR problem, constraining the new member with model dynamics and the previous observations. This should provide more information for the estimation of the new member and reduce dependence of the AEnKF on the assumed stationary background covariance matrix. This is done by integrating the analysis residuals backward in time with the adjoint model. Numerical experiments are performed with the Lorenz-96 model under different scenarios to test the new approach and to evaluate its performance with respect to the EnKF and the hybrid EnKF/3D-VAR. The new method leads to the least root-mean-square estimation errors as long as the linear assumption guaranteeing the stability of the adjoint model holds. It is also found to be less sensitive to choices of the assimilation system inputs and parameters.

  11. Edge and line enhancement by adaptive lattice filtering

    International Nuclear Information System (INIS)

    Brolley, J.E.

    1979-01-01

    Digitized images have been two-dimensionally transformed to the Haar sequency domain. High-sequency boosting was performed and the inverse Haar two-dimensional transform applied. The resulting image was then raster-scanned with a continuously adaptive lattice filter. This procedure was applied to a simple image of a photographic step tablet and a complex scene. All of the lines of the step tablet were well defined over the whole dynamic range. Useful definition of lines in the complex scene was obtained

  12. Reduced rank adaptive filtering in impulsive noise environments

    KAUST Repository

    Soury, Hamza

    2014-11-01

    An impulsive noise environment is considered in this paper. A new aspect of signal truncation is deployed to reduce the harmful effect of the impulsive noise to the signal. A full rank direct solution is derived followed by an iterative solution. The reduced rank adaptive filter is presented in this environment by using two methods for rank reduction, while the minimized objective function is defined using the Lp norm. The results are presented and the efficiency of each method is discussed. © 2014 IEEE.

  13. Adaptation in Living Systems

    Science.gov (United States)

    Tu, Yuhai; Rappel, Wouter-Jan

    2018-03-01

    Adaptation refers to the biological phenomenon where living systems change their internal states in response to changes in their environments in order to maintain certain key functions critical for their survival and fitness. Adaptation is one of the most ubiquitous and arguably one of the most fundamental properties of living systems. It occurs throughout all biological scales, from adaptation of populations of species over evolutionary time to adaptation of a single cell to different environmental stresses during its life span. In this article, we review some of the recent progress made in understanding molecular mechanisms of cellular-level adaptation. We take the minimalist (or the physicist) approach and study the simplest systems that exhibit generic adaptive behaviors, namely chemotaxis in bacterium cells (Escherichia coli) and eukaryotic cells (Dictyostelium). We focus on understanding the basic biochemical interaction networks that are responsible for adaptation dynamics. By combining theoretical modeling with quantitative experimentation, we demonstrate universal features in adaptation as well as important differences in different cellular systems. Future work in extending the modeling framework to study adaptation in more complex systems such as sensory neurons is also discussed.

  14. Development of an adaptive bilateral filter for evaluating color image difference

    Science.gov (United States)

    Wang, Zhaohui; Hardeberg, Jon Yngve

    2012-04-01

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

  15. OPTIMIZATION OF ADVANCED FILTER SYSTEMS; TOPICAL

    International Nuclear Information System (INIS)

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

    1998-01-01

    Reliable, maintainable and cost effective hot gas particulate filter technology is critical to the successful commercialization of advanced, coal-fired power generation technologies, such as IGCC and PFBC. In pilot plant testing, the operating reliability of hot gas particulate filters have been periodically compromised by process issues, such as process upsets and difficult ash cake behavior (ash bridging and sintering), and by design issues, such as cantilevered filter elements damaged by ash bridging, or excessively close packing of filtering surfaces resulting in unacceptable pressure drop or filtering surface plugging. This test experience has focused the issues and has helped to define advanced hot gas filter design concepts that offer higher reliability. Westinghouse has identified two advanced ceramic barrier filter concepts that are configured to minimize the possibility of ash bridge formation and to be robust against ash bridges should they occur. The ''inverted candle filter system'' uses arrays of thin-walled, ceramic candle-type filter elements with inside-surface filtering, and contains the filter elements in metal enclosures for complete separation from ash bridges. The ''sheet filter system'' uses ceramic, flat plate filter elements supported from vertical pipe-header arrays that provide geometry that avoids the buildup of ash bridges and allows free fall of the back-pulse released filter cake. The Optimization of Advanced Filter Systems program is being conducted to evaluate these two advanced designs and to ultimately demonstrate one of the concepts in pilot scale. In the Base Contract program, the subject of this report, Westinghouse has developed conceptual designs of the two advanced ceramic barrier filter systems to assess their performance, availability and cost potential, and to identify technical issues that may hinder the commercialization of the technologies. A plan for the Option I, bench-scale test program has also been developed based

  16. DSP based adaptive hysteresis-band current controlled active filter ...

    African Journals Online (AJOL)

    The use of non-linear loads critically affects the quality of supply by drawing harmonic currents and reactive power from the electrical distribution system. Active power filters are the most viable solution for solving such power quality problems in compliance with the harmonic standards. This article presents a digital signal ...

  17. Mean-square performance of a convex combination of two adaptive filters

    DEFF Research Database (Denmark)

    Garcia, Jeronimo; Figueiras-Vidal, A.R.; Sayed, A.H.

    2006-01-01

    Combination approaches provide an interesting way to improve adaptive filter performance. In this paper, we study the mean-square performance of a convex combination of two transversal filters. The individual filters are independently adapted using their own error signals, while the combination i...

  18. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    Energy Technology Data Exchange (ETDEWEB)

    Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca [Department of Radiology, Stanford University, Stanford, California 94305 (United States); Department of Radiology, Stanford University, Stanford, California 94305 (United States) and Center for Medical Image Science and Visualization, Linkoeping University, Linkoeping (Sweden); Pattern Recognition Laboratory, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen (Germany); Nuclear and Radiological Engineering and Medical Physics Programs, George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332 (United States); Siemens AG Healthcare, Forchheim 91301 (Germany); Department of Radiology, Stanford University, Stanford, California 94305 (United States)

    2011-11-15

    Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8

  19. Three-dimensional anisotropic adaptive filtering of projection data for noise reduction in cone beam CT

    International Nuclear Information System (INIS)

    Maier, Andreas; Wigstroem, Lars; Hofmann, Hannes G.; Hornegger, Joachim; Zhu Lei; Strobel, Norbert; Fahrig, Rebecca

    2011-01-01

    Purpose: The combination of quickly rotating C-arm gantry with digital flat panel has enabled the acquisition of three-dimensional data (3D) in the interventional suite. However, image quality is still somewhat limited since the hardware has not been optimized for CT imaging. Adaptive anisotropic filtering has the ability to improve image quality by reducing the noise level and therewith the radiation dose without introducing noticeable blurring. By applying the filtering prior to 3D reconstruction, noise-induced streak artifacts are reduced as compared to processing in the image domain. Methods: 3D anisotropic adaptive filtering was used to process an ensemble of 2D x-ray views acquired along a circular trajectory around an object. After arranging the input data into a 3D space (2D projections + angle), the orientation of structures was estimated using a set of differently oriented filters. The resulting tensor representation of local orientation was utilized to control the anisotropic filtering. Low-pass filtering is applied only along structures to maintain high spatial frequency components perpendicular to these. The evaluation of the proposed algorithm includes numerical simulations, phantom experiments, and in-vivo data which were acquired using an AXIOM Artis dTA C-arm system (Siemens AG, Healthcare Sector, Forchheim, Germany). Spatial resolution and noise levels were compared with and without adaptive filtering. A human observer study was carried out to evaluate low-contrast detectability. Results: The adaptive anisotropic filtering algorithm was found to significantly improve low-contrast detectability by reducing the noise level by half (reduction of the standard deviation in certain areas from 74 to 30 HU). Virtually no degradation of high contrast spatial resolution was observed in the modulation transfer function (MTF) analysis. Although the algorithm is computationally intensive, hardware acceleration using Nvidia's CUDA Interface provided an 8.9-fold

  20. Microseismic Event Location Improvement Using Adaptive Filtering for Noise Attenuation

    Science.gov (United States)

    de Santana, F. L., Sr.; do Nascimento, A. F.; Leandro, W. P. D. N., Sr.; de Carvalho, B. M., Sr.

    2017-12-01

    In this work we show how adaptive filtering noise suppression improves the effectiveness of the Source Scanning Algorithm (SSA; Kao & Shan, 2004) in microseism location in the context of fracking operations. The SSA discretizes the time and region of interest in a 4D vector and, for each grid point and origin time, a brigthness value (seismogram stacking) is calculated. For a given set of velocity model parameters, when origin time and hypocenter of the seismic event are correct, a maximum value for coherence (or brightness) is achieved. The result is displayed on brightness maps for each origin time. Location methods such as SSA are most effective when the noise present in the seismograms is incoherent, however, the method may present false positives when the noise present in the data is coherent as occurs in fracking operations. To remove from the seismograms, the coherent noise from the pump and engines used in the operation, we use an adaptive filter. As the noise reference, we use the seismogram recorded at the station closest to the machinery employed. Our methodology was tested on semi-synthetic data. The microseismic was represented by Ricker pulses (with central frequency of 30Hz) on synthetics seismograms, and to simulate real seismograms on a surface microseismic monitoring situation, we added real noise recorded in a fracking operation to these synthetics seismograms. The results show that after the filtering of the seismograms, we were able to improve our detection threshold and to achieve a better resolution on the brightness maps of the located events.

  1. Power Line Interference Removal from Electrocardiogram Using a Simplified Lattice Based Adaptive IIR Notch Filter

    National Research Council Canada - National Science Library

    Dhillon, Santpal

    2001-01-01

    ...) notch filter with a simplified adaptation algorithm for removal of power line frequency from ECG signals, The performance of this filter is better as compared to a second order infinite impulse response (IIR...

  2. Turbine system and adapter

    Science.gov (United States)

    Hogberg, Nicholas Alvin; Garcia-Crespo, Andres Jose

    2017-05-30

    A turbine system and adapter are disclosed. The adapter includes a turbine attachment portion having a first geometry arranged to receive a corresponding geometry of a wheelpost of a turbine rotor, and a bucket attachment portion having a second geometry arranged to receive a corresponding geometry of a root portion of a non-metallic turbine bucket. Another adapter includes a turbine attachment portion arranged to receive a plurality of wheelposts of a turbine rotor, and a bucket attachment portion arranged to receive a plurality of non-metallic turbine buckets having single dovetail configuration root portions. The turbine system includes a turbine rotor wheel configured to receive metal buckets, at least one adapter secured to at least one wheelpost on the turbine rotor wheel, and at least one non-metallic bucket secured to the at least one adapter.

  3. Adaptive Command Filtered Integrated Guidance and Control for Hypersonic Vehicle with Magnitude, Rate and Bandwidth Constraints

    Directory of Open Access Journals (Sweden)

    Wang Liang

    2018-01-01

    Full Text Available This paper proposes a novel integrated guidance and control (IGC method for hypersonic vehicle in terminal phase. Firstly, the system model is developed with a second order actuator dynamics. Then the back-stepping controller is designed hierarchically with command filters, where the first order command filters are implemented to construct the virtual control input with ideal states predicted by an adaptive estimator, and the nonlinear command filter is designed to produce magnitude, rate and bandwidth limited control surface deflection finally tracked by a terminal sliding mode controller with finite convergence time. Through a series of 6-DOF numerical simulations, it’s indicated that the proposed method successfully cancels out the large aerodynamics coefficient uncertainties and disturbances in hypersonic flight under limited control surface deflection. The contribution of this paper lies in the application and determination of nonlinear integrated design of guidance and control system for hypersonic vehicle.

  4. Adaptivna digitalna sita v strukturi porazdeljene aritmetike: Adaptive digital filter implementation with distributed arithmetic structure:

    OpenAIRE

    Babič, Rudolf; Horvat, Bogomir; Osebik, Davorin

    2001-01-01

    Adaptive digital filters have a wide range of applications in the area of signal processing where only minimum a priori knowledge of signal characteristics is available. In this article the adaptive FIR digital filter implementation based on the distributed arithmetic technique is described. The major problem with conventional adaptive digital filter is the need for fast multipliers. When using a hardware implementation. These multipliers take up the disproportional amount of the overall cost...

  5. Actuator Fault Diagnosis in a Boeing 747 Model via Adaptive Modified Two-Stage Kalman Filter

    Directory of Open Access Journals (Sweden)

    Fikret Caliskan

    2014-01-01

    Full Text Available An adaptive modified two-stage linear Kalman filtering algorithm is utilized to identify the loss of control effectiveness and the magnitude of low degree of stuck faults in a closed-loop nonlinear B747 aircraft. Control effectiveness factors and stuck magnitudes are used to quantify faults entering control systems through actuators. Pseudorandom excitation inputs are used to help distinguish partial loss and stuck faults. The partial loss and stuck faults in the stabilizer are isolated and identified successfully.

  6. Rotational Kinematics Model Based Adaptive Particle Filter for Robust Human Tracking in Thermal Omnidirectional Vision

    Directory of Open Access Journals (Sweden)

    Yazhe Tang

    2015-01-01

    Full Text Available This paper presents a novel surveillance system named thermal omnidirectional vision (TOV system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.

  7. Theory of affine projection algorithms for adaptive filtering

    CERN Document Server

    Ozeki, Kazuhiko

    2016-01-01

    This book focuses on theoretical aspects of the affine projection algorithm (APA) for adaptive filtering. The APA is a natural generalization of the classical, normalized least-mean-squares (NLMS) algorithm. The book first explains how the APA evolved from the NLMS algorithm, where an affine projection view is emphasized. By looking at those adaptation algorithms from such a geometrical point of view, we can find many of the important properties of the APA, e.g., the improvement of the convergence rate over the NLMS algorithm especially for correlated input signals. After the birth of the APA in the mid-1980s, similar algorithms were put forward by other researchers independently from different perspectives. This book shows that they are variants of the APA, forming a family of APAs. Then it surveys research on the convergence behavior of the APA, where statistical analyses play important roles. It also reviews developments of techniques to reduce the computational complexity of the APA, which are important f...

  8. Adaptive Kalman filter based state of charge estimation algorithm for lithium-ion battery

    International Nuclear Information System (INIS)

    Zheng Hong; Liu Xu; Wei Min

    2015-01-01

    In order to improve the accuracy of the battery state of charge (SOC) estimation, in this paper we take a lithium-ion battery as an example to study the adaptive Kalman filter based SOC estimation algorithm. Firstly, the second-order battery system model is introduced. Meanwhile, the temperature and charge rate are introduced into the model. Then, the temperature and the charge rate are adopted to estimate the battery SOC, with the help of the parameters of an adaptive Kalman filter based estimation algorithm model. Afterwards, it is verified by the numerical simulation that in the ideal case, the accuracy of SOC estimation can be enhanced by adding two elements, namely, the temperature and charge rate. Finally, the actual road conditions are simulated with ADVISOR, and the simulation results show that the proposed method improves the accuracy of battery SOC estimation under actual road conditions. Thus, its application scope in engineering is greatly expanded. (paper)

  9. Development of Shunt-Type Three-Phase Active Power Filter with Novel Adaptive Control for Wind Generators

    Directory of Open Access Journals (Sweden)

    Ming-Hung Chen

    2015-01-01

    Full Text Available This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.

  10. Development of Shunt-Type Three-Phase Active Power Filter with Novel Adaptive Control for Wind Generators.

    Science.gov (United States)

    Chen, Ming-Hung

    2015-01-01

    This paper proposes a new adaptive filter for wind generators that combines instantaneous reactive power compensation technology and current prediction controller, and therefore this system is characterized by low harmonic distortion, high power factor, and small DC-link voltage variations during load disturbances. The performance of the system was first simulated using MATLAB/Simulink, and the possibility of an adaptive digital low-pass filter eliminating current harmonics was confirmed in steady and transient states. Subsequently, a digital signal processor was used to implement an active power filter. The experimental results indicate, that for the rated operation of 2 kVA, the system has a total harmonic distortion of current less than 5.0% and a power factor of 1.0 on the utility side. Thus, the transient performance of the adaptive filter is superior to the traditional digital low-pass filter and is more economical because of its short computation time compared with other types of adaptive filters.

  11. Adaptive Sliding Mode Observer for a Class of Systems

    OpenAIRE

    D.Elleuch; T.Damak

    2010-01-01

    In this paper, the performance of two adaptive observers applied to interconnected systems is studied. The nonlinearity of systems can be written in a fractional form. The first adaptive observer is an adaptive sliding mode observer for a Lipchitz nonlinear system and the second one is an adaptive sliding mode observer having a filtered error as a sliding surface. After comparing their performances throughout the inverted pendulum mounted on a car system, it was shown tha...

  12. Adaptive intrusion data system

    International Nuclear Information System (INIS)

    Johnson, C.S.

    1976-01-01

    An Adaptive Intrusion Data System (AIDS) was developed to collect data from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique digital data compression, storage, and formatting system. It also incorporates capability for video selection and recording for assessment of the sensors monitored by the system. The system is software reprogrammable to numerous configurations that may be utilized for the collection of environmental, bi-level, analog and video data. The output of the system is digital tapes formatted for direct data reduction on a CDC 6400 computer, and video tapes containing timed tagged information that can be correlated with the digital data

  13. Testing Of The Dual Rotary Filter System

    International Nuclear Information System (INIS)

    Herman, D.; Fowley, M.; Stefanko, D.

    2011-01-01

    The Savannah River National Laboratory (SRNL) installed and tested two hydraulically connected SpinTek rotary microfilter (RMF) units to determine the behavior of a multiple filter system. Both units were successfully controlled by a control scheme written in DELTA-V architecture by Savannah River Remediation (SRR) Process Control Engineering personnel. The control system was tuned to provide satisfactory response to changing conditions during the operation of the multi-filter system. Stability was maintained through the startup and shutdown of one of the filter units while the second was still in operation. The installation configuration originally proposed by the Small Colum Ion Exchange (SCIX) project of independent filter and motor mountings may be susceptible to vibration. Significant stiffening of the filter and motor mounts was required to minimize the vibration. Alignment of the motor to the filter was a challenge in this test configuration. The deployment configuration must be easy to manipulate and allow for fine adjustment. An analysis of the vibration signature of the test system identified critical speeds. Whether it corresponds to the resonance frequency of a rotor radial vibration mode that was excited by rotor unbalance is uncertain based upon the measurements. A relative motion series should be completed on the filter with the final shaft configuration to determine if the resonances exist in the final filter design. The instrumentation selected for deployment, including the concentrate discharge control valve and flow meters, performed well. Automation of the valve control integrated well with the control scheme and when used in concert with the other control variables, allowed automated control of the dual RMF system. The one area of concern with the instrumentation was the condition resulting when the filtrate flow meter operated with less than three gpm. This low flow was at the lower range of performance for the flow meter. This should not be

  14. TESTING OF THE DUAL ROTARY FILTER SYSTEM

    Energy Technology Data Exchange (ETDEWEB)

    Herman, D.; Fowley, M.; Stefanko, D.

    2011-08-29

    The Savannah River National Laboratory (SRNL) installed and tested two hydraulically connected SpinTek rotary microfilter (RMF) units to determine the behavior of a multiple filter system. Both units were successfully controlled by a control scheme written in DELTA-V architecture by Savannah River Remediation (SRR) Process Control Engineering personnel. The control system was tuned to provide satisfactory response to changing conditions during the operation of the multi-filter system. Stability was maintained through the startup and shutdown of one of the filter units while the second was still in operation. The installation configuration originally proposed by the Small Colum Ion Exchange (SCIX) project of independent filter and motor mountings may be susceptible to vibration. Significant stiffening of the filter and motor mounts was required to minimize the vibration. Alignment of the motor to the filter was a challenge in this test configuration. The deployment configuration must be easy to manipulate and allow for fine adjustment. An analysis of the vibration signature of the test system identified critical speeds. Whether it corresponds to the resonance frequency of a rotor radial vibration mode that was excited by rotor unbalance is uncertain based upon the measurements. A relative motion series should be completed on the filter with the final shaft configuration to determine if the resonances exist in the final filter design. The instrumentation selected for deployment, including the concentrate discharge control valve and flow meters, performed well. Automation of the valve control integrated well with the control scheme and when used in concert with the other control variables, allowed automated control of the dual RMF system. The one area of concern with the instrumentation was the condition resulting when the filtrate flow meter operated with less than three gpm. This low flow was at the lower range of performance for the flow meter. This should not be

  15. Locally-adaptive Myriad Filters for Processing ECG Signals in Real Time

    Directory of Open Access Journals (Sweden)

    Nataliya Tulyakova

    2017-03-01

    Full Text Available The locally adaptive myriad filters to suppress noise in electrocardiographic (ECG signals in almost in real time are proposed. Statistical estimates of efficiency according to integral values of such criteria as mean square error (MSE and signal-to-noise ratio (SNR for the test ECG signals sampled at 400 Hz embedded in additive Gaussian noise with different values of variance are obtained. Comparative analysis of adaptive filters is carried out. High efficiency of ECG filtering and high quality of signal preservation are demonstrated. It is shown that locally adaptive myriad filters provide higher degree of suppressing additive Gaussian noise with possibility of real time implementation.

  16. Modeling astronomical adaptive optics performance with temporally filtered Wiener reconstruction of slope data

    Science.gov (United States)

    Correia, Carlos M.; Bond, Charlotte Z.; Sauvage, Jean-François; Fusco, Thierry; Conan, Rodolphe; Wizinowich, Peter L.

    2017-10-01

    We build on a long-standing tradition in astronomical adaptive optics (AO) of specifying performance metrics and error budgets using linear systems modeling in the spatial-frequency domain. Our goal is to provide a comprehensive tool for the calculation of error budgets in terms of residual temporally filtered phase power spectral densities and variances. In addition, the fast simulation of AO-corrected point spread functions (PSFs) provided by this method can be used as inputs for simulations of science observations with next-generation instruments and telescopes, in particular to predict post-coronagraphic contrast improvements for planet finder systems. We extend the previous results and propose the synthesis of a distributed Kalman filter to mitigate both aniso-servo-lag and aliasing errors whilst minimizing the overall residual variance. We discuss applications to (i) analytic AO-corrected PSF modeling in the spatial-frequency domain, (ii) post-coronagraphic contrast enhancement, (iii) filter optimization for real-time wavefront reconstruction, and (iv) PSF reconstruction from system telemetry. Under perfect knowledge of wind velocities, we show that $\\sim$60 nm rms error reduction can be achieved with the distributed Kalman filter embodying anti- aliasing reconstructors on 10 m class high-order AO systems, leading to contrast improvement factors of up to three orders of magnitude at few ${\\lambda}/D$ separations ($\\sim1-5{\\lambda}/D$) for a 0 magnitude star and reaching close to one order of magnitude for a 12 magnitude star.

  17. Two-antenna GNSS Aided-INS Alignment Using Adaptive Control of Filter Noise Covariance

    Directory of Open Access Journals (Sweden)

    HAO Yushi

    2018-04-01

    Full Text Available This paper developed a theory of INS fine alignment in order to restrain the divergence of yaw angle,two antennas GNSS aided-INS integrated alignment algorithm was utilized.An attitude error measurement equation was conducted based on the relationship between baseline vectors calculated by two sensors and attitude error.The algorithm was executed by EKF using adaptive control of filter noise covariance.The experimental results showed that stability of the integrated system was improved under the system noise covariance adaptive control mechanism;The measurement noise covariance adaptive control mechanism can reduce the influence of measurement noise and improve the alignment absolute accuracy;Further improvement was achieved under the condition of minim bias of baseline length.The accuracy of roll and pitch was 0.02°,the accuracy of yaw was 0.04°.

  18. Preprocessing of PHERMEX flash radiographic images with Haar and adaptive filtering

    International Nuclear Information System (INIS)

    Brolley, J.E.

    1978-11-01

    Work on image preparation has continued with the application of high-sequency boosting via Haar filtering. This is useful in developing line or edge structures. Widrow LMS adaptive filtering has also been shown to be useful in developing edge structure in special problems. Shadow effects can be obtained with the latter which may be useful for some problems. Combined Haar and adaptive filtering is illustrated for a PHERMEX image

  19. Wiener filter applied to a neutrongraphic system

    International Nuclear Information System (INIS)

    Crispim, V.R.; Lopes, R.T.; Borges, J.C.

    1986-01-01

    The randon characteristics of the image formation process influence the spatial image obtained in a neutrongraphy. Several methods can be used to optimize this image, though estimation of the noise added to the original signal. This work deals with the optimal filtering technique, using Wiener's filter. A simulation is made, where the signal (spatial resolution function) has a Lorentz's form, and ten kinds of random noise with increasing R.M.S. are generated and individually added to the original signal. Wiener's filter is applied to different noise amplitudes and the behaviour of the spatial resolution function for our system is also analysed. (Author) [pt

  20. Adaptive kernels in approximate filtering of state-space models

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil

    2017-01-01

    Roč. 31, č. 6 (2017), s. 938-952 ISSN 0890-6327 R&D Projects: GA ČR(CZ) GP14-06678P Institutional support: RVO:67985556 Keywords : filtering * nonlinear filters * Bayesian filtering * sequential Monte Carlo * approximate filtering Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Statistics and probability Impact factor: 1.708, year: 2016 http://library.utia.cs.cz/separaty/2016/AS/dedecius-0466448.pdf

  1. Tap-length optimization of adaptive filters used in stereophonic acoustic echo cancellation

    DEFF Research Database (Denmark)

    Kar, Asutosh; Swamy, M.N.S.

    2017-01-01

    An adaptive filter with a large number of weights or taps is necessary for stereophonic acoustic echo cancellation (SAEC), depending on the room impulse response and acoustic path where the cancellation is performed. However, a large tap-length results in slow convergence and increases...... the complexity of the tapped delay line structure for FIR adaptive filters. To overcome this problem, there is a need for an optimum tap-length-estimation algorithm that provides better convergence for the adaptive filters used in SAEC. This paper presents a solution to the problem of balancing convergence...... and steady-state performance of long length adaptive filters used for SAEC by proposing a new tap-length-optimization algorithm. The optimum tap length and step size of the adaptive filter are derived considering an impulse response with an exponentially-decaying envelope, which models a wide range...

  2. Research on the Random Shock Vibration Test Based on the Filter-X LMS Adaptive Inverse Control Algorithm

    Directory of Open Access Journals (Sweden)

    Wang Wei

    2016-01-01

    Full Text Available The related theory and algorithm of adaptive inverse control were presented through the research which pointed out the adaptive inverse control strategy could effectively eliminate the noise influence on the system control. Proposed using a frequency domain filter-X LMS adaptive inverse control algorithm, and the control algorithm was applied to the two-exciter hydraulic vibration test system of random shock vibration control process and summarized the process of the adaptive inverse control strategies in the realization of the random shock vibration test. The self-closed-loop and field test show that using the frequency-domain filter-X LMS adaptive inverse control algorithm can realize high precision control of random shock vibration test.

  3. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    Kammoun, Abla

    2016-09-13

    This paper focuses on the problem of detecting a target in the presence of a compound Gaussian clutter with unknown statistics. To this end, we focus on the design of the adaptive normalized matched filter (ANMF) detector which uses the regularized Tyler estimator (RTE) built from N-dimensional observations x, · · ·, x in order to estimate the clutter covariance matrix. The choice for the RTE is motivated by its possessing two major attributes: first its resilience to the presence of outliers, and second its regularization parameter that makes it more suitable to handle the scarcity in observations. In order to facilitate the design of the ANMF detector, we consider the regime in which n and N are both large. This allows us to derive closed-form expressions for the asymptotic false alarm and detection probabilities. Based on these expressions, we propose an asymptotically optimal setting for the regularization parameter of the RTE that maximizes the asymptotic detection probability while keeping the asymptotic false alarm probability below a certain threshold. Numerical results are provided in order to illustrate the gain of the proposed detector over a recently proposed setting of the regularization parameter.

  4. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Fré dé ric; Alouini, Mohamed-Slim

    2016-01-01

    This paper focuses on the problem of detecting a target in the presence of a compound Gaussian clutter with unknown statistics. To this end, we focus on the design of the adaptive normalized matched filter (ANMF) detector which uses the regularized Tyler estimator (RTE) built from N-dimensional observations x, · · ·, x in order to estimate the clutter covariance matrix. The choice for the RTE is motivated by its possessing two major attributes: first its resilience to the presence of outliers, and second its regularization parameter that makes it more suitable to handle the scarcity in observations. In order to facilitate the design of the ANMF detector, we consider the regime in which n and N are both large. This allows us to derive closed-form expressions for the asymptotic false alarm and detection probabilities. Based on these expressions, we propose an asymptotically optimal setting for the regularization parameter of the RTE that maximizes the asymptotic detection probability while keeping the asymptotic false alarm probability below a certain threshold. Numerical results are provided in order to illustrate the gain of the proposed detector over a recently proposed setting of the regularization parameter.

  5. 两种渐消滤波与自适应抗差滤波的综合比较分析%Comparison of Two Fading Filters and Adaptively Robust Filter

    Institute of Scientific and Technical Information of China (English)

    杨元喜; 高为广

    2007-01-01

    Two kinds of fading filters and their principles are introduced. An adaptive robust filter is given with corresponding principle. The basic abilities of the fading filters and adaptively robust filter in controlling the influences of the kinematic model errors are analyzed. A practical example is given. The results of the fading filter and adaptively robust filter are compared and analyzed.

  6. Superconducting Magnetometry for Cardiovascular Studies and AN Application of Adaptive Filtering.

    Science.gov (United States)

    Leifer, Mark Curtis

    Sensitive magnetic detectors utilizing Superconducting Quantum Interference Devices (SQUID's) have been developed and used for studying the cardiovascular system. The theory of magnetic detection of cardiac currents is discussed, and new experimental data supporting the validity of the theory is presented. Measurements on both humans and dogs, in both healthy and diseased states, are presented using the new technique, which is termed vector magnetocardiography. In the next section, a new type of superconducting magnetometer with a room temperature pickup is analyzed, and techniques for optimizing its sensitivity to low-frequency sub-microamp currents are presented. Performance of the actual device displays significantly improved sensitivity in this frequency range, and the ability to measure currents in intact, in vivo biological fibers. The final section reviews the theoretical operation of a digital self-optimizing filter, and presents a four-channel software implementation of the system. The application of the adaptive filter to enhancement of geomagnetic signals for earthquake forecasting is discussed, and the adaptive filter is shown to outperform existing techniques in suppressing noise from geomagnetic records.

  7. Performance enhancement for a GPS vector-tracking loop utilizing an adaptive iterated extended Kalman filter.

    Science.gov (United States)

    Chen, Xiyuan; Wang, Xiying; Xu, Yuan

    2014-12-09

    This paper deals with the problem of state estimation for the vector-tracking loop of a software-defined Global Positioning System (GPS) receiver. For a nonlinear system that has the model error and white Gaussian noise, a noise statistics estimator is used to estimate the model error, and based on this, a modified iterated extended Kalman filter (IEKF) named adaptive iterated Kalman filter (AIEKF) is proposed. A vector-tracking GPS receiver utilizing AIEKF is implemented to evaluate the performance of the proposed method. Through road tests, it is shown that the proposed method has an obvious accuracy advantage over the IEKF and Adaptive Extended Kalman filter (AEKF) in position determination. The results show that the proposed method is effective to reduce the root-mean-square error (RMSE) of position (including longitude, latitude and altitude). Comparing with EKF, the position RMSE values of AIEKF are reduced by about 45.1%, 40.9% and 54.6% in the east, north and up directions, respectively. Comparing with IEKF, the position RMSE values of AIEKF are reduced by about 25.7%, 19.3% and 35.7% in the east, north and up directions, respectively. Compared with AEKF, the position RMSE values of AIEKF are reduced by about 21.6%, 15.5% and 30.7% in the east, north and up directions, respectively.

  8. Adaptive mean filtering for noise reduction in CT polymer gel dosimetry

    International Nuclear Information System (INIS)

    Hilts, Michelle; Jirasek, Andrew

    2008-01-01

    X-ray computed tomography (CT) as a method of extracting 3D dose information from irradiated polymer gel dosimeters is showing potential as a practical means to implement gel dosimetry in a radiation therapy clinic. However, the response of CT contrast to dose is weak and noise reduction is critical in order to achieve adequate dose resolutions with this method. Phantom design and CT imaging technique have both been shown to decrease image noise. In addition, image postprocessing using noise reduction filtering techniques have been proposed. This work evaluates in detail the use of the adaptive mean filter for reducing noise in CT gel dosimetry. Filter performance is systematically tested using both synthetic patterns mimicking a range of clinical dose distribution features as well as actual clinical dose distributions. Both low and high signal-to-noise ratio (SNR) situations are examined. For all cases, the effects of filter kernel size and the number of iterations are investigated. Results indicate that adaptive mean filtering is a highly effective tool for noise reduction CT gel dosimetry. The optimum filtering strategy depends on characteristics of the dose distributions and image noise level. For low noise images (SNR ∼20), the filtered results are excellent and use of adaptive mean filtering is recommended as a standard processing tool. For high noise images (SNR ∼5) adaptive mean filtering can also produce excellent results, but filtering must be approached with more caution as spatial and dose distortions of the original dose distribution can occur

  9. A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter

    International Nuclear Information System (INIS)

    Ye, Min; Guo, Hui; Cao, Binggang

    2017-01-01

    Highlights: • Propose an improved adaptive particle swarm filter method. • The SoC estimation method for the battery based on the adaptive particle swarm filter is presented. • The algorithm is validated by the case study of different aged extent batteries. • The effectiveness and applicability of the algorithm are validated by the LiPB batteries. - Abstract: Obtaining accurate parameters, state of charge (SoC) and capacity of a lithium-ion battery is crucial for a battery management system, and establishing a battery model online is complex. In addition, the errors and perturbations of the battery model dramatically increase throughout the battery lifetime, making it more challenging to model the battery online. To overcome these difficulties, this paper provides three contributions: (1) To improve the robustness of the adaptive particle filter algorithm, an error analysis method is added to the traditional adaptive particle swarm algorithm. (2) An online adaptive SoC estimator based on the improved adaptive particle filter is presented; this estimator can eliminate the estimation error due to battery degradation and initial SoC errors. (3) The effectiveness of the proposed method is verified using various initial states of lithium nickel manganese cobalt oxide (NMC) cells and lithium-ion polymer (LiPB) batteries. The experimental analysis shows that the maximum errors are less than 1% for both the voltage and SoC estimations and that the convergence time of the SoC estimation decreased to 120 s.

  10. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter.

    Science.gov (United States)

    Chu, Hairong; Sun, Tingting; Zhang, Baiqiang; Zhang, Hongwei; Chen, Yang

    2017-01-14

    In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF) algorithm is proposed. First, the model of SINS transfer alignment is defined based on the "Velocity and Attitude" matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.

  11. Rapid Transfer Alignment of MEMS SINS Based on Adaptive Incremental Kalman Filter

    Directory of Open Access Journals (Sweden)

    Hairong Chu

    2017-01-01

    Full Text Available In airborne MEMS SINS transfer alignment, the error of MEMS IMU is highly environment-dependent and the parameters of the system model are also uncertain, which may lead to large error and bad convergence of the Kalman filter. In order to solve this problem, an improved adaptive incremental Kalman filter (AIKF algorithm is proposed. First, the model of SINS transfer alignment is defined based on the “Velocity and Attitude” matching method. Then the detailed algorithm progress of AIKF and its recurrence formulas are presented. The performance and calculation amount of AKF and AIKF are also compared. Finally, a simulation test is designed to verify the accuracy and the rapidity of the AIKF algorithm by comparing it with KF and AKF. The results show that the AIKF algorithm has better estimation accuracy and shorter convergence time, especially for the bias of the gyroscope and the accelerometer, which can meet the accuracy and rapidity requirement of transfer alignment.

  12. Performance and stochastic stability of the adaptive fading extended Kalman filter with the matrix forgetting factor

    Directory of Open Access Journals (Sweden)

    Biçer Cenker

    2016-01-01

    Full Text Available In this paper, the stability of the adaptive fading extended Kalman filter with the matrix forgetting factor when applied to the state estimation problem with noise terms in the non–linear discrete–time stochastic systems has been analysed. The analysis is conducted in a similar manner to the standard extended Kalman filter’s stability analysis based on stochastic framework. The theoretical results show that under certain conditions on the initial estimation error and the noise terms, the estimation error remains bounded and the state estimation is stable.

  13. Complex adaptive systems ecology

    DEFF Research Database (Denmark)

    Sommerlund, Julie

    2003-01-01

    In the following, I will analyze two articles called Complex Adaptive Systems EcologyI & II (Molin & Molin, 1997 & 2000). The CASE-articles are some of the more quirkyarticles that have come out of the Molecular Microbial Ecology Group - a groupwhere I am currently making observational studies....... They are the result of acooperation between Søren Molin, professor in the group, and his brother, JanMolin, professor at Department of Organization and Industrial Sociology atCopenhagen Business School. The cooperation arises from the recognition that bothmicrobial ecology and sociology/organization theory works...

  14. Adaptive oriented PDEs filtering methods based on new controlling speed function for discontinuous optical fringe patterns

    Science.gov (United States)

    Zhou, Qiuling; Tang, Chen; Li, Biyuan; Wang, Linlin; Lei, Zhenkun; Tang, Shuwei

    2018-01-01

    The filtering of discontinuous optical fringe patterns is a challenging problem faced in this area. This paper is concerned with oriented partial differential equations (OPDEs)-based image filtering methods for discontinuous optical fringe patterns. We redefine a new controlling speed function to depend on the orientation coherence. The orientation coherence can be used to distinguish the continuous regions and the discontinuous regions, and can be calculated by utilizing fringe orientation. We introduce the new controlling speed function to the previous OPDEs and propose adaptive OPDEs filtering models. According to our proposed adaptive OPDEs filtering models, the filtering in the continuous and discontinuous regions can be selectively carried out. We demonstrate the performance of the proposed adaptive OPDEs via application to the simulated and experimental fringe patterns, and compare our methods with the previous OPDEs.

  15. A biological inspired fuzzy adaptive window median filter (FAWMF) for enhancing DNA signal processing.

    Science.gov (United States)

    Ahmad, Muneer; Jung, Low Tan; Bhuiyan, Al-Amin

    2017-10-01

    Digital signal processing techniques commonly employ fixed length window filters to process the signal contents. DNA signals differ in characteristics from common digital signals since they carry nucleotides as contents. The nucleotides own genetic code context and fuzzy behaviors due to their special structure and order in DNA strand. Employing conventional fixed length window filters for DNA signal processing produce spectral leakage and hence results in signal noise. A biological context aware adaptive window filter is required to process the DNA signals. This paper introduces a biological inspired fuzzy adaptive window median filter (FAWMF) which computes the fuzzy membership strength of nucleotides in each slide of window and filters nucleotides based on median filtering with a combination of s-shaped and z-shaped filters. Since coding regions cause 3-base periodicity by an unbalanced nucleotides' distribution producing a relatively high bias for nucleotides' usage, such fundamental characteristic of nucleotides has been exploited in FAWMF to suppress the signal noise. Along with adaptive response of FAWMF, a strong correlation between median nucleotides and the Π shaped filter was observed which produced enhanced discrimination between coding and non-coding regions contrary to fixed length conventional window filters. The proposed FAWMF attains a significant enhancement in coding regions identification i.e. 40% to 125% as compared to other conventional window filters tested over more than 250 benchmarked and randomly taken DNA datasets of different organisms. This study proves that conventional fixed length window filters applied to DNA signals do not achieve significant results since the nucleotides carry genetic code context. The proposed FAWMF algorithm is adaptive and outperforms significantly to process DNA signal contents. The algorithm applied to variety of DNA datasets produced noteworthy discrimination between coding and non-coding regions contrary

  16. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    Bibi, Adel Aamer

    2015-12-07

    This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

  17. Model Adaptation for Prognostics in a Particle Filtering Framework

    Data.gov (United States)

    National Aeronautics and Space Administration — One of the key motivating factors for using particle filters for prognostics is the ability to include model parameters as part of the state vector to be estimated....

  18. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2015-01-01

    This paper identifies the major drawbacks of a very computationally efficient and state-of-the-art-tracker known as the Kernelized Correlation Filter (KCF) tracker. These drawbacks include an assumed fixed scale of the target in every frame, as well as, a heuristic update strategy of the filter taps to incorporate historical tracking information (i.e. simple linear combination of taps from the previous frame). In our approach, we update the scale of the tracker by maximizing over the posterior distribution of a grid of scales. As for the filter update, we prove and show that it is possible to use all previous training examples to update the filter taps very efficiently using fixed-point optimization. We validate the efficacy of our approach on two tracking datasets, VOT2014 and VOT2015.

  19. A pilot study on slit lamp-adapted optical coherence tomography imaging of trabeculectomy filtering blebs.

    NARCIS (Netherlands)

    Theelen, T.; Wesseling, P.; Keunen, J.E.E.; Klevering, B.J.

    2007-01-01

    BACKGROUND: Our study aims to identify anatomical characteristics of glaucoma filtering blebs by means of slit lamp-adapted optical coherence tomography (SL-OCT) and to identify new parameters for the functional prognosis of the filter in the early post-operative period. METHODS: Patients with

  20. Robust filtering for uncertain systems a parameter-dependent approach

    CERN Document Server

    Gao, Huijun

    2014-01-01

    This monograph provides the reader with a systematic treatment of robust filter design, a key issue in systems, control and signal processing, because of the fact that the inevitable presence of uncertainty in system and signal models often degrades the filtering performance and may even cause instability. The methods described are therefore not subject to the rigorous assumptions of traditional Kalman filtering. The monograph is concerned with robust filtering for various dynamical systems with parametric uncertainties, and focuses on parameter-dependent approaches to filter design. Classical filtering schemes, like H2 filtering and H¥ filtering, are addressed, and emerging issues such as robust filtering with constraints on communication channels and signal frequency characteristics are discussed. The text features: ·        design approaches to robust filters arranged according to varying complexity level, and emphasizing robust filtering in the parameter-dependent framework for the first time; ·...

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

    International Nuclear Information System (INIS)

    Iliopoulos, AS; Sun, X; Floros, D; Zhang, Y; Yin, FF; Ren, L; Pitsianis, N

    2016-01-01

    Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial

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

    Energy Technology Data Exchange (ETDEWEB)

    Iliopoulos, AS; Sun, X [Duke University, Durham, NC (United States); Floros, D [Aristotle University of Thessaloniki (Greece); Zhang, Y; Yin, FF; Ren, L [Duke University Medical Center, Durham, NC (United States); Pitsianis, N [Aristotle University of Thessaloniki (Greece); Duke University, Durham, NC (United States)

    2016-06-15

    Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial

  3. An adaptive filtering method based on EMD for X-ray pulsar navigation with uncertain measurement noise

    Directory of Open Access Journals (Sweden)

    Li N.

    2017-01-01

    Full Text Available Affected by the unstable pulse radiation and the pulsar directional errors, the statistical characteristics of the pulsar measurement noise may vary with time slowly and cannot be accurately determined, which cause the filtering accuracy of the extended Kalman filter(EKF in pulsar navigation positioning system decline sharply or even diverge. To solve this problem, an adaptive extended Kalman filtering algorithm based on the empirical mode decomposition(EMD is proposed. In this method, the high frequency noise is separated from measurement information of pulsar by the method of EMD, and the noise variance can be estimated to update the parameters of EKF. The simulation results demonstrate that compared with conventional EKF, the proposed method can adaptively track the change of the measurement noise, and still keeps high estimation accuracy with unknown measurement noise, the positioning accuracy of the pulsar navigation is improved simultaneously.

  4. Design of application specific long period waveguide grating filters using adaptive particle swarm optimization algorithms

    International Nuclear Information System (INIS)

    Semwal, Girish; Rastogi, Vipul

    2014-01-01

    We present design optimization of wavelength filters based on long period waveguide gratings (LPWGs) using the adaptive particle swarm optimization (APSO) technique. We demonstrate optimization of the LPWG parameters for single-band, wide-band and dual-band rejection filters for testing the convergence of APSO algorithms. After convergence tests on the algorithms, the optimization technique has been implemented to design more complicated application specific filters such as erbium doped fiber amplifier (EDFA) amplified spontaneous emission (ASE) flattening, erbium doped waveguide amplifier (EDWA) gain flattening and pre-defined broadband rejection filters. The technique is useful for designing and optimizing the parameters of LPWGs to achieve complicated application specific spectra. (paper)

  5. Research and Application on Fractional-Order Darwinian PSO Based Adaptive Extended Kalman Filtering Algorithm

    Directory of Open Access Journals (Sweden)

    Qiguang Zhu

    2014-05-01

    Full Text Available To resolve the difficulty in establishing accurate priori noise model for the extended Kalman filtering algorithm, propose the fractional-order Darwinian particle swarm optimization (PSO algorithm has been proposed and introduced into the fuzzy adaptive extended Kalman filtering algorithm. The natural selection method has been adopted to improve the standard particle swarm optimization algorithm, which enhanced the diversity of particles and avoided the premature. In addition, the fractional calculus has been used to improve the evolution speed of particles. The PSO algorithm after improved has been applied to train fuzzy adaptive extended Kalman filter and achieve the simultaneous localization and mapping. The simulation results have shown that compared with the geese particle swarm optimization training of fuzzy adaptive extended Kalman filter localization and mapping algorithm, has been greatly improved in terms of localization and mapping.

  6. Adaptivity in Professional Printing Systems

    NARCIS (Netherlands)

    Verriet, J.H.; Basten, T; Hamberg, R.; Reckers, F.J.; Somers, L.

    2013-01-01

    There is a constant pressure on developers of embedded systems to simultaneously increase system functionality and to decrease development costs. Aviable way to obtain a better system performance with the same physical hardware is adaptivity: a system should be able to adapt itself to dynamically

  7. A hybrid robust fault tolerant control based on adaptive joint unscented Kalman filter.

    Science.gov (United States)

    Shabbouei Hagh, Yashar; Mohammadi Asl, Reza; Cocquempot, Vincent

    2017-01-01

    In this paper, a new hybrid robust fault tolerant control scheme is proposed. A robust H ∞ control law is used in non-faulty situation, while a Non-Singular Terminal Sliding Mode (NTSM) controller is activated as soon as an actuator fault is detected. Since a linear robust controller is designed, the system is first linearized through the feedback linearization method. To switch from one controller to the other, a fuzzy based switching system is used. An Adaptive Joint Unscented Kalman Filter (AJUKF) is used for fault detection and diagnosis. The proposed method is based on the simultaneous estimation of the system states and parameters. In order to show the efficiency of the proposed scheme, a simulated 3-DOF robotic manipulator is used. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

  8. An adaptive unscented Kalman filter-based adaptive tracking control for wheeled mobile robots with control constrains in the presence of wheel slipping

    Directory of Open Access Journals (Sweden)

    Mingyue Cui

    2016-09-01

    Full Text Available A novel control approach is proposed for trajectory tracking of a wheeled mobile robot with unknown wheels’ slipping. The longitudinal and lateral slipping are considered and processed as three time-varying parameters. The adaptive unscented Kalman filter is then designed to estimate the slipping parameters online, an adaptive adjustment of the noise covariances in the estimation process is implemented using a technique of covariance matching in the adaptive unscented Kalman filter context. Considering the practical physical constrains, a stable tracking control law for this robot system is proposed by the backstepping method. Asymptotic stability is guaranteed by Lyapunov stability theory. Control gains are determined online by applying pole placement method. Simulation and real experiment results show the effectiveness and robustness of the proposed control method.

  9. Band-pass filtering algorithms for adaptive control of compressor pre-stall modes in aircraft gas-turbine engine

    Science.gov (United States)

    Kuznetsova, T. A.

    2018-05-01

    The methods for increasing gas-turbine aircraft engines' (GTE) adaptive properties to interference based on empowerment of automatic control systems (ACS) are analyzed. The flow pulsation in suction and a discharge line of the compressor, which may cause the stall, are considered as the interference. The algorithmic solution to the problem of GTE pre-stall modes’ control adapted to stability boundary is proposed. The aim of the study is to develop the band-pass filtering algorithms to provide the detection functions of the compressor pre-stall modes for ACS GTE. The characteristic feature of pre-stall effect is the increase of pressure pulsation amplitude over the impeller at the multiples of the rotor’ frequencies. The used method is based on a band-pass filter combining low-pass and high-pass digital filters. The impulse response of the high-pass filter is determined through a known low-pass filter impulse response by spectral inversion. The resulting transfer function of the second order band-pass filter (BPF) corresponds to a stable system. The two circuit implementations of BPF are synthesized. Designed band-pass filtering algorithms were tested in MATLAB environment. Comparative analysis of amplitude-frequency response of proposed implementation allows choosing the BPF scheme providing the best quality of filtration. The BPF reaction to the periodic sinusoidal signal, simulating the experimentally obtained pressure pulsation function in the pre-stall mode, was considered. The results of model experiment demonstrated the effectiveness of applying band-pass filtering algorithms as part of ACS to identify the pre-stall mode of the compressor for detection of pressure fluctuations’ peaks, characterizing the compressor’s approach to the stability boundary.

  10. A Robust Adaptive Unscented Kalman Filter for Nonlinear Estimation with Uncertain Noise Covariance.

    Science.gov (United States)

    Zheng, Binqi; Fu, Pengcheng; Li, Baoqing; Yuan, Xiaobing

    2018-03-07

    The Unscented Kalman filter (UKF) may suffer from performance degradation and even divergence while mismatch between the noise distribution assumed as a priori by users and the actual ones in a real nonlinear system. To resolve this problem, this paper proposes a robust adaptive UKF (RAUKF) to improve the accuracy and robustness of state estimation with uncertain noise covariance. More specifically, at each timestep, a standard UKF will be implemented first to obtain the state estimations using the new acquired measurement data. Then an online fault-detection mechanism is adopted to judge if it is necessary to update current noise covariance. If necessary, innovation-based method and residual-based method are used to calculate the estimations of current noise covariance of process and measurement, respectively. By utilizing a weighting factor, the filter will combine the last noise covariance matrices with the estimations as the new noise covariance matrices. Finally, the state estimations will be corrected according to the new noise covariance matrices and previous state estimations. Compared with the standard UKF and other adaptive UKF algorithms, RAUKF converges faster to the actual noise covariance and thus achieves a better performance in terms of robustness, accuracy, and computation for nonlinear estimation with uncertain noise covariance, which is demonstrated by the simulation results.

  11. Sampled data CT system including analog filter and compensating digital filter

    International Nuclear Information System (INIS)

    Glover, G. H.; DallaPiazza, D. G.; Pelc, N. J.

    1985-01-01

    A CT scanner in which the amount of x-ray information acquired per unit time is substantially increased by using a continuous-on x-ray source and a sampled data system with the detector. An analog filter is used in the sampling system for band limiting the detector signal below the highest frequency of interest, but is a practically realizable filter and is therefore non-ideal. A digital filter is applied to the detector data after digitization to compensate for the characteristics of the analog filter, and to provide an overall filter characteristic more nearly like the ideal

  12. Semiconductor cleaning liquid delivery system and its filter; Handotaiyo seijo yakueki kyokyu system to filter

    Energy Technology Data Exchange (ETDEWEB)

    Saito, T. [Kanto Chemical Co. Inc., Tokyo (Japan); Hayama, H.; Sakka, T. [Nitto Denko Corp., Osaka (Japan)

    1994-11-30

    Most of chemicals used for producing semiconductors are supplied automatically by a chemical delivery system to production devices. This paper explains the current status and the trends of the system. This system supplies the chemicals in the order of a tank lorry, a storage tank, a supply tank, a filter and a production device, and the transfer is performed receiving a supply signal from the supply tank and the production device. The transfer may be done through a dilution equipment. Filters currently used have membrane pore sizes of 0.2 to 0.1 microns as prefilters, and 0.1 to 0.05 microns as final filters. Chemicals used are diverse and can be divided into acid-, alkaline-, and solvent-based groups. Fluorine resin filters are used for acid- and alkaline-resistant applications, and SUS/fluorine resin filters for solvent-resistant applications. Use of large-sized filters of element construction with a membrane area of 1 m{sup 2} class is increasing recently in addition to selection from a performance viewpoint, including particle removing performance. 9 figs., 7 tabs.

  13. Adaptive filtering of GOCE-derived gravity gradients of the disturbing potential in the context of the space-wise approach

    Science.gov (United States)

    Piretzidis, Dimitrios; Sideris, Michael G.

    2017-09-01

    Filtering and signal processing techniques have been widely used in the processing of satellite gravity observations to reduce measurement noise and correlation errors. The parameters and types of filters used depend on the statistical and spectral properties of the signal under investigation. Filtering is usually applied in a non-real-time environment. The present work focuses on the implementation of an adaptive filtering technique to process satellite gravity gradiometry data for gravity field modeling. Adaptive filtering algorithms are commonly used in communication systems, noise and echo cancellation, and biomedical applications. Two independent studies have been performed to introduce adaptive signal processing techniques and test the performance of the least mean-squared (LMS) adaptive algorithm for filtering satellite measurements obtained by the gravity field and steady-state ocean circulation explorer (GOCE) mission. In the first study, a Monte Carlo simulation is performed in order to gain insights about the implementation of the LMS algorithm on data with spectral behavior close to that of real GOCE data. In the second study, the LMS algorithm is implemented on real GOCE data. Experiments are also performed to determine suitable filtering parameters. Only the four accurate components of the full GOCE gravity gradient tensor of the disturbing potential are used. The characteristics of the filtered gravity gradients are examined in the time and spectral domain. The obtained filtered GOCE gravity gradients show an agreement of 63-84 mEötvös (depending on the gravity gradient component), in terms of RMS error, when compared to the gravity gradients derived from the EGM2008 geopotential model. Spectral-domain analysis of the filtered gradients shows that the adaptive filters slightly suppress frequencies in the bandwidth of approximately 10-30 mHz. The limitations of the adaptive LMS algorithm are also discussed. The tested filtering algorithm can be

  14. Adaptive Filtering for Aeroservoelastic Response Suppression, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — CSA Engineering proposes the design of an adaptive aeroelastic mode suppression for advanced fly-by-wire aircraft, which will partition the modal suppression...

  15. Hierarchical Threshold Adaptive for Point Cloud Filter Algorithm of Moving Surface Fitting

    Directory of Open Access Journals (Sweden)

    ZHU Xiaoxiao

    2018-02-01

    Full Text Available In order to improve the accuracy,efficiency and adaptability of point cloud filtering algorithm,a hierarchical threshold adaptive for point cloud filter algorithm of moving surface fitting was proposed.Firstly,the noisy points are removed by using a statistic histogram method.Secondly,the grid index is established by grid segmentation,and the surface equation is set up through the lowest point among the neighborhood grids.The real height and fit are calculated.The difference between the elevation and the threshold can be determined.Finally,in order to improve the filtering accuracy,hierarchical filtering is used to change the grid size and automatically set the neighborhood size and threshold until the filtering result reaches the accuracy requirement.The test data provided by the International Photogrammetry and Remote Sensing Society (ISPRS is used to verify the algorithm.The first and second error and the total error are 7.33%,10.64% and 6.34% respectively.The algorithm is compared with the eight classical filtering algorithms published by ISPRS.The experiment results show that the method has well-adapted and it has high accurate filtering result.

  16. Delay Estimator and Improved Proportionate Multi-Delay Adaptive Filtering Algorithm

    Directory of Open Access Journals (Sweden)

    E. Verteletskaya

    2012-04-01

    Full Text Available This paper pertains to speech and acoustic signal processing, and particularly to a determination of echo path delay and operation of echo cancellers. To cancel long echoes, the number of weights in a conventional adaptive filter must be large. The length of the adaptive filter will directly affect both the degree of accuracy and the convergence speed of the adaptation process. We present a new adaptive structure which is capable to deal with multiple dispersive echo paths. An adaptive filter according to the present invention includes means for storing an impulse response in a memory, the impulse response being indicative of the characteristics of a transmission line. It also includes a delay estimator for detecting ranges of samples within the impulse response having relatively large distribution of echo energy. These ranges of samples are being indicative of echoes on the transmission line. An adaptive filter has a plurality of weighted taps, each of the weighted taps having an associated tap weight value. A tap allocation/control circuit establishes the tap weight values in response to said detecting means so that only taps within the regions of relatively large distributions of echo energy are turned on. Thus, the convergence speed and the degree of estimation in the adaptation process can be improved.

  17. Adaptive Output Tracking Control for Nonlinear Systems with Failed Actuators and Aircraft Flight System Applications

    Directory of Open Access Journals (Sweden)

    Chuanjing Hou

    2015-01-01

    Full Text Available An adaptive failure compensation scheme using output feedback is proposed for a class of nonlinear systems with nonlinearities depending on the unmeasured states of systems. Adaptive high-gain K-filters are presented to suppress the nonlinearities while the proposed backstepping adaptive high-gain controller guarantees the stability of the closed-loop system and small tracking errors. Simulation results verify that the adaptive failure compensation scheme is effective.

  18. Adaptive Output Tracking Control for Nonlinear Systems with Failed Actuators and Aircraft Flight System Applications

    OpenAIRE

    Hou, Chuanjing; Hu, Lisheng; Zhang, Yingwei

    2015-01-01

    An adaptive failure compensation scheme using output feedback is proposed for a class of nonlinear systems with nonlinearities depending on the unmeasured states of systems. Adaptive high-gain K-filters are presented to suppress the nonlinearities while the proposed backstepping adaptive high-gain controller guarantees the stability of the closed-loop system and small tracking errors. Simulation results verify that the adaptive failure compensation scheme is effective.

  19. Automatic speech signal segmentation based on the innovation adaptive filter

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

    Full Text Available Speech segmentation is an essential stage in designing automatic speech recognition systems and one can find several algorithms proposed in the literature. It is a difficult problem, as speech is immensely variable. The aim of the authors’ studies was to design an algorithm that could be employed at the stage of automatic speech recognition. This would make it possible to avoid some problems related to speech signal parametrization. Posing the problem in such a way requires the algorithm to be capable of working in real time. The only such algorithm was proposed by Tyagi et al., (2006, and it is a modified version of Brandt’s algorithm. The article presents a new algorithm for unsupervised automatic speech signal segmentation. It performs segmentation without access to information about the phonetic content of the utterances, relying exclusively on second-order statistics of a speech signal. The starting point for the proposed method is time-varying Schur coefficients of an innovation adaptive filter. The Schur algorithm is known to be fast, precise, stable and capable of rapidly tracking changes in second order signal statistics. A transfer from one phoneme to another in the speech signal always indicates a change in signal statistics caused by vocal track changes. In order to allow for the properties of human hearing, detection of inter-phoneme boundaries is performed based on statistics defined on the mel spectrum determined from the reflection coefficients. The paper presents the structure of the algorithm, defines its properties, lists parameter values, describes detection efficiency results, and compares them with those for another algorithm. The obtained segmentation results, are satisfactory.

  20. Potential for HEPA filter damage from water spray systems in filter plenums

    Energy Technology Data Exchange (ETDEWEB)

    Bergman, W. [Lawrence Livermore National Lab., CA (United States); Fretthold, J.K. [Rocky Flats Safe Sites of Colorado, Golden, CO (United States); Slawski, J.W. [Department of Energy, Germantown, MD (United States)

    1997-08-01

    The water spray systems in high efficiency particulate air (HEPA) filter plenums that are used in nearly all Department of Energy (DOE) facilities for protection against fire was designed under the assumption that the HEPA filters would not be damaged by the water sprays. The most likely scenario for filter damage involves filter plugging by the water spray, followed by the fan blowing out the filter medium. A number of controlled laboratory tests that were previously conducted in the late 1980s are reviewed in this paper to provide a technical basis for the potential HEPA filter damage by the water spray system in HEPA filter plenums. In addition to the laboratory tests, the scenario for BEPA filter damage during fires has also occurred in the field. A fire in a four-stage, BEPA filter plenum at Rocky Flats in 1980 caused the first three stages of BEPA filters to blow out of their housing and the fourth stage to severely bow. Details of this recently declassified fire are presented in this paper. Although these previous findings suggest serious potential problems exist with the current water spray system in filter plenums, additional studies are required to confirm unequivocally that DOE`s critical facilities are at risk. 22 refs., 15 figs.

  1. Suppression of narrow-band interference in a PN spread-spectrum receiver using a CTD-based adaptive filter

    Science.gov (United States)

    Saulnier, G. J.; Das, P.; Milstein, L. B.

    1984-11-01

    Analytical results have shown that adaptive filtering can be a powerful tool for the rejection of narrow-band interference in a spread-spectrum receiver. However, the complexity of adaptive filtering hardware has hindered the experimental verification of these results. This paper describes a new adaptive filter architecture for implementing the Widrow-Hoff LMS algorithm while using only two multipliers regardless of filter order. This hardware simplification is achieved through the use of a burst processing technique. A 16-tap version of this adaptive filter constructed using charge-transfer devices (CTD's) is used to suppress a single tone jammer in a direct sequence spread-spectrum receiver. Probability of error measurements demonstrating the effectiveness of the adaptive filter for suppressing the single tone jammer along with simulation results for the optimal Weiner-Hopf filter are presented and discussed.

  2. Nonlinear control and filtering using differential flatness approaches applications to electromechanical systems

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The bo...

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

    Science.gov (United States)

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

    2011-02-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  5. Adaptive vibration isolation system for diesel engine

    Institute of Scientific and Technical Information of China (English)

    YANG Tie-jun; ZHANG Xin-yu; XIAO You-hong; HUANG Jin-e; LIU Zhi-gang

    2004-01-01

    An active two-stage isolation mounting, on which servo-hydraulic system is used as the actuator (secondary vibration source) and a diesel engine is used as primary vibration source, has been built. The upper mass of the mounting is composed of a 495diesel and an electrical eddy current dynamometer. The lower mass is divided into four small masses to which servo-hydraulic actuator and rubber isolators are attached. According to the periodical characteristics of diesel vibration signals, a multi-point adaptive strategy based on adaptive comb filtered algorithm is applied to active multi-direction coupled vibrations control for the engine. The experimental results demonstrate that a good suppression in the effective range of phase compensation in secondary path (within 100Hz) at different operation conditions is achieved, and verify that this strategy is effective. The features of the active system, the development activities carried out on the system and experimental results are discussed in the paper.

  6. Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

    KAUST Repository

    Xie, Qing

    2016-01-12

    The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially the filter can be shared by different queries and expensive to evaluate. The conventional objective for such a problem is to minimize the overall execution cost to solve all queries, by planning the order of filter evaluation in shared strategy. However, in streaming scenario, the characteristics of data items may change in process, which can bring some uncertainty to the outcome of individual filter evaluation, and affect the plan of query execution as well as the overall execution cost. In our work, considering the influence of the uncertain variation of data characteristics, we propose a framework to deal with the dynamic adjustment of filter ordering for query execution on data stream, and focus on the issues of cost management. By incrementally monitoring and analyzing the results of filter evaluation, our proposed approach can be effectively adaptive to the varied stream behavior and adjust the optimal ordering of filter evaluation, so as to optimize the execution cost. In order to achieve satisfactory performance and efficiency, we also discuss the trade-off between the adaptivity of our framework and the overhead incurred by filter adaption. The experimental results on synthetic and two real data sets (traffic and multimedia) show that our framework can effectively reduce and balance the overall query execution cost and keep high adaptivity in streaming scenario.

  7. SUPPRESSION OF POWERLINE INTERFERENCE IN ECG USING ADAPTIVE DIGITAL FILTER BY

    OpenAIRE

    Mbachu C.B; Onoh G. N; Idigo V.E; Oguejiofor O.S

    2011-01-01

    Artifacts in electrocardiogram (ECG) records are caused by various factors, such as powerline interference, electroencephalogram (EEG), electromyogram (EMG) and baseline wander. These noise sources increase the difficulty in analyzing the ECG and to obtaining clinical information. For that reason, it is necessary to designspecific filters to decrease such artifacts in ECG records. In this paper, FIR adaptive filter based on a least mean square (LMS) algorithm for eliminating 50Hz powerline in...

  8. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

    Directory of Open Access Journals (Sweden)

    Byeong Hak Kim

    2017-12-01

    Full Text Available Unmanned aerial vehicles (UAVs are equipped with optical systems including an infrared (IR camera such as electro-optical IR (EO/IR, target acquisition and designation sights (TADS, or forward looking IR (FLIR. However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC and scene-based NUC (SBNUC. However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA. In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR and long wave infrared (LWIR images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

  9. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

    Science.gov (United States)

    Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

    2017-01-01

    Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC. PMID:29280970

  10. Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications.

    Science.gov (United States)

    Kim, Byeong Hak; Kim, Min Young; Chae, You Seong

    2017-12-27

    Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.

  11. Adaptive Matrices and Filters for Color Texture Classification

    NARCIS (Netherlands)

    Giotis, Ioannis; Bunte, Kerstin; Petkov, Nicolai; Biehl, Michael

    In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain,

  12. Filter system for purifying gas or air streams

    International Nuclear Information System (INIS)

    Ohlmeyer, M.; Wilhelm, J.

    1981-01-01

    A filter system is provided for purifying a gas stream by means of flowable or tricklable contact filter material, wherein the stream flows through the filter material and the filter material forms a movable bed. The system contains a filter chamber through which the filter material can flow and which is provided with an inlet opening and an outlet opening for the filter material between which the filter material is conveyed by gravity. The filter system includes deflection means for deflecting the stream , after a first passage of the stream through the filter bed to charge the filter bed for a first time, to a position above where the stream first passed through the filter bed and for conducting the stream at least once again transversely through the filter bed above the first charge so that the filter bed is charged a second time. The filter chamber contains a first opening where the stream enters the filter bed for the first time and is aligned with the deflection means, and a second opening aligned with the deflection means and above the first opening. The second opening is located where the stream leaves the filter bed for the second time, with a partial quantity of the gas stream being able to pass directly through the filter bed from the first opening to the second opening without going through the deflection means. The distance between the upper edge of the first opening and the lower edge of the second opening is at least twice the thickness of the filter chamber

  13. Adaptive Kalman Filter Applied to Vision Based Head Gesture Tracking for Playing Video Games

    Directory of Open Access Journals (Sweden)

    Mohammadreza Asghari Oskoei

    2017-11-01

    Full Text Available This paper proposes an adaptive Kalman filter (AKF to improve the performance of a vision-based human machine interface (HMI applied to a video game. The HMI identifies head gestures and decodes them into corresponding commands. Face detection and feature tracking algorithms are used to detect optical flow produced by head gestures. Such approaches often fail due to changes in head posture, occlusion and varying illumination. The adaptive Kalman filter is applied to estimate motion information and reduce the effect of missing frames in a real-time application. Failure in head gesture tracking eventually leads to malfunctioning game control, reducing the scores achieved, so the performance of the proposed vision-based HMI is examined using a game scoring mechanism. The experimental results show that the proposed interface has a good response time, and the adaptive Kalman filter improves the game scores by ten percent.

  14. An Adaptive Filter for the Removal of Drifting Sinusoidal Noise Without a Reference.

    Science.gov (United States)

    Kelly, John W; Siewiorek, Daniel P; Smailagic, Asim; Wang, Wei

    2016-01-01

    This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoid's drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filter's bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noise's frequency, properly adjust its bandwidth, and outperform comparative methods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.

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

    Science.gov (United States)

    Diaz-Ramirez, Victor H.; Kober, Vitaly

    2008-08-01

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

  16. Artifact reduction of compressed images and video combining adaptive fuzzy filtering and directional anisotropic diffusion

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari

    2011-01-01

    and ringing artifacts, we have applied directional anisotropic diffusion. Besides that, the selection of the adaptive threshold parameter for the diffusion coefficient has also improved the performance of the algorithm. Experimental results on JPEG compressed images as well as MJPEG and H.264 compressed......Fuzzy filtering is one of the recently developed methods for reducing distortion in compressed images and video. In this paper, we combine the powerful anisotropic diffusion equations with fuzzy filtering in order to reduce the impact of artifacts. Based on the directional nature of the blocking...... videos show improvement in artifact reduction of the proposed algorithm over other directional and spatial fuzzy filters....

  17. Failure detection by adaptive lattice modelling using Kalman filtering methodology : application to NPP

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1991-03-01

    Detection of failure in the operational status of a NPP is described. The method uses lattice form of the signal modelling established by means of Kalman filtering methodology. In this approach each lattice parameter is considered to be a state and the minimum variance estimate of the states is performed adaptively by optimal parameter estimation together with fast convergence and favourable statistical properties. In particular, the state covariance is also the covariance of the error committed by that estimate of the state value and the Mahalanobis distance formed for pattern comparison takes x 2 distribution for normally distributed signals. The failure detection is performed after a decision making process by probabilistic assessments based on the statistical information provided. The failure detection system is implemented in multi-channel signal environment of Borssele NPP and its favourable features are demonstrated. (author). 29 refs.; 7 figs

  18. Environmental Adaptations, Ecological Filtering, and Dispersal Central to Insect Invasions.

    Science.gov (United States)

    Renault, David; Laparie, Mathieu; McCauley, Shannon J; Bonte, Dries

    2018-01-07

    Insect invasions, the establishment and spread of nonnative insects in new regions, can have extensive economic and environmental consequences. Increased global connectivity accelerates rates of introductions, while climate change may decrease the barriers to invader species' spread. We follow an individual-level insect- and arachnid-centered perspective to assess how the process of invasion is influenced by phenotypic heterogeneity associated with dispersal and stress resistance, and their coupling, across the multiple steps of the invasion process. We also provide an overview and synthesis on the importance of environmental filters during the entire invasion process for the facilitation or inhibition of invasive insect population spread. Finally, we highlight important research gaps and the relevance and applicability of ongoing natural range expansions in the context of climate change to gain essential mechanistic insights into insect invasions.

  19. Support Vector Regression-Based Adaptive Divided Difference Filter for Nonlinear State Estimation Problems

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

    Full Text Available We present a support vector regression-based adaptive divided difference filter (SVRADDF algorithm for improving the low state estimation accuracy of nonlinear systems, which are typically affected by large initial estimation errors and imprecise prior knowledge of process and measurement noises. The derivative-free SVRADDF algorithm is significantly simpler to compute than other methods and is implemented using only functional evaluations. The SVRADDF algorithm involves the use of the theoretical and actual covariance of the innovation sequence. Support vector regression (SVR is employed to generate the adaptive factor to tune the noise covariance at each sampling instant when the measurement update step executes, which improves the algorithm’s robustness. The performance of the proposed algorithm is evaluated by estimating states for (i an underwater nonmaneuvering target bearing-only tracking system and (ii maneuvering target bearing-only tracking in an air-traffic control system. The simulation results show that the proposed SVRADDF algorithm exhibits better performance when compared with a traditional DDF algorithm.

  20. An Adaptive Estimation of Forecast Error Covariance Parameters for Kalman Filtering Data Assimilation

    Institute of Scientific and Technical Information of China (English)

    Xiaogu ZHENG

    2009-01-01

    An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed approach could be applied to Kalman filtering data assimilation with imperfect models when the model error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate the efficacy of the proposed approach.

  1. Kalman Filtering for Delayed Singular Systems with Multiplicative Noise

    Institute of Scientific and Technical Information of China (English)

    Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang

    2016-01-01

    Kalman filtering problem for singular systems is dealt with, where the measurements consist of instantaneous measurements and delayed ones, and the plant includes multiplicative noise. By utilizing standard singular value decomposition, the restricted equivalent delayed system is presented, and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma. The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations, and a numerical example is presented to show the validity and efficiency of the proposed approach, where the comparison between the filter and predictor is also given.

  2. Subspace System Identification of the Kalman Filter

    Directory of Open Access Journals (Sweden)

    David Di Ruscio

    2003-07-01

    Full Text Available Some proofs concerning a subspace identification algorithm are presented. It is proved that the Kalman filter gain and the noise innovations process can be identified directly from known input and output data without explicitly solving the Riccati equation. Furthermore, it is in general and for colored inputs, proved that the subspace identification of the states only is possible if the deterministic part of the system is known or identified beforehand. However, if the inputs are white, then, it is proved that the states can be identified directly. Some alternative projection matrices which can be used to compute the extended observability matrix directly from the data are presented. Furthermore, an efficient method for computing the deterministic part of the system is presented. The closed loop subspace identification problem is also addressed and it is shown that this problem is solved and unbiased estimates are obtained by simply including a filter in the feedback. Furthermore, an algorithm for consistent closed loop subspace estimation is presented. This algorithm is using the controller parameters in order to overcome the bias problem.

  3. Comparison of robust H∞ filter and Kalman filter for initial alignment of inertial navigation system

    Institute of Scientific and Technical Information of China (English)

    HAO Yan-ling; CHEN Ming-hui; LI Liang-jun; XU Bo

    2008-01-01

    There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system.This paper discussed the use of GPS,but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS).One method is based on the Kalman filter (KF),and the other is based on the robust filter.Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF,given substantial process noise or unknown noise statistics.So the robust filter is an effective and useful method for initial alignment of SINS.This research should make the use of SINS more popular,and is also a step for further research.

  4. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

    KAUST Repository

    Cannistraci, Carlo Vittorio

    2015-01-26

    Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet\\'s performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.

  5. Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra

    KAUST Repository

    Cannistraci, Carlo Vittorio; Abbas, Ahmed; Gao, Xin

    2015-01-01

    Denoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.

  6. A zero phase adaptive fuzzy Kalman filter for physiological tremor suppression in robotically assisted minimally invasive surgery.

    Science.gov (United States)

    Sang, Hongqiang; Yang, Chenghao; Liu, Fen; Yun, Jintian; Jin, Guoguang; Chen, Fa

    2016-12-01

    Hand physiological tremor of surgeons can cause vibration at the surgical instrument tip, which may make it difficult for the surgeon to perform fine manipulations of tissue, needles, and sutures. A zero phase adaptive fuzzy Kalman filter (ZPAFKF) is proposed to suppress hand tremor and vibration of a robotic surgical system. The involuntary motion can be reduced by adding a compensating signal that has the same magnitude and frequency but opposite phase with the tremor signal. Simulations and experiments using different filters were performed. Results show that the proposed filter can avoid the loss of useful motion information and time delay, and better suppress minor and varying tremor. The ZPAFKF can provide less error, preferred accuracy, better tremor estimation, and more desirable compensation performance, to suppress hand tremor and decrease vibration at the surgical instrument tip. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  7. Adaptive Kalman filtering for real-time mapping of the visual field

    Science.gov (United States)

    Ward, B. Douglas; Janik, John; Mazaheri, Yousef; Ma, Yan; DeYoe, Edgar A.

    2013-01-01

    This paper demonstrates the feasibility of real-time mapping of the visual field for clinical applications. Specifically, three aspects of this problem were considered: (1) experimental design, (2) statistical analysis, and (3) display of results. Proper experimental design is essential to achieving a successful outcome, particularly for real-time applications. A random-block experimental design was shown to have less sensitivity to measurement noise, as well as greater robustness to error in modeling of the hemodynamic impulse response function (IRF) and greater flexibility than common alternatives. In addition, random encoding of the visual field allows for the detection of voxels that are responsive to multiple, not necessarily contiguous, regions of the visual field. Due to its recursive nature, the Kalman filter is ideally suited for real-time statistical analysis of visual field mapping data. An important feature of the Kalman filter is that it can be used for nonstationary time series analysis. The capability of the Kalman filter to adapt, in real time, to abrupt changes in the baseline arising from subject motion inside the scanner and other external system disturbances is important for the success of clinical applications. The clinician needs real-time information to evaluate the success or failure of the imaging run and to decide whether to extend, modify, or terminate the run. Accordingly, the analytical software provides real-time displays of (1) brain activation maps for each stimulus segment, (2) voxel-wise spatial tuning profiles, (3) time plots of the variability of response parameters, and (4) time plots of activated volume. PMID:22100663

  8. Robust and Adaptive Block Tracking Method Based on Particle Filter

    Directory of Open Access Journals (Sweden)

    Bin Sun

    2015-10-01

    Full Text Available In the field of video analysis and processing, object tracking is attracting more and more attention especially in traffic management, digital surveillance and so on. However problems such as objects’ abrupt motion, occlusion and complex target structures would bring difficulties to academic study and engineering application. In this paper, a fragmentsbased tracking method using the block relationship coefficient is proposed. In this method, we use particle filter algorithm and object region is divided into blocks initially. The contribution of this method is that object features are not extracted just from a single block, the relationship between current block and its neighbor blocks are extracted to describe the variation of the block. Each block is weighted according to the block relationship coefficient when the block is voted on the most matched region in next frame. This method can make full use of the relationship between blocks. The experimental results demonstrate that our method can provide good performance in condition of occlusion and abrupt posture variation.

  9. Technical Note: A fast online adaptive replanning method for VMAT using flattening filter free beams

    Energy Technology Data Exchange (ETDEWEB)

    Ates, Ozgur; Ahunbay, Ergun E.; Li, X. Allen, E-mail: ali@mcw.edu [Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin 53226 (United States); Moreau, Michel [Elekta, Inc., Maryland Heights, Missouri 63043 (United States)

    2016-06-15

    Purpose: To develop a fast replanning algorithm based on segment aperture morphing (SAM) for online replanning of volumetric modulated arc therapy (VMAT) with flattening filter free (FFF) beams. Methods: A software tool was developed to interface with a VMAT research planning system, which enables the input and output of beam and machine parameters of VMAT plans. The SAM algorithm was used to modify multileaf collimator positions for each segment aperture based on the changes of the target from the planning (CT/MR) to daily image [CT/CBCT/magnetic resonance imaging (MRI)]. The leaf travel distance was controlled for large shifts to prevent the increase of VMAT delivery time. The SAM algorithm was tested for 11 patient cases including prostate, pancreatic, and lung cancers. For each daily image set, three types of VMAT plans, image-guided radiation therapy (IGRT) repositioning, SAM adaptive, and full-scope reoptimization plans, were generated and compared. Results: The SAM adaptive plans were found to have improved the plan quality in target and/or critical organs when compared to the IGRT repositioning plans and were comparable to the reoptimization plans based on the data of planning target volume (PTV)-V100 (volume covered by 100% of prescription dose). For the cases studied, the average PTV-V100 was 98.85% ± 1.13%, 97.61% ± 1.45%, and 92.84% ± 1.61% with FFF beams for the reoptimization, SAM adaptive, and repositioning plans, respectively. The execution of the SAM algorithm takes less than 10 s using 16-CPU (2.6 GHz dual core) hardware. Conclusions: The SAM algorithm can generate adaptive VMAT plans using FFF beams with comparable plan qualities as those from the full-scope reoptimization plans based on daily CT/CBCT/MRI and can be used for online replanning to address interfractional variations.

  10. Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices

    Directory of Open Access Journals (Sweden)

    Andrea Baiocchi

    2015-01-01

    Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.

  11. Comparisons of adaptive TIN modelling filtering method and threshold segmentation filtering method of LiDAR point cloud

    International Nuclear Information System (INIS)

    Chen, Lin; Fan, Xiangtao; Du, Xiaoping

    2014-01-01

    Point cloud filtering is the basic and key step in LiDAR data processing. Adaptive Triangle Irregular Network Modelling (ATINM) algorithm and Threshold Segmentation on Elevation Statistics (TSES) algorithm are among the mature algorithms. However, few researches concentrate on the parameter selections of ATINM and the iteration condition of TSES, which can greatly affect the filtering results. First the paper presents these two key problems under two different terrain environments. For a flat area, small height parameter and angle parameter perform well and for areas with complex feature changes, large height parameter and angle parameter perform well. One-time segmentation is enough for flat areas, and repeated segmentations are essential for complex areas. Then the paper makes comparisons and analyses of the results by these two methods. ATINM has a larger I error in both two data sets as it sometimes removes excessive points. TSES has a larger II error in both two data sets as it ignores topological relations between points. ATINM performs well even with a large region and a dramatic topology while TSES is more suitable for small region with flat topology. Different parameters and iterations can cause relative large filtering differences

  12. The second order extended Kalman filter and Markov nonlinear filter for data processing in interferometric systems

    International Nuclear Information System (INIS)

    Ermolaev, P; Volynsky, M

    2014-01-01

    Recurrent stochastic data processing algorithms using representation of interferometric signal as output of a dynamic system, which state is described by vector of parameters, in some cases are more effective, compared with conventional algorithms. Interferometric signals depend on phase nonlinearly. Consequently it is expedient to apply algorithms of nonlinear stochastic filtering, such as Kalman type filters. An application of the second order extended Kalman filter and Markov nonlinear filter that allows to minimize estimation error is described. Experimental results of signals processing are illustrated. Comparison of the algorithms is presented and discussed.

  13. Adaptive protection algorithm and system

    Science.gov (United States)

    Hedrick, Paul [Pittsburgh, PA; Toms, Helen L [Irwin, PA; Miller, Roger M [Mars, PA

    2009-04-28

    An adaptive protection algorithm and system for protecting electrical distribution systems traces the flow of power through a distribution system, assigns a value (or rank) to each circuit breaker in the system and then determines the appropriate trip set points based on the assigned rank.

  14. Design of adaptive filter amplifier in UV communication based on DSP

    Science.gov (United States)

    Lv, Zhaoshun; Wu, Hanping; Li, Junyu

    2016-10-01

    According to the problem of the weak signal at receiving end in UV communication, we design a high gain, continuously adjustable adaptive filter amplifier. Based on proposing overall technical indicators and analyzing its working principle of the signal amplifier, we use chip LMH6629MF and two chips of AD797BN to achieve three-level cascade amplification. And apply hardware of DSP TMS320VC5509A to implement digital filtering. Design and verification by Multisim, Protel 99SE and CCS, the results show that: the amplifier can realize continuously adjustable amplification from 1000 to 10000 times without distortion. Magnification error is <=%4@1000 10000. And equivalent input noise voltage of amplification circuit is <=6 nV/ √Hz @30KHz 45KHz, and realizing function of adaptive filtering. The design provides theoretical reference and technical support for the UV weak signal processing.

  15. A multi-stage noise adaptive switching filter for extremely corrupted images

    Science.gov (United States)

    Dinh, Hai; Adhami, Reza; Wang, Yi

    2015-07-01

    A multi-stage noise adaptive switching filter (MSNASF) is proposed for the restoration of images extremely corrupted by impulse and impulse-like noise. The filter consists of two steps: noise detection and noise removal. The proposed extrema-based noise detection scheme utilizes the false contouring effect to get better over detection rate at low noise density. It is adaptive and will detect not only impulse but also impulse-like noise. In the noise removal step, a novel multi-stage filtering scheme is proposed. It replaces corrupted pixel with the nearest uncorrupted median to preserve details. When compared with other methods, MSNASF provides better peak signal to noise ratio (PSNR) and structure similarity index (SSIM). A subjective evaluation carried out online also demonstrates that MSNASF yields higher fidelity.

  16. An adaptive Multiplicative Extened Kalman Filter for Attitude Estimation of Marine Satellite Tracking Antenna

    DEFF Research Database (Denmark)

    Wang, Yunlong; Soltani, Mohsen; Hussain, Dil muhammed Akbar

    2016-01-01

    , an adaptive Multiplicative Extended Kalman Filter (MEKF) for attitude estimation of Marine Satellite Tracking Antenna (MSTA) is presented with the measurement noise covariance matrix adjusted according to the norm of accelerometer measurements, which can significantly reduce the slamming influence from waves...

  17. Morphological adaptations in filtering screens of Daphnia galeata to food quantity and food quality

    NARCIS (Netherlands)

    Repka, S.; Veen, A.; Vijverberg, J.

    1999-01-01

    We reared clones of the waterflea Daphnia galeata, a common grazer in many types of lakes, under several food regimes to study adaptations to feeding conditions in filter screen morphology and life history. As food regimes, we used low and high concentrations of the green alga Scenedesmus, a high

  18. Super-resolution pupil filtering for visual performance enhancement using adaptive optics

    Science.gov (United States)

    Zhao, Lina; Dai, Yun; Zhao, Junlei; Zhou, Xiaojun

    2018-05-01

    Ocular aberration correction can significantly improve visual function of the human eye. However, even under ideal aberration correction conditions, pupil diffraction restricts the resolution of retinal images. Pupil filtering is a simple super-resolution (SR) method that can overcome this diffraction barrier. In this study, a 145-element piezoelectric deformable mirror was used as a pupil phase filter because of its programmability and high fitting accuracy. Continuous phase-only filters were designed based on Zernike polynomial series and fitted through closed-loop adaptive optics. SR results were validated using double-pass point spread function images. Contrast sensitivity was further assessed to verify the SR effect on visual function. An F-test was conducted for nested models to statistically compare different CSFs. These results indicated CSFs for the proposed SR filter were significantly higher than the diffraction correction (p vision optical correction of the human eye.

  19. Optimization of the reconstruction and anti-aliasing filter in a Wiener filter system

    NARCIS (Netherlands)

    Wesselink, J.M.; Berkhoff, Arthur P.

    2006-01-01

    This paper discusses the influence of the reconstruction and anti-aliasing filters on the performance of a digital implementation of a Wiener filter for active noise control. The overall impact will be studied in combination with a multi-rate system approach. A reconstruction and anti-aliasing

  20. Planetary gearbox fault feature enhancement based on combined adaptive filter method

    Directory of Open Access Journals (Sweden)

    Shuangshu Tian

    2015-12-01

    Full Text Available The reliability of vibration signals acquired from a planetary gear system (the indispensable part of wind turbine gearbox is directly related to the accuracy of fault diagnosis. The complex operation environment leads to lots of interference signals which are included in the vibration signals. Furthermore, both multiple gears meshing with each other and the differences in transmission rout produce strong nonlinearity in the vibration signals, which makes it difficult to eliminate the noise. This article presents a combined adaptive filter method by taking a delayed signal as reference signal, the Self-Adaptive Noise Cancellation method is adopted to eliminate the white noise. In the meanwhile, by applying Gaussian function to transform the input signal into high-dimension feature-space signal, the kernel least mean square algorithm is used to cancel the nonlinear interference. Effectiveness of the method has been verified by simulation signals and test rig signals. By dealing with simulation signal, the signal-to-noise ratio can be improved around 30 dB (white noise and the amplitude of nonlinear interference signal can be depressed up to 50%. Experimental results show remarkable improvements and enhance gear fault features.

  1. Performance Enhancement of Pharmacokinetic Diffuse Fluorescence Tomography by Use of Adaptive Extended Kalman Filtering.

    Science.gov (United States)

    Wang, Xin; Wu, Linhui; Yi, Xi; Zhang, Yanqi; Zhang, Limin; Zhao, Huijuan; Gao, Feng

    2015-01-01

    Due to both the physiological and morphological differences in the vascularization between healthy and diseased tissues, pharmacokinetic diffuse fluorescence tomography (DFT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. In this regime, the extended Kalman filtering (EKF) based method shows numerous advantages including accurate modeling, online estimation of multiparameters, and universal applicability to any optical fluorophore. Nevertheless the performance of the conventional EKF highly hinges on the exact and inaccessible prior knowledge about the initial values. To address the above issues, an adaptive-EKF scheme is proposed based on a two-compartmental model for the enhancement, which utilizes a variable forgetting-factor to compensate the inaccuracy of the initial states and emphasize the effect of the current data. It is demonstrated using two-dimensional simulative investigations on a circular domain that the proposed adaptive-EKF can obtain preferable estimation of the pharmacokinetic-rates to the conventional-EKF and the enhanced-EKF in terms of quantitativeness, noise robustness, and initialization independence. Further three-dimensional numerical experiments on a digital mouse model validate the efficacy of the method as applied in realistic biological systems.

  2. Video enhancement using content-adaptive least mean square filters

    NARCIS (Netherlands)

    Zhao, M.

    2006-01-01

    The current television system is evolving towards an entirely digital, high resolution and high picture rate broadcasting system. As compatibility with the past weights heavily for a popular consumer service, this evolution progresses rather slowly. The progress is particularly slow compared to the

  3. Adaptive digital filtering for X-ray spectroscopy

    International Nuclear Information System (INIS)

    Lakatos, T.

    1988-01-01

    Our intention was to realise a system (patent pending) with a finite weighting time interval to eliminate the high rate effects and with a weighting function which is symmetrical with respect to the time of pulse appearance providing simultaneously high resolution and much more higher througput rate then the traditional systems give

  4. Adaptive security systems -- Combining expert systems with adaptive technologies

    International Nuclear Information System (INIS)

    Argo, P.; Loveland, R.; Anderson, K.

    1997-01-01

    The Adaptive Multisensor Integrated Security System (AMISS) uses a variety of computational intelligence techniques to reason from raw sensor data through an array of processing layers to arrive at an assessment for alarm/alert conditions based on human behavior within a secure facility. In this paper, the authors give an overview of the system and briefly describe some of the major components of the system. This system is currently under development and testing in a realistic facility setting

  5. Evaluation of non-linear adaptive smoothing filter by digital phantom

    International Nuclear Information System (INIS)

    Sato, Kazuhiro; Ishiya, Hiroki; Oshita, Ryosuke; Yanagawa, Isao; Goto, Mitsunori; Mori, Issei

    2008-01-01

    As a result of the development of multi-slice CT, diagnoses based on three-dimensional reconstruction images and multi-planar reconstruction have spread. For these applications, which require high z-resolution, thin slice imaging is essential. However, because z-resolution is always based on a trade-off with image noise, thin slice imaging is necessarily accompanied by an increase in noise level. To improve the quality of thin slice images, a non-linear adaptive smoothing filter has been developed, and is being widely applied to clinical use. We developed a digital bar pattern phantom for the purpose of evaluating the effect of this filter and attempted evaluation from an addition image of the bar pattern phantom and the image of the water phantom. The effect of this filter was changed in a complex manner by the contrast and spatial frequency of the original image. We have confirmed the reduced effect of image noise in the low frequency component of the image, but decreased contrast or increased quantity of noise in the image of the high frequency component. This result represents the effect of change in the adaptation of this filter. The digital phantom was useful for this evaluation, but to understand the total effect of filtering, much improvement of the shape of the digital phantom is required. (author)

  6. Combined adaptive multiple subtraction based on optimized event tracing and extended wiener filtering

    Science.gov (United States)

    Tan, Jun; Song, Peng; Li, Jinshan; Wang, Lei; Zhong, Mengxuan; Zhang, Xiaobo

    2017-06-01

    The surface-related multiple elimination (SRME) method is based on feedback formulation and has become one of the most preferred multiple suppression methods used. However, some differences are apparent between the predicted multiples and those in the source seismic records, which may result in conventional adaptive multiple subtraction methods being barely able to effectively suppress multiples in actual production. This paper introduces a combined adaptive multiple attenuation method based on the optimized event tracing technique and extended Wiener filtering. The method firstly uses multiple records predicted by SRME to generate a multiple velocity spectrum, then separates the original record to an approximate primary record and an approximate multiple record by applying the optimized event tracing method and short-time window FK filtering method. After applying the extended Wiener filtering method, residual multiples in the approximate primary record can then be eliminated and the damaged primary can be restored from the approximate multiple record. This method combines the advantages of multiple elimination based on the optimized event tracing method and the extended Wiener filtering technique. It is an ideal method for suppressing typical hyperbolic and other types of multiples, with the advantage of minimizing damage of the primary. Synthetic and field data tests show that this method produces better multiple elimination results than the traditional multi-channel Wiener filter method and is more suitable for multiple elimination in complicated geological areas.

  7. Creating adaptive web recommendation system based on user behavior

    Science.gov (United States)

    Walek, Bogdan

    2018-01-01

    The paper proposes adaptive web recommendation system based on user behavior. The proposed system uses expert system to evaluating and recommending suitable items of content. Relevant items are subsequently evaluated and filtered based on history of visited items and user´s preferred categories of items. Main parts of the proposed system are presented and described. The proposed recommendation system is verified on specific example.

  8. Object-oriented alarm-filtering system

    International Nuclear Information System (INIS)

    Corsberg, D.R.; Wilkie, D.

    1986-01-01

    This paper discusses an alarm-filtering system (AFS) being developed by EG and G Idaho, Inc. for the Advanced Test Reactor (ATR) at the Idaho National Engineering Laboratory. The ultimate goal of this project is to place AFS into ATR's reactor control room to act as an aid during major plant transients. In addition, methods of alarm analysis are investigated based on functional relationships rather than on a historical approach utilizing cause-consequence trees. Artificial intelligence techniques, including object-oriented programming, are also demonstrated as useful in analyzing alarms and alarm sequences. After a brief description of the problem AFS addresses, this paper discusses the design constraints and human factors that influenced the development of the system. The reader is then presented with operational and architectural descriptions of the system as well as what directions the future development of AFS may take. The fact that AFS is being considered as a partial solution to the problems discussed in the next section demonstrates the viability of its underlying technology and approach. 10 refs

  9. Recommendation System for Adaptive Learning.

    Science.gov (United States)

    Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

    2018-01-01

    An adaptive learning system aims at providing instruction tailored to the current status of a learner, differing from the traditional classroom experience. The latest advances in technology make adaptive learning possible, which has the potential to provide students with high-quality learning benefit at a low cost. A key component of an adaptive learning system is a recommendation system, which recommends the next material (video lectures, practices, and so on, on different skills) to the learner, based on the psychometric assessment results and possibly other individual characteristics. An important question then follows: How should recommendations be made? To answer this question, a mathematical framework is proposed that characterizes the recommendation process as a Markov decision problem, for which decisions are made based on the current knowledge of the learner and that of the learning materials. In particular, two plain vanilla systems are introduced, for which the optimal recommendation at each stage can be obtained analytically.

  10. Cleanable sintered metal filters in hot off-gas systems

    International Nuclear Information System (INIS)

    Schurr, G.A.

    1981-01-01

    Filters with sintered metal elements, arranged as tube bundles with backflush air cleaning, are the equivalent of bag filters for high-temperature, harsh environments. They are virtually the only alternative for high-temperature off-gas systems where a renewable, highly efficient particle trap is required. Tests were conducted which show that the sintered metal elements installed in a filter system provide effective powder collection in high-temperature atmospheres over thousands of cleaning cycles. Such a sintered metal filter system is now installed on the experimental defense waste calciner at the Savannah River Laboratory. The experimental results included in this paper were used as the basis for its design

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

    Science.gov (United States)

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

    2018-02-01

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

  12. Decontamination factors of ceramic filter in radioactive waste incineration system

    International Nuclear Information System (INIS)

    Kanbe, Hiromi; Mayuzumi, Masami; Ono, Tetsuo; Yoshiki, Shinya; Kouyama, Hiroaki; Nagae, Madoka; Sekiguchi, Ryosaku; Takaoku, Yoshinobu; Hozumi, Masahiro.

    1987-01-01

    A suspension-firing type radioactive waste incineration system is developed and cold demonstration testing of ceramic filters for the system are carried out. The incineration system, which is useful for a wide variety of waste materials, can serve to simplify the facilities and to reduce the costs for waste disposal. The incineration system can be used for drying-processing of concentrated waste liquids and disposal of flame resistant materials including ion exchange resins and rubber, as well as for ordinary combustible solid materials. An on-line backwash system is adopted to allow the ceramic filters to operate stably for a long period of time. For one-step filtering using the ceramic filter, the decontamination factor is greater than 10 5 for the processing of various wastes. In a practical situation, there exist vapor produced by the spray drier and the cladding in used ion exchange resin, which act to increase the decontamination performance of the ceramic filters to ensure safe operation. For the waste incineration system equipped with a waste gas processing apparatus consisting of a ceramic filter and HEPA filter, the overall decontamination factor is expected to be greater than 10 6 at portions down to the outlet of the ceramic filter and greater than 10 8 at portions down to the outlet of the HEPA filter. (Nogami, K.)

  13. An Adaptive Filtering Algorithm Based on Genetic Algorithm-Backpropagation Network

    Directory of Open Access Journals (Sweden)

    Kai Hu

    2013-01-01

    Full Text Available A new image filtering algorithm is proposed. GA-BPN algorithm uses genetic algorithm (GA to decide weights in a back propagation neural network (BPN. It has better global optimal characteristics than traditional optimal algorithm. In this paper, we used GA-BPN to do image noise filter researching work. Firstly, this paper uses training samples to train GA-BPN as the noise detector. Then, we utilize the well-trained GA-BPN to recognize noise pixels in target image. And at last, an adaptive weighted average algorithm is used to recover noise pixels recognized by GA-BPN. Experiment data shows that this algorithm has better performance than other filters.

  14. Adaptive nonlocal means filtering based on local noise level for CT denoising

    International Nuclear Information System (INIS)

    Li, Zhoubo; Trzasko, Joshua D.; Lake, David S.; Blezek, Daniel J.; Manduca, Armando; Yu, Lifeng; Fletcher, Joel G.; McCollough, Cynthia H.

    2014-01-01

    Purpose: To develop and evaluate an image-domain noise reduction method based on a modified nonlocal means (NLM) algorithm that is adaptive to local noise level of CT images and to implement this method in a time frame consistent with clinical workflow. Methods: A computationally efficient technique for local noise estimation directly from CT images was developed. A forward projection, based on a 2D fan-beam approximation, was used to generate the projection data, with a noise model incorporating the effects of the bowtie filter and automatic exposure control. The noise propagation from projection data to images was analytically derived. The analytical noise map was validated using repeated scans of a phantom. A 3D NLM denoising algorithm was modified to adapt its denoising strength locally based on this noise map. The performance of this adaptive NLM filter was evaluated in phantom studies in terms of in-plane and cross-plane high-contrast spatial resolution, noise power spectrum (NPS), subjective low-contrast spatial resolution using the American College of Radiology (ACR) accreditation phantom, and objective low-contrast spatial resolution using a channelized Hotelling model observer (CHO). Graphical processing units (GPU) implementation of this noise map calculation and the adaptive NLM filtering were developed to meet demands of clinical workflow. Adaptive NLM was piloted on lower dose scans in clinical practice. Results: The local noise level estimation matches the noise distribution determined from multiple repetitive scans of a phantom, demonstrated by small variations in the ratio map between the analytical noise map and the one calculated from repeated scans. The phantom studies demonstrated that the adaptive NLM filter can reduce noise substantially without degrading the high-contrast spatial resolution, as illustrated by modulation transfer function and slice sensitivity profile results. The NPS results show that adaptive NLM denoising preserves the

  15. Accelerometer North Finding System Based on the Wavelet Packet De-noising Algorithm and Filtering Circuit

    Directory of Open Access Journals (Sweden)

    LU Yongle

    2014-07-01

    Full Text Available This paper demonstrates a method and system for north finding with a low-cost piezoelectricity accelerometer based on the Coriolis acceleration principle. The proposed setup is based on the choice of an accelerometer with residual noise of 35 ng•Hz-1/2. The plane of the north finding system is aligned parallel to the local level, which helps to eliminate the effect of plane error. The Coriolis acceleration caused by the earth’s rotation and the acceleration’s instantaneous velocity is much weaker than the g-sensitivity acceleration. To get a high accuracy and a shorter time for north finding system, in this paper, the Filtering Circuit and the wavelet packet de-nosing algorithm are used as the following. First, the hardware is designed as the alternating currents across by filtering circuit, so the DC will be isolated and the weak AC signal will be amplified. The DC is interfering signal generated by the earth's gravity. Then, we have used a wavelet packet to filter the signal which has been done through the filtering circuit. Finally, compare the north finding results measured by wavelet packet filtering with those measured by a low-pass filter. Wavelet filter de-noise data shows that wavelet packet filtering and wavelet filter measurement have high accuracy. Wavelet Packet filtering has stronger ability to remove burst noise and higher engineering environment adaptability than that of Wavelet filtering. Experimental results prove the effectiveness and project implementation of the accelerometer north finding method based on wavelet packet de-noising algorithm.

  16. Detection of arcing location on photovoltaic systems using filters

    Science.gov (United States)

    Johnson, Jay

    2018-02-20

    The present invention relates to photovoltaic systems capable of identifying the location of an arc-fault. In particular, such systems include a unique filter connected to each photovoltaic (PV) string, thereby providing a unique filtered noise profile associated with a particular PV string. Also described herein are methods for identifying and isolating such arc-faults.

  17. ? filtering for stochastic systems driven by Poisson processes

    Science.gov (United States)

    Song, Bo; Wu, Zheng-Guang; Park, Ju H.; Shi, Guodong; Zhang, Ya

    2015-01-01

    This paper investigates the ? filtering problem for stochastic systems driven by Poisson processes. By utilising the martingale theory such as the predictable projection operator and the dual predictable projection operator, this paper transforms the expectation of stochastic integral with respect to the Poisson process into the expectation of Lebesgue integral. Then, based on this, this paper designs an ? filter such that the filtering error system is mean-square asymptotically stable and satisfies a prescribed ? performance level. Finally, a simulation example is given to illustrate the effectiveness of the proposed filtering scheme.

  18. Sealing Clay Text Segmentation Based on Radon-Like Features and Adaptive Enhancement Filters

    Directory of Open Access Journals (Sweden)

    Xia Zheng

    2015-01-01

    Full Text Available Text extraction is a key issue in sealing clay research. The traditional method based on rubbings increases the risk of sealing clay damage and is unfavorable to sealing clay protection. Therefore, using digital image of sealing clay, a new method for text segmentation based on Radon-like features and adaptive enhancement filters is proposed in this paper. First, adaptive enhancement LM filter bank is used to get the maximum energy image; second, the edge image of the maximum energy image is calculated; finally, Radon-like feature images are generated by combining maximum energy image and its edge image. The average image of Radon-like feature images is segmented by the image thresholding method. Compared with 2D Otsu, GA, and FastFCM, the experiment result shows that this method can perform better in terms of accuracy and completeness of the text.

  19. Implementation of high-speed–low-power adaptive finite impulse response filter with novel architecture

    Directory of Open Access Journals (Sweden)

    Manish Jaiswal

    2015-03-01

    Full Text Available An energy efficient high-speed adaptive finite impulse response filter with novel architecture is developed. Synthesis results along with novel architecture on different complementary metal–oxide semiconductor (CMOS families are presented. Analysis is performed using Artix-7, Spartan-6 and Virtex-4 for most popular adaptive least mean square filter for different orders such as N = 8, 16, 32. The presented work is done using MATLAB (2013b and Xilinx (14.2. From the synthesis results, it can be found that CMOS (28 nm achieves the lowest power and critical path delay compared to others, and thus proves its efficiency in terms of energy. Different parameters are considered such as look up tables and input–output blocks, along with their optimised results.

  20. State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter

    Directory of Open Access Journals (Sweden)

    Bizhong Xia

    2015-06-01

    Full Text Available Accurate state of charge (SOC estimation is of great significance for a lithium-ion battery to ensure its safe operation and to prevent it from over-charging or over-discharging. However, it is difficult to get an accurate value of SOC since it is an inner sate of a battery cell, which cannot be directly measured. This paper presents an Adaptive Cubature Kalman filter (ACKF-based SOC estimation algorithm for lithium-ion batteries in electric vehicles. Firstly, the lithium-ion battery is modeled using the second-order resistor-capacitor (RC equivalent circuit and parameters of the battery model are determined by the forgetting factor least-squares method. Then, the Adaptive Cubature Kalman filter for battery SOC estimation is introduced and the estimated process is presented. Finally, two typical driving cycles, including the Dynamic Stress Test (DST and New European Driving Cycle (NEDC are applied to evaluate the performance of the proposed method by comparing with the traditional extended Kalman filter (EKF and cubature Kalman filter (CKF algorithms. Experimental results show that the ACKF algorithm has better performance in terms of SOC estimation accuracy, convergence to different initial SOC errors and robustness against voltage measurement noise as compared with the traditional EKF and CKF algorithms.

  1. Fast digital zooming system using directionally adaptive image interpolation and restoration.

    Science.gov (United States)

    Kang, Wonseok; Jeon, Jaehwan; Yu, Soohwan; Paik, Joonki

    2014-01-01

    This paper presents a fast digital zooming system for mobile consumer cameras using directionally adaptive image interpolation and restoration methods. The proposed interpolation algorithm performs edge refinement along the initially estimated edge orientation using directionally steerable filters. Either the directionally weighted linear or adaptive cubic-spline interpolation filter is then selectively used according to the refined edge orientation for removing jagged artifacts in the slanted edge region. A novel image restoration algorithm is also presented for removing blurring artifacts caused by the linear or cubic-spline interpolation using the directionally adaptive truncated constrained least squares (TCLS) filter. Both proposed steerable filter-based interpolation and the TCLS-based restoration filters have a finite impulse response (FIR) structure for real time processing in an image signal processing (ISP) chain. Experimental results show that the proposed digital zooming system provides high-quality magnified images with FIR filter-based fast computational structure.

  2. Rate Adaptive OFDMA Communication Systems

    International Nuclear Information System (INIS)

    Abdelhakim, M.M.M.

    2009-01-01

    Due to the varying nature of the wireless channels, adapting the transmission parameters, such as code rate, modulation order and power, in response to the channel variations provides a significant improvement in the system performance. In the OFDM systems, Per-Frame adaptation (PFA) can be employed where the transmission variables are fixed over a given frame and may change from one frame to the other. Subband (tile) loading offers more degrees of adaptation such that each group of carriers (subband) uses the same transmission parameters and different subbands may use different parameters. Changing the code rate for each tile in the same frame, results in transmitting multiple codewords (MCWs) for a single frame. In this thesis a scheme is proposed for adaptively changing the code rate of coded OFDMA systems via changing the puncturing rate within a single codeword (SCW). In the proposed structure, the data is encoded with the lowest available code rate then it is divided among the different tiles where it is punctured adaptively based on some measure of the channel quality for each tile. The proposed scheme is compared against using multiple codewords (MCWs) where the different code rates for the tiles are obtained using separate encoding processes. For bit interleaved coded modulation architecture two novel interleaving methods are proposed, namely the puncturing dependant interleaver (PDI) and interleaved puncturing (IntP), which provide larger interleaving depth. In the PDI method the coded bits with the same rate over different tiles are grouped for interleaving. In IntP structure the interleaving is performed prior to puncturing. The performance of the adaptive puncturing technique is investigated under constant bit rate constraint and variable bit rate. Two different adaptive modulation and coding (AMC) selection methods are examined for variable bit rate adaptive system. The first is a recursive scheme that operates directly on the SNR whereas the second

  3. FOG Random Drift Signal Denoising Based on the Improved AR Model and Modified Sage-Husa Adaptive Kalman Filter.

    Science.gov (United States)

    Sun, Jin; Xu, Xiaosu; Liu, Yiting; Zhang, Tao; Li, Yao

    2016-07-12

    In order to reduce the influence of fiber optic gyroscope (FOG) random drift error on inertial navigation systems, an improved auto regressive (AR) model is put forward in this paper. First, based on real-time observations at each restart of the gyroscope, the model of FOG random drift can be established online. In the improved AR model, the FOG measured signal is employed instead of the zero mean signals. Then, the modified Sage-Husa adaptive Kalman filter (SHAKF) is introduced, which can directly carry out real-time filtering on the FOG signals. Finally, static and dynamic experiments are done to verify the effectiveness. The filtering results are analyzed with Allan variance. The analysis results show that the improved AR model has high fitting accuracy and strong adaptability, and the minimum fitting accuracy of single noise is 93.2%. Based on the improved AR(3) model, the denoising method of SHAKF is more effective than traditional methods, and its effect is better than 30%. The random drift error of FOG is reduced effectively, and the precision of the FOG is improved.

  4. Detecting an atomic clock frequency anomaly using an adaptive Kalman filter algorithm

    Science.gov (United States)

    Song, Huijie; Dong, Shaowu; Wu, Wenjun; Jiang, Meng; Wang, Weixiong

    2018-06-01

    The abnormal frequencies of an atomic clock mainly include frequency jump and frequency drift jump. Atomic clock frequency anomaly detection is a key technique in time-keeping. The Kalman filter algorithm, as a linear optimal algorithm, has been widely used in real-time detection for abnormal frequency. In order to obtain an optimal state estimation, the observation model and dynamic model of the Kalman filter algorithm should satisfy Gaussian white noise conditions. The detection performance is degraded if anomalies affect the observation model or dynamic model. The idea of the adaptive Kalman filter algorithm, applied to clock frequency anomaly detection, uses the residuals given by the prediction for building ‘an adaptive factor’ the prediction state covariance matrix is real-time corrected by the adaptive factor. The results show that the model error is reduced and the detection performance is improved. The effectiveness of the algorithm is verified by the frequency jump simulation, the frequency drift jump simulation and the measured data of the atomic clock by using the chi-square test.

  5. Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach

    Science.gov (United States)

    Chen, Chaochao; Vachtsevanos, George; Orchard, Marcos E.

    2012-04-01

    Machine prognosis can be considered as the generation of long-term predictions that describe the evolution in time of a fault indicator, with the purpose of estimating the remaining useful life (RUL) of a failing component/subsystem so that timely maintenance can be performed to avoid catastrophic failures. This paper proposes an integrated RUL prediction method using adaptive neuro-fuzzy inference systems (ANFIS) and high-order particle filtering, which forecasts the time evolution of the fault indicator and estimates the probability density function (pdf) of RUL. The ANFIS is trained and integrated in a high-order particle filter as a model describing the fault progression. The high-order particle filter is used to estimate the current state and carry out p-step-ahead predictions via a set of particles. These predictions are used to estimate the RUL pdf. The performance of the proposed method is evaluated via the real-world data from a seeded fault test for a UH-60 helicopter planetary gear plate. The results demonstrate that it outperforms both the conventional ANFIS predictor and the particle-filter-based predictor where the fault growth model is a first-order model that is trained via the ANFIS.

  6. In-situ testing of HEPA filters in the nuclear Karlsruhe filter system

    International Nuclear Information System (INIS)

    Ohlmeyer, M.; Stotz, W.

    1977-01-01

    Nuclear plant operators and filter manufacturers are endeavouring to improve environmental protection by intensifying process control and/or improving filter quality. In-situ testing is an important element in these efforts since it represents a direct means of checking the success or otherwise of a particular development. The arrangements for in-situ testing should satisfy the following minimum requirements: the staff should not be exposed to risk during the test; the test method should be objective and reproducible as well as being as sensitive as possible; the test method should permit detection of individual leaks in the filter system so that they can be remedied as efficiently as possible; the test equipment should not necessitate modifications to the extract systems or plant construction; the test should be simple and capable of being carried out with a minimum of effort and equipment. GfK has developed the 'Nuclear-Karlsruhe' filter housing in accordance with these principles. This housing permits in-situ testing similar to the DIN 24184 visual oil-fog test or the DOP test. External visual checks on the general condition of the filter is also possible. A safe system of filter changing with a specially designed plastic bag attachment at an accessible height considerably increases the degree of protection of operating personnel

  7. Kalman Filtering for Delayed Singular Systems with Multiplicative Noise

    Institute of Scientific and Technical Information of China (English)

    Xiao Lu; Linglong Wang; Haixia Wang; Xianghua Wang

    2016-01-01

    Kalman filtering problem for singular systems is dealt with,where the measurements consist of instantaneous measurements and delayed ones,and the plant includes multiplicative noise.By utilizing standard singular value decomposition,the restricted equivalent delayed system is presented,and the Kalman filters for the restricted equivalent system are given by using the well-known re-organization of innovation analysis lemma.The optimal Kalman filter for the original system is given based on the above Kalman filter by recursive Riccati equations,and a numerical example is presented to show the validity and efficiency of the proposed approach,where the comparison between the filter and predictor is also given.

  8. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles

    International Nuclear Information System (INIS)

    Sun, Fengchun; Hu, Xiaosong; Zou, Yuan; Li, Siguang

    2011-01-01

    An accurate battery State of Charge estimation is of great significance for battery electric vehicles and hybrid electric vehicles. This paper presents an adaptive unscented Kalman filtering method to estimate State of Charge of a lithium-ion battery for battery electric vehicles. The adaptive adjustment of the noise covariances in the State of Charge estimation process is implemented by an idea of covariance matching in the unscented Kalman filter context. Experimental results indicate that the adaptive unscented Kalman filter-based algorithm has a good performance in estimating the battery State of Charge. A comparison with the adaptive extended Kalman filter, extended Kalman filter, and unscented Kalman filter-based algorithms shows that the proposed State of Charge estimation method has a better accuracy. -- Highlights: → Adaptive unscented Kalman filtering is proposed to estimate State of Charge of a lithium-ion battery for electric vehicles. → The proposed method has a good performance in estimating the battery State of Charge. → A comparison with three other Kalman filtering algorithms shows that the proposed method has a better accuracy.

  9. Generic adaptation framework for unifying adaptive web-based systems

    NARCIS (Netherlands)

    Knutov, E.

    2012-01-01

    The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the

  10. Ash reduction system using electrically heated particulate matter filter

    Science.gov (United States)

    Gonze, Eugene V [Pinckney, MI; Paratore, Jr., Michael J; He, Yongsheng [Sterling Heights, MI

    2011-08-16

    A control system for reducing ash comprises a temperature estimator module that estimates a temperature of an electrically heated particulate matter (PM) filter. A temperature and position estimator module estimates a position and temperature of an oxidation wave within the electrically heated PM filter. An ash reduction control module adjusts at least one of exhaust flow, fuel and oxygen levels in the electrically heated PM filter to adjust a position of the oxidation wave within the electrically heated PM filter based on the oxidation wave temperature and position.

  11. A new extended H∞ filter for discrete nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    张永安; 周荻; 段广仁

    2004-01-01

    Nonlinear estimation problem is investigated in this paper. By extension of a linear H∞ estimation with corrector-predictor form to nonlinear cases, a new extended H∞ filter is proposed for time-varying discretetime nonlinear systems. The new filter has a simple observer structure based on a local linearization model, and can be viewed as a general case of the extended Kalman filter (EKF). An example demonstrates that the new filter with a suitable-chosen prescribed H∞ bound performs better than the EKF.

  12. Filter aids influence on pressure drop across a filtration system

    Science.gov (United States)

    Hajar, S.; Rashid, M.; Nurnadia, A.; Ammar, M. R.; Hasfalina, C. M.

    2017-06-01

    Filter aids is commonly used to reduce pressure drop across air filtration system as it helps to increase the efficiency of filtration of accumulated filter cake. Filtration velocity is one of the main parameters that affect the performance of filter aids material. In this study, a formulated filter aids consisting of PreKot™ and activated carbon mixture (designated as PrekotAC) was tested on PTFE filter media under various filtration velocities of 5, 6, and 8 m/min at a constant material loading of 0.2 mg/mm2. Results showed that pressure drop is highly influenced by filtration velocity where higher filtration velocity leads to a higher pressure drop across the filter cake. It was found that PrekotAC performed better in terms of reducing the pressure drop across the filter cake even at the highest filtration velocity. The diversity in different particle size distribution of non-uniform particle size in the formulated PrekotAC mixture presents a higher permeability causes a lower pressure drop across the accumulated filter cake. The finding suggests that PrekotAC is a promising filter aids material that helps reducing the pressure drop across fabric filtration system.

  13. Intelligent Optical Systems Using Adaptive Optics

    Science.gov (United States)

    Clark, Natalie

    2012-01-01

    Until recently, the phrase adaptive optics generally conjured images of large deformable mirrors being integrated into telescopes to compensate for atmospheric turbulence. However, the development of smaller, cheaper devices has sparked interest for other aerospace and commercial applications. Variable focal length lenses, liquid crystal spatial light modulators, tunable filters, phase compensators, polarization compensation, and deformable mirrors are becoming increasingly useful for other imaging applications including guidance navigation and control (GNC), coronagraphs, foveated imaging, situational awareness, autonomous rendezvous and docking, non-mechanical zoom, phase diversity, and enhanced multi-spectral imaging. The active components presented here allow flexibility in the optical design, increasing performance. In addition, the intelligent optical systems presented offer advantages in size and weight and radiation tolerance.

  14. Adaptive, dynamic, and resilient systems

    CERN Document Server

    Suri, Niranjan

    2015-01-01

    As the complexity of today's networked computer systems grows, they become increasingly difficult to understand, predict, and control. Addressing these challenges requires new approaches to building these systems. Adaptive, Dynamic, and Resilient Systems supplies readers with various perspectives of the critical infrastructure that systems of networked computers rely on. It introduces the key issues, describes their interrelationships, and presents new research in support of these areas.The book presents the insights of a different group of international experts in each chapter. Reporting on r

  15. An Oversampled Filter Bank Multicarrier System for cognitive Radio

    NARCIS (Netherlands)

    Kokkeler, Andre B.J.; Smit, Gerardus Johannes Maria; Zhang, Q; Zhang, Q.

    2008-01-01

    Due to small sideband power leakage, filter bank multicarrier techniques are considered as interesting alternatives to traditional OFDMs for spectrum pooling Cognitive Radio. In this paper, we propose an oversampled filter bank multicarrier system for Cognitive Radio. The increased spacing between

  16. Ship detection for high resolution optical imagery with adaptive target filter

    Science.gov (United States)

    Ju, Hongbin

    2015-10-01

    Ship detection is important due to both its civil and military use. In this paper, we propose a novel ship detection method, Adaptive Target Filter (ATF), for high resolution optical imagery. The proposed framework can be grouped into two stages, where in the first stage, a test image is densely divided into different detection windows and each window is transformed to a feature vector in its feature space. The Histograms of Oriented Gradients (HOG) is accumulated as a basic feature descriptor. In the second stage, the proposed ATF highlights all the ship regions and suppresses the undesired backgrounds adaptively. Each detection window is assigned a score, which represents the degree of the window belonging to a certain ship category. The ATF can be adaptively obtained by the weighted Logistic Regression (WLR) according to the distribution of backgrounds and targets of the input image. The main innovation of our method is that we only need to collect positive training samples to build the filter, while the negative training samples are adaptively generated by the input image. This is different to other classification method such as Support Vector Machine (SVM) and Logistic Regression (LR), which need to collect both positive and negative training samples. The experimental result on 1-m high resolution optical images shows the proposed method achieves a desired ship detection performance with higher quality and robustness than other methods, e.g., SVM and LR.

  17. An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps

    Directory of Open Access Journals (Sweden)

    Tongyang LI

    2018-05-01

    Full Text Available An accurate estimation of the remaining useful life (RUL not only contributes to an effective application of an aviation piston pump, but also meets the necessity of condition based maintenance (CBM. For the current RUL evaluation methods, a model-based method is inappropriate for the degradation process of an aviation piston pump due to difficulties of modeling, while a data-based method rarely presents high-accuracy prediction in a long period of time. In this work, an adaptive-order particle filter (AOPF prognostic process is proposed aiming at improving long-term prediction accuracy of RUL by combining both kinds of methods. A dynamic model is initialized by a data-driven or empirical method. When a new observation comes, the prior state distribution is approximated by a current model. The order of the current model is updated adaptively by fusing the information of the observation. Monte Carlo simulation is employed for estimating the posterior probability density function of future states of the pump’s degradation. With updating the order number adaptively, the method presents a higher precision in contrast with those of traditional methods. In a case study, the proposed AOPF method is adopted to forecast the degradation status of an aviation piston pump with experimental return oil flow data, and the analytical results show the effectiveness of the proposed AOPF method. Keywords: Adaptive prognosis, Condition based maintenance (CBM, Particle filter (PF, Piston pump, Remaining useful life (RUL

  18. Power adaptive multi-filter carrierless amplitude and phase access scheme for visible light communication network

    Science.gov (United States)

    Li, Wei; Huang, Zhitong; Li, Haoyue; Ji, Yuefeng

    2018-04-01

    Visible light communication (VLC) is a promising candidate for short-range broadband access due to its integration of advantages for both optical communication and wireless communication, whereas multi-user access is a key problem because of the intra-cell and inter-cell interferences. In addition, the non-flat channel effect results in higher losses for users in high frequency bands, which leads to unfair qualities. To solve those issues, we propose a power adaptive multi-filter carrierless amplitude and phase access (PA-MF-CAPA) scheme, and in the first step of this scheme, the MF-CAPA scheme utilizing multiple filters as different CAP dimensions is used to realize multi-user access. The character of orthogonality among the filters in different dimensions can mitigate the effect of intra-cell and inter-cell interferences. Moreover, the MF-CAPA scheme provides different channels modulated on the same frequency bands, which further increases the transmission rate. Then, the power adaptive procedure based on MF-CAPA scheme is presented to realize quality fairness. As demonstrated in our experiments, the MF-CAPA scheme yields an improved throughput compared with multi-band CAP access scheme, and the PA-MF-CAPA scheme enhances the quality fairness and further improves the throughput compared with the MF-CAPA scheme.

  19. Processing-Efficient Distributed Adaptive RLS Filtering for Computationally Constrained Platforms

    Directory of Open Access Journals (Sweden)

    Noor M. Khan

    2017-01-01

    Full Text Available In this paper, a novel processing-efficient architecture of a group of inexpensive and computationally incapable small platforms is proposed for a parallely distributed adaptive signal processing (PDASP operation. The proposed architecture runs computationally expensive procedures like complex adaptive recursive least square (RLS algorithm cooperatively. The proposed PDASP architecture operates properly even if perfect time alignment among the participating platforms is not available. An RLS algorithm with the application of MIMO channel estimation is deployed on the proposed architecture. Complexity and processing time of the PDASP scheme with MIMO RLS algorithm are compared with sequentially operated MIMO RLS algorithm and liner Kalman filter. It is observed that PDASP scheme exhibits much lesser computational complexity parallely than the sequential MIMO RLS algorithm as well as Kalman filter. Moreover, the proposed architecture provides an improvement of 95.83% and 82.29% decreased processing time parallely compared to the sequentially operated Kalman filter and MIMO RLS algorithm for low doppler rate, respectively. Likewise, for high doppler rate, the proposed architecture entails an improvement of 94.12% and 77.28% decreased processing time compared to the Kalman and RLS algorithms, respectively.

  20. Study on vent containment filtering for the Spanish NPPS systems

    International Nuclear Information System (INIS)

    Peinado, A.; Serrano, C.; Garcia-Serrano, J. L.

    2013-01-01

    The study discusses filtering systems on the market, and its suppliers, taking into account aspects such as ease of integration into the current plant design, characteristics of the process of filtering, operational range, autonomy of the system, maintenance, qualification and proven experiences, among others. The study, also contains an analysis of sequences kind of accident that serve to define the design parameters of the system.

  1. Hybrid Adaptive Filter development for the minimisation of transient fluctuations superimposed on electrotelluric field recordings mainly by magnetic storms

    Directory of Open Access Journals (Sweden)

    A. Konstantaras

    2006-01-01

    Full Text Available The method of Hybrid Adaptive Filtering (HAF aims to recover the recorded electric field signals from anomalies of magnetotelluric origin induced mainly by magnetic storms. An adaptive filter incorporating neuro-fuzzy technology has been developed to remove any significant distortions from the equivalent magnetic field signal, as retrieved from the original electric field signal by reversing the magnetotelluric method. Testing with further unseen data verifies the reliability of the model and demonstrates the effectiveness of the HAF method.

  2. Using high-order methods on adaptively refined block-structured meshes - discretizations, interpolations, and filters.

    Energy Technology Data Exchange (ETDEWEB)

    Ray, Jaideep; Lefantzi, Sophia; Najm, Habib N.; Kennedy, Christopher A.

    2006-01-01

    Block-structured adaptively refined meshes (SAMR) strive for efficient resolution of partial differential equations (PDEs) solved on large computational domains by clustering mesh points only where required by large gradients. Previous work has indicated that fourth-order convergence can be achieved on such meshes by using a suitable combination of high-order discretizations, interpolations, and filters and can deliver significant computational savings over conventional second-order methods at engineering error tolerances. In this paper, we explore the interactions between the errors introduced by discretizations, interpolations and filters. We develop general expressions for high-order discretizations, interpolations, and filters, in multiple dimensions, using a Fourier approach, facilitating the high-order SAMR implementation. We derive a formulation for the necessary interpolation order for given discretization and derivative orders. We also illustrate this order relationship empirically using one and two-dimensional model problems on refined meshes. We study the observed increase in accuracy with increasing interpolation order. We also examine the empirically observed order of convergence, as the effective resolution of the mesh is increased by successively adding levels of refinement, with different orders of discretization, interpolation, or filtering.

  3. Financial markets as adaptive systems

    Science.gov (United States)

    Potters, M.; Cont, R.; Bouchaud, J.-P.

    1998-02-01

    We show, by studying in detail the market prices of options on liquid markets, that the market has empirically corrected the simple, but inadequate Black-Scholes formula to account for two important statistical features of asset fluctuations: "fat tails" and correlations in the scale of fluctuations. These aspects, although not included in the pricing models, are very precisely reflected in the price fixed by the market as a whole. Financial markets thus behave as rather efficient adaptive systems.

  4. The ''Nuclear-Karlsruhe'' air-filter system

    International Nuclear Information System (INIS)

    Berliner, P.; Ohlmeyer, M.; Stotz, W.

    1976-01-01

    Increasing requirements for exhaust-air filter systems used in nuclear facilities induced the Gesellschaft fuer Kernforschung to develop the ''Nuclear-Karlsruhe'' HEPA filter system. This novel development has profited by experience gained in previous incidents as well as by maitenance and decontamination work performed with different HEPA filter systems. The proved ''Nuclear-Karlsruhe'' system takes equally into account the demands for optimum safety, maximum efficiency and economy, and is distinguished by the following features: (1) The air current is defected by 180 0 in the casing. Deflection causes quite a number of improvements, results in substantial reduction of space requirements, and avoids the dispersion of pollutants to the clean-air side. Besides, the HEPA filter is protected from damage by condensed particles or foreign materials entrained; (2) The ''Nuclear-Karlsruhe'' system allows gas-tight filter replacement. Special replacement collars have been provided at the casing, which allow the tight fastening of replacement bags which are self-locking. (3) In-place testing in the operating condition can be carried out very conveniently because the air is deflected. Minimum leaks in the filter medium or in the filter gasket can be detected by the high-sensitivity visual oil-thread test, which makes leaks distinctly visible as oil mist threads through a transparent front window provided on the clean-air side. The test takes only some minutes and its sensitivity is hardly matched by any other technique. (4) The clamping mechanism is installed outside the casing, i.e. outside the polluted or aggressive media. The contact force is spring-loaded absolutely uniformly to the circular filter gasket. (5) For practical and econmic reasons the filter casings can be locked individually so as to be gas-tight. (6) The entire system is made of stainless or coated steel and metal parts which are corrosion and fire-resistant. (author)

  5. Selection vector filter framework

    Science.gov (United States)

    Lukac, Rastislav; Plataniotis, Konstantinos N.; Smolka, Bogdan; Venetsanopoulos, Anastasios N.

    2003-10-01

    We provide a unified framework of nonlinear vector techniques outputting the lowest ranked vector. The proposed framework constitutes a generalized filter class for multichannel signal processing. A new class of nonlinear selection filters are based on the robust order-statistic theory and the minimization of the weighted distance function to other input samples. The proposed method can be designed to perform a variety of filtering operations including previously developed filtering techniques such as vector median, basic vector directional filter, directional distance filter, weighted vector median filters and weighted directional filters. A wide range of filtering operations is guaranteed by the filter structure with two independent weight vectors for angular and distance domains of the vector space. In order to adapt the filter parameters to varying signal and noise statistics, we provide also the generalized optimization algorithms taking the advantage of the weighted median filters and the relationship between standard median filter and vector median filter. Thus, we can deal with both statistical and deterministic aspects of the filter design process. It will be shown that the proposed method holds the required properties such as the capability of modelling the underlying system in the application at hand, the robustness with respect to errors in the model of underlying system, the availability of the training procedure and finally, the simplicity of filter representation, analysis, design and implementation. Simulation studies also indicate that the new filters are computationally attractive and have excellent performance in environments corrupted by bit errors and impulsive noise.

  6. Rotating Ceramic Water Filter Discs System for Water Filtration

    Directory of Open Access Journals (Sweden)

    Riyadh Z. Al Zubaidy

    2017-04-01

    Full Text Available This work aimed to design, construct and operate a new laboratory scale water filtration system. This system was used to examine the efficiency of two ceramic filter discs as a medium for water filtration. These filters were made from two different ceramic mixtures of local red clay, sawdust, and water. The filtration system was designed with two rotating interfered modules of these filters. Rotating these modules generates shear force between water and the surfaces of filter discs of the filtration modules that works to reduce thickness of layer of rejected materials on the filters surfaces. Each module consists of seven filtration units and each unit consists of two ceramic filter discs. The average measured hydraulic conductivity of the first module was 13.7mm/day and that for the second module was 50mm/day. Results showed that the water filtration system can be operated continuously with a constant flow rate and the filtration process was controlled by a skin thin layer of rejected materials. The ceramic water filters of both filtration modules have high removal efficiency of total suspended solids up to 100% and of turbidity up to 99.94%.

  7. Computerized adaptive testing item selection in computerized adaptive learning systems

    NARCIS (Netherlands)

    Eggen, Theodorus Johannes Hendrikus Maria; Eggen, T.J.H.M.; Veldkamp, B.P.

    2012-01-01

    Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item

  8. Linear filtering of systems with memory and application to finance

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available We study the linear filtering problem for systems driven by continuous Gaussian processes V ( 1 and V ( 2 with memory described by two parameters. The processes V ( j have the virtue that they possess stationary increments and simple semimartingale representations simultaneously. They allow for straightforward parameter estimations. After giving the semimartingale representations of V ( j by innovation theory, we derive Kalman-Bucy-type filtering equations for the systems. We apply the result to the optimal portfolio problem for an investor with partial observations. We illustrate the tractability of the filtering algorithm by numerical implementations.

  9. Adsorption of Chloroform by the Rapid Response System Filter

    National Research Council Canada - National Science Library

    Karwacki, Christopher

    1997-01-01

    Adsorption equilibria and dynamic breakthrough data were measured to determine the adsorption capacity and effect of purge air on the desorption of chloroform from activated carbon simulating the Rapid Response System (RRS) filter...

  10. Online event filtering in the JADE data acquisition system

    International Nuclear Information System (INIS)

    Mills, H.E.

    1986-01-01

    The data acquisition system developed for the JADE experiment at PETRA, DESY includes the facility to use software to filter out background events. The design, implementation, testing and experience gained are discussed. (orig.)

  11. Load compensation for single phase system using series active filter ...

    African Journals Online (AJOL)

    Load compensation for single phase system using series active filter. ... KK Mishra, R Gupta ... load varies from time to time, the non linear load ranging from voltage source type harmonic load (VSHL) dominant to current source type harmonic ...

  12. Adaptive Intrusion Data System (AIDS)

    International Nuclear Information System (INIS)

    Corlis, N.E.

    1980-05-01

    The adaptive intrusion data system (AIDS) was developed to collect data from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique data system which uses computer controlled data systems, video cameras and recorders, analog-to-digital conversion, environmental sensors, and digital recorders to collect sensor data. The data can be viewed either manually or with a special computerized data-reduction system which adds new data to a data base stored on a magnetic disc recorder. This report provides a synoptic account of the AIDS as it presently exists. Modifications to the purchased subsystems are described, and references are made to publications which describe the Sandia-designed subsystems

  13. Certification Considerations for Adaptive Systems

    Science.gov (United States)

    Bhattacharyya, Siddhartha; Cofer, Darren; Musliner, David J.; Mueller, Joseph; Engstrom, Eric

    2015-01-01

    Advanced capabilities planned for the next generation of aircraft, including those that will operate within the Next Generation Air Transportation System (NextGen), will necessarily include complex new algorithms and non-traditional software elements. These aircraft will likely incorporate adaptive control algorithms that will provide enhanced safety, autonomy, and robustness during adverse conditions. Unmanned aircraft will operate alongside manned aircraft in the National Airspace (NAS), with intelligent software performing the high-level decision-making functions normally performed by human pilots. Even human-piloted aircraft will necessarily include more autonomy. However, there are serious barriers to the deployment of new capabilities, especially for those based upon software including adaptive control (AC) and artificial intelligence (AI) algorithms. Current civil aviation certification processes are based on the idea that the correct behavior of a system must be completely specified and verified prior to operation. This report by Rockwell Collins and SIFT documents our comprehensive study of the state of the art in intelligent and adaptive algorithms for the civil aviation domain, categorizing the approaches used and identifying gaps and challenges associated with certification of each approach.

  14. A paired wedge filter system for compensation in dose differences

    International Nuclear Information System (INIS)

    Kobayashi, H.; Sakurai, Y.; Kondo, S.; Abe, S.; Hayakawa, N.; Aoyama, Y.; Obata, Y.; Ishigaki, T.

    1998-01-01

    Objective: In radiotherapy, it is important to conform the high dose volume to the planned target volume. A variable thickness paired wedge filter system was developed to compensate for dose inhomogeneity arising from field width segment variation in conformal irradiation. Materials and methods: The present study used a 6 MV linear accelerator equipped with multileaf collimator leaves and a paired wedge compensating filter system. The dose variation due to field width was measured in each field segment width. The variation in attenuation of the compensators was measured as a function of filter position. As the field width increases, the relative absorbed dose also increases; this is the point of requiring compensation, so it can be in reverse proportion. Results: As the field width increases, the relative absorbed dose also increases; this is why compensation is required and thus it must be in reverse proportion. Attenuation of the absorbed dose by the paired filters was in proportion to the filter position. The filter position to compensate for the difference of absorbed doses was defined by the square root of the field width. For a field varying in width from 4 to 16 cm, the variation in the absorbed dose across the field was reduced from 12% to 2.7%. Conclusion: This paired wedge filter system reduced absorbed dose variations across multileaf collimator shaped fields and can facilitate treatment planning in conformal therapy. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

  15. Adaptive Kalman filtering for histogram-based appearance learning in infrared imagery.

    Science.gov (United States)

    Venkataraman, Vijay; Fan, Guoliang; Havlicek, Joseph P; Fan, Xin; Zhai, Yan; Yeary, Mark B

    2012-11-01

    Targets of interest in video acquired from imaging infrared sensors often exhibit profound appearance variations due to a variety of factors, including complex target maneuvers, ego-motion of the sensor platform, background clutter, etc., making it difficult to maintain a reliable detection process and track lock over extended time periods. Two key issues in overcoming this problem are how to represent the target and how to learn its appearance online. In this paper, we adopt a recent appearance model that estimates the pixel intensity histograms as well as the distribution of local standard deviations in both the foreground and background regions for robust target representation. Appearance learning is then cast as an adaptive Kalman filtering problem where the process and measurement noise variances are both unknown. We formulate this problem using both covariance matching and, for the first time in a visual tracking application, the recent autocovariance least-squares (ALS) method. Although convergence of the ALS algorithm is guaranteed only for the case of globally wide sense stationary process and measurement noises, we demonstrate for the first time that the technique can often be applied with great effectiveness under the much weaker assumption of piecewise stationarity. The performance advantages of the ALS method relative to the classical covariance matching are illustrated by means of simulated stationary and nonstationary systems. Against real data, our results show that the ALS-based algorithm outperforms the covariance matching as well as the traditional histogram similarity-based methods, achieving sub-pixel tracking accuracy against the well-known AMCOM closure sequences and the recent SENSIAC automatic target recognition dataset.

  16. Adaptive wave filtering for dynamic positioning of marine vessels using maximum likelihood identification: Theory and experiments

    Digital Repository Service at National Institute of Oceanography (India)

    Hassani, V.; Sorensen, A.J.; Pascoal, A.M.

    This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The dynamic model of the vessel captures explicitly the sea state as an uncertain...

  17. A novel methodology for adaptive wave filtering of marine vessels: Theory and experiments

    Digital Repository Service at National Institute of Oceanography (India)

    Hassani, V.; Pascoal, A.M.; Sorensen, A.J.

    This paper addresses a filtering problem that arises in the design of dynamic positioning systems for ships and offshore rigs subjected to the influence of sea waves. The vessel`s dynamic model adopted captures the sea state as an uncertain...

  18. Temporal Scalability through Adaptive -Band Filter Banks for Robust H.264/MPEG-4 AVC Video Coding

    Directory of Open Access Journals (Sweden)

    Pau G

    2006-01-01

    Full Text Available This paper presents different structures that use adaptive -band hierarchical filter banks for temporal scalability. Open-loop and closed-loop configurations are introduced and illustrated using existing video codecs. In particular, it is shown that the H.264/MPEG-4 AVC codec allows us to introduce scalability by frame shuffling operations, thus keeping backward compatibility with the standard. The large set of shuffling patterns introduced here can be exploited to adapt the encoding process to the video content features, as well as to the user equipment and transmission channel characteristics. Furthermore, simulation results show that this scalability is obtained with no degradation in terms of subjective and objective quality in error-free environments, while in error-prone channels the scalable versions provide increased robustness.

  19. Design of 2-D Recursive Filters Using Self-adaptive Mutation Differential Evolution Algorithm

    Directory of Open Access Journals (Sweden)

    Lianghong Wu

    2011-08-01

    Full Text Available This paper investigates a novel approach to the design of two-dimensional recursive digital filters using differential evolution (DE algorithm. The design task is reformulated as a constrained minimization problem and is solved by an Self-adaptive Mutation DE algorithm (SAMDE, which adopts an adaptive mutation operator that combines with the advantages of the DE/rand/1/bin strategy and the DE/best/2/bin strategy. As a result, its convergence performance is improved greatly. Numerical experiment results confirm the conclusion. The proposedSAMDE approach is effectively applied to test a numerical example and is compared with previous design methods. The computational experiments show that the SAMDE approach can obtain better results than previous design methods.

  20. Robust filtering and fault detection of switched delay systems

    CERN Document Server

    Wang, Dong; Wang, Wei

    2013-01-01

    Switched delay systems appear in a wide field of applications including networked control systems, power systems, memristive systems. Though the large amount of ideas with respect to such systems have generated, until now, it still lacks a framework to focus on filter design and fault detection issues which are relevant to life safety and property loss. Beginning with the comprehensive coverage of the new developments in the analysis and control synthesis for switched delay systems, the monograph not only provides a systematic approach to designing the filter and detecting the fault of switched delay systems, but it also covers the model reduction issues. Specific topics covered include: (1) Arbitrary switching signal where delay-independent and delay-dependent conditions are presented by proposing a linearization technique. (2) Average dwell time where a weighted Lyapunov function is come up with dealing with filter design and fault detection issues beside taking model reduction problems. The monograph is in...

  1. Towards Adaptive Spoken Dialog Systems

    CERN Document Server

    Schmitt, Alexander

    2013-01-01

    In Monitoring Adaptive Spoken Dialog Systems, authors Alexander Schmitt and Wolfgang Minker investigate statistical approaches that allow for recognition of negative dialog patterns in Spoken Dialog Systems (SDS). The presented stochastic methods allow a flexible, portable and  accurate use.  Beginning with the foundations of machine learning and pattern recognition, this monograph examines how frequently users show negative emotions in spoken dialog systems and develop novel approaches to speech-based emotion recognition using hybrid approach to model emotions. The authors make use of statistical methods based on acoustic, linguistic and contextual features to examine the relationship between the interaction flow and the occurrence of emotions using non-acted  recordings several thousand real users from commercial and non-commercial SDS. Additionally, the authors present novel statistical methods that spot problems within a dialog based on interaction patterns. The approaches enable future SDS to offer m...

  2. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    Science.gov (United States)

    Ortolan, A.; Baggio, L.; Cerdonio, M.; Prodi, G. A.; Vedovato, G.; Vitale, S.

    2002-04-01

    We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ2 and the time of arrival are those that are expected.

  3. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    International Nuclear Information System (INIS)

    Ortolan, A; Baggio, L; Cerdonio, M; Prodi, G A; Vedovato, G; Vitale, S

    2002-01-01

    We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio χ 2 and the time of arrival are those that are expected

  4. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    Energy Technology Data Exchange (ETDEWEB)

    Ortolan, A [INFN - Laboratori Nazionali di Legnaro, Via Romea, 4 I-35020 Legnaro, Padova (Italy); Baggio, L [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy); Cerdonio, M [Department of Physics, University of Padova and INFN Sezione di Padova, Via Marzolo 8, I-35131 Padova (Italy); Prodi, G A [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy); Vedovato, G [INFN - Laboratori Nazionali di Legnaro, Via Romea, 4 I-35020 Legnaro, Padova (Italy); Vitale, S [Department of Physics, University of Trento and INFN Gruppo Collegato di Trento, I-38050 Povo, Trento (Italy)

    2002-04-07

    We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio {chi}{sup 2} and the time of arrival are those that are expected.

  5. Parametric adaptive filtering and data validation in the bar GW detector AURIGA

    CERN Document Server

    Ortolan, A; Cerdonio, M; Prodi, G A; Vedovato, G; Vitale, S

    2002-01-01

    We report on our experience gained in the signal processing of the resonant GW detector AURIGA. Signal amplitude and arrival time are estimated by means of a matched-adaptive Wiener filter. The detector noise, entering in the filter set-up, is modelled as a parametric ARMA process; to account for slow non-stationarity of the noise, the ARMA parameters are estimated on an hourly basis. A requirement of the set-up of an unbiased Wiener filter is the separation of time spans with 'almost Gaussian' noise from non-Gaussian and/or strongly non-stationary time spans. The separation algorithm consists basically of a variance estimate with the Chauvenet convergence method and a threshold on the Curtosis index. The subsequent validation of data is strictly connected with the separation procedure: in fact, by injecting a large number of artificial GW signals into the 'almost Gaussian' part of the AURIGA data stream, we have demonstrated that the effective probability distributions of the signal-to-noise ratio chi sup 2 ...

  6. Three-State Locally Adaptive Texture Preserving Filter for Radar and Optical Image Processing

    Directory of Open Access Journals (Sweden)

    Jaakko T. Astola

    2005-05-01

    Full Text Available Textural features are one of the most important types of useful information contained in images. In practice, these features are commonly masked by noise. Relatively little attention has been paid to texture preserving properties of noise attenuation methods. This stimulates solving the following tasks: (1 to analyze the texture preservation properties of various filters; and (2 to design image processing methods capable to preserve texture features well and to effectively reduce noise. This paper deals with examining texture feature preserving properties of different filters. The study is performed for a set of texture samples and different noise variances. The locally adaptive three-state schemes are proposed for which texture is considered as a particular class. For “detection” of texture regions, several classifiers are proposed and analyzed. As shown, an appropriate trade-off of the designed filter properties is provided. This is demonstrated quantitatively for artificial test images and is confirmed visually for real-life images.

  7. Adaptive Behaviour Assessment System: Indigenous Australian Adaptation Model (ABAS: IAAM)

    Science.gov (United States)

    du Plessis, Santie

    2015-01-01

    The study objectives were to develop, trial and evaluate a cross-cultural adaptation of the Adaptive Behavior Assessment System-Second Edition Teacher Form (ABAS-II TF) ages 5-21 for use with Indigenous Australian students ages 5-14. This study introduced a multiphase mixed-method design with semi-structured and informal interviews, school…

  8. Power system static state estimation using Kalman filter algorithm

    Directory of Open Access Journals (Sweden)

    Saikia Anupam

    2016-01-01

    Full Text Available State estimation of power system is an important tool for operation, analysis and forecasting of electric power system. In this paper, a Kalman filter algorithm is presented for static estimation of power system state variables. IEEE 14 bus system is employed to check the accuracy of this method. Newton Raphson load flow study is first carried out on our test system and a set of data from the output of load flow program is taken as measurement input. Measurement inputs are simulated by adding Gaussian noise of zero mean. The results of Kalman estimation are compared with traditional Weight Least Square (WLS method and it is observed that Kalman filter algorithm is numerically more efficient than traditional WLS method. Estimation accuracy is also tested for presence of parametric error in the system. In addition, numerical stability of Kalman filter algorithm is tested by considering inclusion of zero mean errors in the initial estimates.

  9. An Adaptive Approach to Mitigate Background Covariance Limitations in the Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon

    2010-07-01

    A new approach is proposed to address the background covariance limitations arising from undersampled ensembles and unaccounted model errors in the ensemble Kalman filter (EnKF). The method enhances the representativeness of the EnKF ensemble by augmenting it with new members chosen adaptively to add missing information that prevents the EnKF from fully fitting the data to the ensemble. The vectors to be added are obtained by back projecting the residuals of the observation misfits from the EnKF analysis step onto the state space. The back projection is done using an optimal interpolation (OI) scheme based on an estimated covariance of the subspace missing from the ensemble. In the experiments reported here, the OI uses a preselected stationary background covariance matrix, as in the hybrid EnKF–three-dimensional variational data assimilation (3DVAR) approach, but the resulting correction is included as a new ensemble member instead of being added to all existing ensemble members. The adaptive approach is tested with the Lorenz-96 model. The hybrid EnKF–3DVAR is used as a benchmark to evaluate the performance of the adaptive approach. Assimilation experiments suggest that the new adaptive scheme significantly improves the EnKF behavior when it suffers from small size ensembles and neglected model errors. It was further found to be competitive with the hybrid EnKF–3DVAR approach, depending on ensemble size and data coverage.

  10. AMA- and RWE- Based Adaptive Kalman Filter for Denoising Fiber Optic Gyroscope Drift Signal.

    Science.gov (United States)

    Yang, Gongliu; Liu, Yuanyuan; Li, Ming; Song, Shunguang

    2015-10-23

    An improved double-factor adaptive Kalman filter called AMA-RWE-DFAKF is proposed to denoise fiber optic gyroscope (FOG) drift signal in both static and dynamic conditions. The first factor is Kalman gain updated by random weighting estimation (RWE) of the covariance matrix of innovation sequence at any time to ensure the lowest noise level of output, but the inertia of KF response increases in dynamic condition. To decrease the inertia, the second factor is the covariance matrix of predicted state vector adjusted by RWE only when discontinuities are detected by adaptive moving average (AMA).The AMA-RWE-DFAKF is applied for denoising FOG static and dynamic signals, its performance is compared with conventional KF (CKF), RWE-based adaptive KF with gain correction (RWE-AKFG), AMA- and RWE- based dual mode adaptive KF (AMA-RWE-DMAKF). Results of Allan variance on static signal and root mean square error (RMSE) on dynamic signal show that this proposed algorithm outperforms all the considered methods in denoising FOG signal.

  11. Off-gas filter system of the SNR-300

    International Nuclear Information System (INIS)

    Boehm, L.; Jordan, S.; Schikarski, W.

    1975-01-01

    The Reventing-Exventing-System of the German Liquid Metal Fast Breeder Prototype SNR-300 is described. After an accident with major damage to the core the ventilation valves are quickly closed. At the same time the blower of the reventing system evacuates the reventing gap up to a pressure difference of 2 mbar between the containment and the outer atmosphere. This pressure difference prevents a leakage from the containment to the outside. The revented gas is recirculated into the outer-containment. Leaks from the atmosphere and possibly from the inner containment into the reventing gap increase the pressure in the outer-containment. Therefore depending on the pressure build-up which is determined by the course of the accident, it is necessary to exvent the containment after several days. The exvented gas is filtered by a filter combination consisting of pre-filters, charcoal-filters and HEPA-filters. Because accidental sodium fires produce high concentrations of sodium oxide-aerosols this filter system must resist chemical aggressive aerosols. (U.S.)

  12. End of FY2014 Report - Filter Measurement System for Nuclear Material Storage Canisters (Including Altitude Correction for Filter Pressure Drop)

    Energy Technology Data Exchange (ETDEWEB)

    Moore, Murray E. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Reeves, Kirk Patrick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2015-02-24

    Two LANL FTS (Filter Test System ) devices for nuclear material storage canisters are fully operational. One is located in PF-4 ( i.e. the TA-55 FTS) while the other is located at the Radiation Protection Division’s Aerosol Engineering Facility ( i.e. the TA-3 FTS). The systems are functionally equivalent , with the TA-3 FTS being the test-bed for new additions and for resolving any issues found in the TA-55 FTS. There is currently one unresolved issue regarding the TA-55 FTS device. The canister lid clamp does not give a leak tight seal when testing the 1 QT (quart) or 2 QT SAVY lids. An adapter plate is being developed that will ensure a correct test configuration when the 1 or 2 QT SAVY lid s are being tested .

  13. Enhanced performance of a filter-sensor system.

    Science.gov (United States)

    Sasaki, Isao; Josowicz, Mira; Janata, Jirí; Glezer, Ari

    2006-06-01

    In this paper are addressed two important, but seemingly unrelated issues: long term performance of a gas sensing array and performance of an air purification unit. It is shown that when considered together, the system can be regarded as a "smart filter". The enhancement is achieved by periodic differential sampling and measurement of the "upstream" and "downstream" gases of a filter. The correctly functioning filter supplies the "zero gas" from the downstream for the continuous sensor baseline correction. A key element in this scheme is the synthetic jet that delivers well-defined pulses of the two gases. The deterioration of the performance of the "smart filter" can be diagnosed from the response pattern of the sensor. The approach has been demonstrated on removal/sensing of ammonia gas from air.

  14. Kalman filtering applied to a reagent feed system

    International Nuclear Information System (INIS)

    Griffin, C.D.; Croson, D.V.; Feeley, J.J.

    1988-01-01

    Using a Kalman filter solves a troublesome measurement noise problem and, at the same time, improves nuclear safety by detecting leaks to the process' feed tanks. To demonstrate how this technology of optimal estimation can be exploited, this article presents a systematic plan and example of how a Kalman filter was proven in industrial use on a reagent analyzer. A process to recycle uranium from spent fuel elements uses a reagent stream containing boron to dissolve the fuel. The boron is the neutron poison that prevents a nuclear chain reaction during the uranium dissolution. The purpose of the Kalman filter for this system is to reduce the uncertainty in the boron concentration measurement. The filter also provides incipient fault detection by estimating the unmeasured state of any unpoisoned solution, which would dilute the boron solution, entering the feed vessel

  15. Kalman Filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry.

    Science.gov (United States)

    Zhang, Yuxin; Chen, Shuo; Deng, Kexin; Chen, Bingyao; Wei, Xing; Yang, Jiafei; Wang, Shi; Ying, Kui

    2017-01-01

    To develop a self-adaptive and fast thermometry method by combining the original hybrid magnetic resonance thermometry method and the bio heat transfer equation (BHTE) model. The proposed Kalman filtered Bio Heat Transfer Model Based Self-adaptive Hybrid Magnetic Resonance Thermometry, abbreviated as KalBHT hybrid method, introduced the BHTE model to synthesize a window on the regularization term of the hybrid algorithm, which leads to a self-adaptive regularization both spatially and temporally with change of temperature. Further, to decrease the sensitivity to accuracy of the BHTE model, Kalman filter is utilized to update the window at each iteration time. To investigate the effect of the proposed model, computer heating simulation, phantom microwave heating experiment and dynamic in-vivo model validation of liver and thoracic tumor were conducted in this study. The heating simulation indicates that the KalBHT hybrid algorithm achieves more accurate results without adjusting λ to a proper value in comparison to the hybrid algorithm. The results of the phantom heating experiment illustrate that the proposed model is able to follow temperature changes in the presence of motion and the temperature estimated also shows less noise in the background and surrounding the hot spot. The dynamic in-vivo model validation with heating simulation demonstrates that the proposed model has a higher convergence rate, more robustness to susceptibility problem surrounding the hot spot and more accuracy of temperature estimation. In the healthy liver experiment with heating simulation, the RMSE of the hot spot of the proposed model is reduced to about 50% compared to the RMSE of the original hybrid model and the convergence time becomes only about one fifth of the hybrid model. The proposed model is able to improve the accuracy of the original hybrid algorithm and accelerate the convergence rate of MR temperature estimation.

  16. Adaptive control of port-Hamiltonian systems

    NARCIS (Netherlands)

    Dirksz, D.A.; Scherpen, J.M.A.; Edelmayer, András

    2010-01-01

    In this paper an adaptive control scheme is presented for general port-Hamiltonian systems. Adaptive control is used to compensate for control errors that are caused by unknown or uncertain parameter values of a system. The adaptive control is also combined with canonical transformation theory for

  17. Farming System Evolution and Adaptive Capacity: Insights for Adaptation Support

    Directory of Open Access Journals (Sweden)

    Jami L. Dixon

    2014-02-01

    Full Text Available Studies of climate impacts on agriculture and adaptation often provide current or future assessments, ignoring the historical contexts farming systems are situated within. We investigate how historical trends have influenced farming system adaptive capacity in Uganda using data from household surveys, semi-structured interviews, focus-group discussions and observations. By comparing two farming systems, we note three major findings: (1 similar trends in farming system evolution have had differential impacts on the diversity of farming systems; (2 trends have contributed to the erosion of informal social and cultural institutions and an increasing dependence on formal institutions; and (3 trade-offs between components of adaptive capacity are made at the farm-scale, thus influencing farming system adaptive capacity. To identify the actual impacts of future climate change and variability, it is important to recognize the dynamic nature of adaptation. In practice, areas identified for further adaptation support include: shift away from one-size-fits-all approach the identification and integration of appropriate modern farming method; a greater focus on building inclusive formal and informal institutions; and a more nuanced understanding regarding the roles and decision-making processes of influential, but external, actors. More research is needed to understand farm-scale trade-offs and the resulting impacts across spatial and temporal scales.

  18. Sensor failure detection in dynamical systems by Kalman filtering methodology

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1991-03-01

    Design of a sensor failure detection system by Kalman filtering methodology is described. The method models the process systems in state-space form, the information on each state being provided by relevant sensors present in the process system. Since the measured states are usually subject to noise, the estimation of the states optimally is an essential requirement. To this end the detection system comprises Kalman estimation filters, the number of which is equal to the number of states concerned. The estimated state of a particular signal in each filter is compared with the corresponding measured signal and difference beyond a predetermined bound is identified as failure, the sensor being identified/isolated as faulty. (author). 19 refs.; 8 figs.; 1 tab

  19. Nonlinear dynamical system identification using unscented Kalman filter

    Science.gov (United States)

    Rehman, M. Javvad ur; Dass, Sarat Chandra; Asirvadam, Vijanth Sagayan

    2016-11-01

    Kalman Filter is the most suitable choice for linear state space and Gaussian error distribution from decades. In general practical systems are not linear and Gaussian so these assumptions give inconsistent results. System Identification for nonlinear dynamical systems is a difficult task to perform. Usually, Extended Kalman Filter (EKF) is used to deal with non-linearity in which Jacobian method is used for linearizing the system dynamics, But it has been observed that in highly non-linear environment performance of EKF is poor. Unscented Kalman Filter (UKF) is proposed here as a better option because instead of analytical linearization of state space, UKF performs statistical linearization by using sigma point calculated from deterministic samples. Formation of the posterior distribution is based on the propagation of mean and covariance through sigma points.

  20. Kalman filtering for time-delayed linear systems

    Institute of Scientific and Technical Information of China (English)

    LU Xiao; WANG Wei

    2006-01-01

    This paper is to study the linear minimum variance estimation for discrete- time systems. A simple approach to the problem is presented by developing re-organized innovation analysis for the systems with instantaneous and double time-delayed measurements. It is shown that the derived estimator involves solving three different standard Kalman filtering with the same dimension as the original system. The obtained results form the basis for solving some complicated problems such as H∞ fixed-lag smoothing, preview control, H∞ filtering and control with time delays.

  1. Digital filter algorithm study and simulation of SSRF feedback system

    International Nuclear Information System (INIS)

    Han Lifeng; Yuan Renxian; Ye Kairong

    2008-01-01

    Least Square Fitting was used to design a FIR filter of the transverse feedback system for the Shanghai Synchrotron Radiation Facility (SSRF). The algorithm helped us to set appropriate gain and phase at special frequency points. This reduced the power needed for damping the beam oscillations, which was proved by System View signal simulation. And with AT (Accelerator Tool) simulation, the Gain calculation and settings to the output signals from the FIR filter were deduced. The relationship between the Kicker power and the system damping time was also given. (authors)

  2. Control and filtering for semi-Markovian jump systems

    CERN Document Server

    Li, Fanbiao; Wu, Ligang

    2017-01-01

    This book presents up-to-date research developments and novel methodologies on semi-Markovian jump systems (S-MJS). It presents solutions to a series of problems with new approaches for the control and filtering of S-MJS, including stability analysis, sliding mode control, dynamic output feedback control, robust filter design, and fault detection. A set of newly developed techniques such as piecewise analysis method, positively invariant set approach, event-triggered method, and cone complementary linearization approaches are presented. Control and Filtering for Semi-Markovian Jump Systems is a comprehensive reference for researcher and practitioners working in control engineering, system sciences and applied mathematics, and is also a useful source of information for senior undergraduates and graduates in these areas. The readers will benefit from some new concepts, new models and new methodologies with practical significance in control engineering and signal processing.

  3. DESIGN AND FABRICATION OF MULTI STAGE CUTTING OIL FILTER SYSTEM

    OpenAIRE

    ABHIJIT S. GETME , ADITENDRA JAISWAL

    2018-01-01

    Multistage oil filtration system is a system used to filter out all the metallic, non metallic particles from the cutting fluid, which is used during the machining processes. Basic procedures we are using in it are: centrifugal process, magnetic filtration, paper filtration.

  4. Filtering and control of stochastic jump hybrid systems

    CERN Document Server

    Yao, Xiuming; Zheng, Wei Xing

    2016-01-01

    This book presents recent research work on stochastic jump hybrid systems. Specifically, the considered stochastic jump hybrid systems include Markovian jump Ito stochastic systems, Markovian jump linear-parameter-varying (LPV) systems, Markovian jump singular systems, Markovian jump two-dimensional (2-D) systems, and Markovian jump repeated scalar nonlinear systems. Some sufficient conditions are first established respectively for the stability and performances of those kinds of stochastic jump hybrid systems in terms of solution of linear matrix inequalities (LMIs). Based on the derived analysis conditions, the filtering and control problems are addressed. The book presents up-to-date research developments and novel methodologies on stochastic jump hybrid systems. The contents can be divided into two parts: the first part is focused on robust filter design problem, while the second part is put the emphasis on robust control problem. These methodologies provide a framework for stability and performance analy...

  5. A Frequency-Domain Adaptive Filter (FDAF) Prediction Error Method (PEM) Framework for Double-Talk-Robust Acoustic Echo Cancellation

    DEFF Research Database (Denmark)

    Gil-Cacho, Jose M.; van Waterschoot, Toon; Moonen, Marc

    2014-01-01

    to the FDAF-PEM-AFROW algorithm. We show that FDAF-PEM-AFROW is by construction related to the best linear unbiased estimate (BLUE) of the echo path. We depart from this framework to show an improvement in performance with respect to other adaptive filters minimizing the BLUE criterion, namely the PEM......In this paper, we propose a new framework to tackle the double-talk (DT) problem in acoustic echo cancellation (AEC). It is based on a frequency-domain adaptive filter (FDAF) implementation of the so-called prediction error method adaptive filtering using row operations (PEM-AFROW) leading...... regularization (VR) algorithms. The FDAF-PEM-AFROW versions significantly outperform the original versions in every simulation. In terms of computational complexity, the FDAF-PEM-AFROW versions are themselves about two orders of magnitude cheaper than the original versions....

  6. An adaptive surface filter for airborne laser scanning point clouds by means of regularization and bending energy

    Science.gov (United States)

    Hu, Han; Ding, Yulin; Zhu, Qing; Wu, Bo; Lin, Hui; Du, Zhiqiang; Zhang, Yeting; Zhang, Yunsheng

    2014-06-01

    The filtering of point clouds is a ubiquitous task in the processing of airborne laser scanning (ALS) data; however, such filtering processes are difficult because of the complex configuration of the terrain features. The classical filtering algorithms rely on the cautious tuning of parameters to handle various landforms. To address the challenge posed by the bundling of different terrain features into a single dataset and to surmount the sensitivity of the parameters, in this study, we propose an adaptive surface filter (ASF) for the classification of ALS point clouds. Based on the principle that the threshold should vary in accordance to the terrain smoothness, the ASF embeds bending energy, which quantitatively depicts the local terrain structure to self-adapt the filter threshold automatically. The ASF employs a step factor to control the data pyramid scheme in which the processing window sizes are reduced progressively, and the ASF gradually interpolates thin plate spline surfaces toward the ground with regularization to handle noise. Using the progressive densification strategy, regularization and self-adaption, both performance improvement and resilience to parameter tuning are achieved. When tested against the benchmark datasets provided by ISPRS, the ASF performs the best in comparison with all other filtering methods, yielding an average total error of 2.85% when optimized and 3.67% when using the same parameter set.

  7. Fault diagnosis system of electromagnetic valve using neural network filter

    International Nuclear Information System (INIS)

    Hayashi, Shoji; Odaka, Tomohiro; Kuroiwa, Jousuke; Ogura, Hisakazu

    2008-01-01

    This paper is concerned with the gas leakage fault detection of electromagnetic valve using a neural network filter. In modern plants, the ability to detect and identify gas leakage faults is becoming increasingly important. The main difficulty in detecting gas leakage faults by sound signals lies in the fact that the practical plants are usually very noisy. To solve this difficulty, a neural network filter is used to eliminate background noise and raise the signal noise ratio of the sound signal. The background noise is assumed as a dynamic system, and an accurate mathematical model of the dynamic system can be established using a neural network filter. The predicted error between predicted values and practical ones constitutes the output of the filter. If the predicted error is zero, then there is no leakage. If the predicted error is greater than a certain value, then there is a leakage fault. Through application to practical pneumatic systems, it is verified that the neural network filter was effective in gas leakage detection. (author)

  8. Staged fluidized-bed combustion and filter system

    International Nuclear Information System (INIS)

    Mei, J.S.; Halow, J.S.

    1994-01-01

    A staged fluidized-bed combustion and filter system are described for substantially reducing the quantity of waste through the complete combustion into ash-type solids and gaseous products. The device has two fluidized-bed portions, the first primarily as a combustor/pyrolyzer bed, and the second as a combustor/filter bed. The two portions each have internal baffles to define stages so that material moving therein as fluidized beds travel in an extended route through those stages. Fluidization and movement is achieved by the introduction of gases into each stage through a directional nozzle. Gases produced in the combustor/pyrolyzer bed are permitted to travel into corresponding stages of the combustor/filter bed through screen filters that permit gas flow but inhibit solids flow. Any catalyst used in the combustor/filter bed is recycled. The two beds share a common wall to minimize total volume of the system. A slightly modified embodiment can be used for hot gas desulfurization and sorbent regeneration. Either side-by-side rectangular beds or concentric beds can be used. The system is particularly suited to the processing of radioactive and chemically hazardous waste. 10 figures

  9. The filtered venting system under construction at barseback

    International Nuclear Information System (INIS)

    Persson, A.H.

    1985-01-01

    A filter venting containment system, bearing the acronym FILTRA will be installed at the Swedish nuclear power plant Barseback. The Barseback Power Plant is owned by the Southern Sweden Power Supply (Sydkraft AB) and has two 1700-MW boiling water reactors. The reactors are of ASEA-ATOM design with pressure suppression containments (Mark IItype). The installation of the filter venting system is a condition set by the Swedish government for a continued operating license after September 1, 1986. The construction work for the FILTRA plant, the first of its kind ever planned, will be completed at the end of 1985. The FILTRA is designed so that 99.9% of the core inventory of radioactivity, excluding inert gases, is retained in the reactor containment and filter system in the event of containment venting. Another design guideline is to achieve passive functioning of the FILTRA plant during the first 24 h of an accident. The FILTRA plant is common to the two reactors on the site and consists mainly of two systems, a venting system (pressure relief system) and a filtering system. The total cost is approximately U.S. $15 million

  10. The optimal filtering of a class of dynamic multiscale systems

    Institute of Scientific and Technical Information of China (English)

    PAN Quan; ZHANG Lei; CUI Peiling; ZHANG Hongcai

    2004-01-01

    This paper discusses the optimal filtering of a class of dynamic multiscale systems (DMS), which are observed independently by several sensors distributed at different resolution spaces. The system is subject to known dynamic system model. The resolution and sampling frequencies of the sensors are supposed to decrease by a factor of two. By using the Haar wavelet transform to link the state nodes at each of the scales within a time block, a discrete-time model of this class of multiscale systems is given, and the conditions for applying Kalman filtering are proven. Based on the linear time-invariant system, the controllability and observability of the system and the stability of the Kalman filtering is studied, and a theorem is given. It is proved that the Kalman filter is stable if only the system is controllable and observable at the finest scale. Finally, a constant-velocity process is used to obtain insight into the efficiencies offered by our model and algorithm.

  11. Notch Filters for Port-Hamiltonian Systems

    NARCIS (Netherlands)

    Dirksz, Danny; Scherpen, Jacquelien M.A.; van der Schaft, Abraham J.; Steinbuch, Maarten

    Many powerful tools exist for control design in the frequency domain, but are theoretically only justified for linear systems. On the other hand, nonlinear control deals with control design methodologies that are theoretically justified for a larger and more realistic class of systems, but primarily

  12. Cost-Effective Video Filtering Solution for Real-Time Vision Systems

    Directory of Open Access Journals (Sweden)

    Karl Martin

    2005-08-01

    Full Text Available This paper presents an efficient video filtering scheme and its implementation in a field-programmable logic device (FPLD. Since the proposed nonlinear, spatiotemporal filtering scheme is based on order statistics, its efficient implementation benefits from a bit-serial realization. The utilization of both the spatial and temporal correlation characteristics of the processed video significantly increases the computational demands on this solution, and thus, implementation becomes a significant challenge. Simulation studies reported in this paper indicate that the proposed pipelined bit-serial FPLD filtering solution can achieve speeds of up to 97.6 Mpixels/s and consumes 1700 to 2700 logic cells for the speed-optimized and area-optimized versions, respectively. Thus, the filter area represents only 6.6 to 10.5% of the Altera STRATIX EP1S25 device available on the Altera Stratix DSP evaluation board, which has been used to implement a prototype of the entire real-time vision system. As such, the proposed adaptive video filtering scheme is both practical and attractive for real-time machine vision and surveillance systems as well as conventional video and multimedia applications.

  13. Extraction of ECG signal with adaptive filter for hearth abnormalities detection

    Science.gov (United States)

    Turnip, Mardi; Saragih, Rijois. I. E.; Dharma, Abdi; Esti Kusumandari, Dwi; Turnip, Arjon; Sitanggang, Delima; Aisyah, Siti

    2018-04-01

    This paper demonstrates an adaptive filter method for extraction ofelectrocardiogram (ECG) feature in hearth abnormalities detection. In particular, electrocardiogram (ECG) is a recording of the heart's electrical activity by capturing a tracingof cardiac electrical impulse as it moves from the atrium to the ventricles. The applied algorithm is to evaluate and analyze ECG signals for abnormalities detection based on P, Q, R and S peaks. In the first phase, the real-time ECG data is acquired and pre-processed. In the second phase, the procured ECG signal is subjected to feature extraction process. The extracted features detect abnormal peaks present in the waveform. Thus the normal and abnormal ECG signal could be differentiated based on the features extracted.

  14. Processing of pulse oximeter signals using adaptive filtering and autocorrelation to isolate perfusion and oxygenation components

    Science.gov (United States)

    Ibey, Bennett; Subramanian, Hariharan; Ericson, Nance; Xu, Weijian; Wilson, Mark; Cote, Gerard L.

    2005-03-01

    A blood perfusion and oxygenation sensor has been developed for in situ monitoring of transplanted organs. In processing in situ data, motion artifacts due to increased perfusion can create invalid oxygenation saturation values. In order to remove the unwanted artifacts from the pulsatile signal, adaptive filtering was employed using a third wavelength source centered at 810nm as a reference signal. The 810 nm source resides approximately at the isosbestic point in the hemoglobin absorption curve where the absorbance of light is nearly equal for oxygenated and deoxygenated hemoglobin. Using an autocorrelation based algorithm oxygenation saturation values can be obtained without the need for large sampling data sets allowing for near real-time processing. This technique has been shown to be more reliable than traditional techniques and proven to adequately improve the measurement of oxygenation values in varying perfusion states.

  15. A novel nonlinear adaptive filter using a pipelined second-order Volterra recurrent neural network.

    Science.gov (United States)

    Zhao, Haiquan; Zhang, Jiashu

    2009-12-01

    To enhance the performance and overcome the heavy computational complexity of recurrent neural networks (RNN), a novel nonlinear adaptive filter based on a pipelined second-order Volterra recurrent neural network (PSOVRNN) is proposed in this paper. A modified real-time recurrent learning (RTRL) algorithm of the proposed filter is derived in much more detail. The PSOVRNN comprises of a number of simple small-scale second-order Volterra recurrent neural network (SOVRNN) modules. In contrast to the standard RNN, these modules of a PSOVRNN can be performed simultaneously in a pipelined parallelism fashion, which can lead to a significant improvement in its total computational efficiency. Moreover, since each module of the PSOVRNN is a SOVRNN in which nonlinearity is introduced by the recursive second-order Volterra (RSOV) expansion, its performance can be further improved. Computer simulations have demonstrated that the PSOVRNN performs better than the pipelined recurrent neural network (PRNN) and RNN for nonlinear colored signals prediction and nonlinear channel equalization. However, the superiority of the PSOVRNN over the PRNN is at the cost of increasing computational complexity due to the introduced nonlinear expansion of each module.

  16. Dim small targets detection based on self-adaptive caliber temporal-spatial filtering

    Science.gov (United States)

    Fan, Xiangsuo; Xu, Zhiyong; Zhang, Jianlin; Huang, Yongmei; Peng, Zhenming

    2017-09-01

    To boost the detect ability of dim small targets, this paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HQS). Finally, on the basis of image pre-processing, to address the problem of missed and wrong detection caused by fixed caliber of traditional pipeline filtering, this paper used targets' multi-frame movement correlation in the time-space domain, combined with the scale-space theory, to propose a temporal-spatial filtering algorithm which allows the caliber to make self-adaptive changes according to the changes of the targets' scale, effectively solving the detection-related issues brought by unchanged caliber and decreased/increased size of the targets. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HQS significantly increased the signal-noise ratio of images; when the signal-noise ratio was lower than 2.6 dB, this detection algorithm could effectively eliminate noise and detect targets. For the algorithm, the lowest signal-to-noise ratio of the detectable target is 0.37.

  17. Adaptive spatial filtering of daytime sky noise in a satellite quantum key distribution downlink receiver

    Science.gov (United States)

    Gruneisen, Mark T.; Sickmiller, Brett A.; Flanagan, Michael B.; Black, James P.; Stoltenberg, Kurt E.; Duchane, Alexander W.

    2016-02-01

    Spatial filtering is an important technique for reducing sky background noise in a satellite quantum key distribution downlink receiver. Atmospheric turbulence limits the extent to which spatial filtering can reduce sky noise without introducing signal losses. Using atmospheric propagation and compensation simulations, the potential benefit of adaptive optics (AO) to secure key generation (SKG) is quantified. Simulations are performed assuming optical propagation from a low-Earth-orbit satellite to a terrestrial receiver that includes AO. Higher-order AO correction is modeled assuming a Shack-Hartmann wavefront sensor and a continuous-face-sheet deformable mirror. The effects of atmospheric turbulence, tracking, and higher-order AO on the photon capture efficiency are simulated using statistical representations of turbulence and a time-domain wave-optics hardware emulator. SKG rates are calculated for a decoy-state protocol as a function of the receiver field of view for various strengths of turbulence, sky radiances, and pointing angles. The results show that at fields of view smaller than those discussed by others, AO technologies can enhance SKG rates in daylight and enable SKG where it would otherwise be prohibited as a consequence of background optical noise and signal loss due to propagation and turbulence effects.

  18. A MIT-Based Nonlinear Adaptive Set-Membership Filter for the Ellipsoidal Estimation of Mobile Robots' States

    Directory of Open Access Journals (Sweden)

    Dalei Song

    2012-10-01

    Full Text Available The adaptive extended set-membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.

  19. Power system harmonics and passive filter designs

    CERN Document Server

    Das, J C

    2015-01-01

    J.C. Das is a consultant of electrical power systems at Power Systems Studies, Inc., USA. He is Life Fellow of IEEE (UK), Fellow of IET (India), and has authored approximately sixty technical papers and published 190 study reports of real-world power systems. He is the author of three books including ARC Flash Hazard Analysis and Mitigation. He is a registered P.E. in the states of Georgia and Oklahoma, C.Eng. in UK, and Eur Ing in Europe. J. C. Das is also a member of CIGRE, Federation of European Engineers, and other technical associations and organizations.

  20. Implementation of a Recommender System using Collaborative Filtering

    Directory of Open Access Journals (Sweden)

    Andrei-Cristian Prodan

    2010-12-01

    Full Text Available Nowadays, consumers have a lot of choices. Electronic retailers offer a great variety of products. Because of this, there is a need for Recommender Systems. These systems aim to solve the problem of matching consumers with the most appealing products for them. They do this by analyzing either the products information details (Content Based methods or users social behavior (Collaborative Filtering. This paper describes the Collaborative Filtering technique in more detail. It then presents one of the best methods for CF: the Matrix Factorization technique. Next, it presents two algorithms used for matrix factorization. Then, the paper describes the implementation details of a framework created by us, called Rho, that uses Collaborative Filtering. In the end, we present some results obtained after experimenting with this framework.

  1. A Performance Comparison Between Extended Kalman Filter and Unscented Kalman Filter in Power System Dynamic State Estimation

    DEFF Research Database (Denmark)

    Khazraj, Hesam; Silva, Filipe Miguel Faria da; Bak, Claus Leth

    2016-01-01

    Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve......-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing...

  2. Using Kalman Filters to Reduce Noise from RFID Location System

    Science.gov (United States)

    Xavier, José; Reis, Luís Paulo; Petry, Marcelo

    2014-01-01

    Nowadays, there are many technologies that support location systems involving intrusive and nonintrusive equipment and also varying in terms of precision, range, and cost. However, the developers some time neglect the noise introduced by these systems, which prevents these systems from reaching their full potential. Focused on this problem, in this research work a comparison study between three different filters was performed in order to reduce the noise introduced by a location system based on RFID UWB technology with an associated error of approximately 18 cm. To achieve this goal, a set of experiments was devised and executed using a miniature train moving at constant velocity in a scenario with two distinct shapes—linear and oval. Also, this train was equipped with a varying number of active tags. The obtained results proved that the Kalman Filter achieved better results when compared to the other two filters. Also, this filter increases the performance of the location system by 15% and 12% for the linear and oval paths respectively, when using one tag. For a multiple tags and oval shape similar results were obtained (11–13% of improvement). PMID:24592186

  3. Filter bed systems treating domestic wastewater in the Nordic countries - Performance and reuse of filter media

    DEFF Research Database (Denmark)

    Jenssen, Petter D.; Krogstad, T.; Paruch, A.M.

    2010-01-01

    Nine filter beds have been constructed in the Nordic countries, Denmark, Finland, Norway and Sweden. Filter beds consist of a septic tank followed by an aerobic pre-treatment biofilter and a subsequent saturated flow grass-covered filter. Thus, filter beds are similar to subsurface flow construct...

  4. A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners

    International Nuclear Information System (INIS)

    Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih; Wu, Jay; Chang, Shu-Jun

    2014-01-01

    Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images

  5. A novel adaptive discrete cosine transform-domain filter for gap-inpainting of high resolution PET scanners

    Energy Technology Data Exchange (ETDEWEB)

    Shih, Cheng-Ting; Lin, Hsin-Hon; Chuang, Keh-Shih [Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan (China); Wu, Jay, E-mail: jwu@mail.cmu.edu.tw [Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung 40402, Taiwan (China); Chang, Shu-Jun [Health Physics Division, Institute of Nuclear Energy Research, Atomic Energy Council, Taoyuan 32546, Taiwan (China)

    2014-08-15

    Purpose: Several positron emission tomography (PET) scanners with special detector block arrangements have been developed in recent years to improve the resolution of PET images. However, the discontinuous detector blocks cause gaps in the sinogram. This study proposes an adaptive discrete cosine transform-based (aDCT) filter for gap-inpainting. Methods: The gap-corrupted sinogram was morphologically closed and subsequently converted to the DCT domain. A certain number of the largest coefficients in the DCT spectrum were identified to determine the low-frequency preservation region. The weighting factors for the remaining coefficients were determined by an exponential weighting function. The aDCT filter was constructed and applied to two digital phantoms and a simulated phantom introduced with various levels of noise. Results: For the Shepp-Logan head phantom, the aDCT filter filled the gaps effectively. For the Jaszczak phantom, no secondary artifacts were induced after aDCT filtering. The percent mean square error and mean structure similarity of the aDCT filter were superior to those of the DCT2 filter at all noise levels. For the simulated striatal dopamine innervation study, the aDCT filter recovered the shape of the striatum and restored the striatum to reference activity ratios to the ideal value. Conclusions: The proposed aDCT filter can recover the missing gap data in the sinogram and improve the image quality and quantitative accuracy of PET images.

  6. FEATURES OF LOGISTIC SYSTEM ADAPTIVE MANAGEMENT

    Directory of Open Access Journals (Sweden)

    Natalya VOZNENKO

    2015-08-01

    Full Text Available The study presents literature survey on enterprise logistic system adaptive management place and structure in the general enterprise management system. The theoretical basics of logistic system functioning, levels of its management and its effectiveness had been investigated. The role of adaptive management and its types had been scrutinized. The necessity of creating company’s adaptive regulator such as its economic mechanism had been proved.

  7. Linear theory for filtering nonlinear multiscale systems with model error.

    Science.gov (United States)

    Berry, Tyrus; Harlim, John

    2014-07-08

    In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuous-time noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higher order asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this non-Gaussian, nonlinear configuration as long as we know the optimal stochastic parametrization and the correct observation model. However, when the stochastic parametrization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results on our nonlinear test model and the two-layer Lorenz-96 model. Finally, even when the correct stochastic ansatz is given, it is imperative to estimate the parameters simultaneously and to account for the nonlinear feedback of the stochastic parameters into the reduced filter estimates. In numerical experiments on the two-layer Lorenz-96 model, we find that the parameters estimated online , as part of a filtering

  8. Movie Recommendation System Based on Collaborative Filtering

    OpenAIRE

    Kejun, Wang

    2013-01-01

    With the explosively growing of the technologies and services of the Internet, the information data world increases rapidly. Recommendation systems have acted an important role in many ways, including movies, books, friends, shopping on net and music. Especially like today, people are surrounded by mass of information. People try to find their preferred movies. It has become difficult when facing so many kinds of movies. People may dizzied by plenty of items on the net, they don't know how to...

  9. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla; Couillet, Romain; Pascal, Frederic; Alouini, Mohamed-Slim

    2017-01-01

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  10. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in quantifying coronary calcium.

    Science.gov (United States)

    Takahashi, Masahiro; Kimura, Fumiko; Umezawa, Tatsuya; Watanabe, Yusuke; Ogawa, Harumi

    2016-01-01

    Adaptive statistical iterative reconstruction (ASIR) has been used to reduce radiation dose in cardiac computed tomography. However, change of image parameters by ASIR as compared to filtered back projection (FBP) may influence quantification of coronary calcium. To investigate the influence of ASIR on calcium quantification in comparison to FBP. In 352 patients, CT images were reconstructed using FBP alone, FBP combined with ASIR 30%, 50%, 70%, and ASIR 100% based on the same raw data. Image noise, plaque density, Agatston scores and calcium volumes were compared among the techniques. Image noise, Agatston score, and calcium volume decreased significantly with ASIR compared to FBP (each P ASIR reduced Agatston score by 10.5% to 31.0%. In calcified plaques both of patients and a phantom, ASIR decreased maximum CT values and calcified plaque size. In comparison to FBP, adaptive statistical iterative reconstruction (ASIR) may significantly decrease Agatston scores and calcium volumes. Copyright © 2016 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

  11. Optimal Design of the Adaptive Normalized Matched Filter Detector using Regularized Tyler Estimators

    KAUST Repository

    Kammoun, Abla

    2017-10-25

    This article addresses improvements on the design of the adaptive normalized matched filter (ANMF) for radar detection. It is well-acknowledged that the estimation of the noise-clutter covariance matrix is a fundamental step in adaptive radar detection. In this paper, we consider regularized estimation methods which force by construction the eigenvalues of the covariance estimates to be greater than a positive regularization parameter ρ. This makes them more suitable for high dimensional problems with a limited number of secondary data samples than traditional sample covariance estimates. The motivation behind this work is to understand the effect and properly set the value of ρthat improves estimate conditioning while maintaining a low estimation bias. More specifically, we consider the design of the ANMF detector for two kinds of regularized estimators, namely the regularized sample covariance matrix (RSCM), the regularized Tyler estimator (RTE). The rationale behind this choice is that the RTE is efficient in mitigating the degradation caused by the presence of impulsive noises while inducing little loss when the noise is Gaussian. Based on asymptotic results brought by recent tools from random matrix theory, we propose a design for the regularization parameter that maximizes the asymptotic detection probability under constant asymptotic false alarm rates. Provided Simulations support the efficiency of the proposed method, illustrating its gain over conventional settings of the regularization parameter.

  12. Parameter estimation for stiff deterministic dynamical systems via ensemble Kalman filter

    International Nuclear Information System (INIS)

    Arnold, Andrea; Calvetti, Daniela; Somersalo, Erkki

    2014-01-01

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

  13. Adaptive estimation of a time-varying phase with coherent states: Smoothing can give an unbounded improvement over filtering

    Science.gov (United States)

    Laverick, Kiarn T.; Wiseman, Howard M.; Dinani, Hossein T.; Berry, Dominic W.

    2018-04-01

    The problem of measuring a time-varying phase, even when the statistics of the variation is known, is considerably harder than that of measuring a constant phase. In particular, the usual bounds on accuracy, such as the 1 /(4 n ¯) standard quantum limit with coherent states, do not apply. Here, by restricting to coherent states, we are able to analytically obtain the achievable accuracy, the equivalent of the standard quantum limit, for a wide class of phase variation. In particular, we consider the case where the phase has Gaussian statistics and a power-law spectrum equal to κp -1/|ω| p for large ω , for some p >1 . For coherent states with mean photon flux N , we give the quantum Cramér-Rao bound on the mean-square phase error as [psin(π /p ) ] -1(4N /κ ) -(p -1 )/p . Next, we consider whether the bound can be achieved by an adaptive homodyne measurement in the limit N /κ ≫1 , which allows the photocurrent to be linearized. Applying the optimal filtering for the resultant linear Gaussian system, we find the same scaling with N , but with a prefactor larger by a factor of p . By contrast, if we employ optimal smoothing we can exactly obtain the quantum Cramér-Rao bound. That is, contrary to previously considered (p =2 ) cases of phase estimation, here the improvement offered by smoothing over filtering is not limited to a factor of 2 but rather can be unbounded by a factor of p . We also study numerically the performance of these estimators for an adaptive measurement in the limit where N /κ is not large and find a more complicated picture.

  14. Design of HTS filter for GSM-R communication system

    Science.gov (United States)

    Cui, Hongyu; Ji, Laiyun

    2018-04-01

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

  15. Modeling Power Systems as Complex Adaptive Systems

    Energy Technology Data Exchange (ETDEWEB)

    Chassin, David P.; Malard, Joel M.; Posse, Christian; Gangopadhyaya, Asim; Lu, Ning; Katipamula, Srinivas; Mallow, J V.

    2004-12-30

    Physical analogs have shown considerable promise for understanding the behavior of complex adaptive systems, including macroeconomics, biological systems, social networks, and electric power markets. Many of today's most challenging technical and policy questions can be reduced to a distributed economic control problem. Indeed, economically based control of large-scale systems is founded on the conjecture that the price-based regulation (e.g., auctions, markets) results in an optimal allocation of resources and emergent optimal system control. This report explores the state-of-the-art physical analogs for understanding the behavior of some econophysical systems and deriving stable and robust control strategies for using them. We review and discuss applications of some analytic methods based on a thermodynamic metaphor, according to which the interplay between system entropy and conservation laws gives rise to intuitive and governing global properties of complex systems that cannot be otherwise understood. We apply these methods to the question of how power markets can be expected to behave under a variety of conditions.

  16. A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

    Science.gov (United States)

    Luo, Yong; Wu, Wenqi; Babu, Ravindra; Tang, Kanghua; Luo, Bing

    2012-01-01

    COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System). Since the ultra-tight GPS/INS (Inertial Navigation System) integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF), and INS's accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load. PMID:23012564

  17. A Simplified Baseband Prefilter Model with Adaptive Kalman Filter for Ultra-Tight COMPASS/INS Integration

    Directory of Open Access Journals (Sweden)

    Bing Luo

    2012-07-01

    Full Text Available COMPASS is an indigenously developed Chinese global navigation satellite system and will share many features in common with GPS (Global Positioning System. Since the ultra-tight GPS/INS (Inertial Navigation System integration shows its advantage over independent GPS receivers in many scenarios, the federated ultra-tight COMPASS/INS integration has been investigated in this paper, particularly, by proposing a simplified prefilter model. Compared with a traditional prefilter model, the state space of this simplified system contains only carrier phase, carrier frequency and carrier frequency rate tracking errors. A two-quadrant arctangent discriminator output is used as a measurement. Since the code tracking error related parameters were excluded from the state space of traditional prefilter models, the code/carrier divergence would destroy the carrier tracking process, and therefore an adaptive Kalman filter algorithm tuning process noise covariance matrix based on state correction sequence was incorporated to compensate for the divergence. The federated ultra-tight COMPASS/INS integration was implemented with a hardware COMPASS intermediate frequency (IF, and INS’s accelerometers and gyroscopes signal sampling system. Field and simulation test results showed almost similar tracking and navigation performances for both the traditional prefilter model and the proposed system; however, the latter largely decreased the computational load.

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

    Science.gov (United States)

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

    2018-04-01

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

  19. A synthesis theory for self-oscillating adaptive systems /SOAS/

    Science.gov (United States)

    Horowitz, I.; Smay, J.; Shapiro, A.

    1974-01-01

    A quantitative synthesis theory is presented for the Self-Oscillating Adaptive System (SOAS), whose nonlinear element has a static, odd character with hard saturation. The synthesis theory is based upon the quasilinear properties of the SOAS to forced inputs, which permits the extension of quantitative linear feedback theory to the SOAS. A reasonable definition of optimum design is shown to be the minimization of the limit cycle frequency. The great advantages of the SOAS is its zero sensitivity to pure gain changes. However, quasilinearity and control of the limit cycle amplitude at the system output, impose additional constraints which partially or completely cancel this advantage, depending on the numerical values of the design parameters. By means of narrow-band filtering, an additional factor is introduced which permits trade-off between filter complexity and limit cycle frequency minimization.

  20. Adaptive control of energy storage systems for power smoothing applications

    DEFF Research Database (Denmark)

    Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.

    2017-01-01

    Energy storage systems (ESSs) are desired and widely applied for power smoothing especially in systems with renewable generation and pulsed loads. High-pass-filter (HPF) is commonly applied in those applications in which the HPF extracts the high frequency fluctuating power and uses...... that as the power reference for ESS. The cut-off frequency, as the critical parameter, actually decides the power/energy compensated by ESS. Practically the state-of-charge (SoC) of the ESS has to be limited for safety and life-cycle considerations. In this paper an adaptive cut-off frequency design is proposed...

  1. Development of laundry drainage treatment system with ceramic ultra filter

    International Nuclear Information System (INIS)

    Kanda, Masanori; Kurahasi, Takafumi

    1995-01-01

    A compact laundry drainage treatment system (UF system hereafter) with a ceramic ultra filter membrane (UF membrane hereafter) has been developed to reduce radioactivity in laundry drainage from nuclear power plants. The UF membrane is made of sintered fine ceramic. The UF membrane has 0.01 μm fine pores, resulting in a durable, heat-resistant, and corrosion-resistant porous ceramic filter medium. A cross-flow system, laundry drainage is filtrated while it flows across the UF membrane, is used as the filtration method. This method creates less caking when compared to other methods. The UF membrane is back washed at regular intervals with permeated water to minimize caking of the filter. The UF membrane and cross-flow system provides long stable filtration. The ceramic UF membrane is strong enough to concentrate suspended solids in laundry drainage up to a weight concentration of 10%. The final concentrated laundry drainage can be treated in an incinerator. The performance of the UF system was checked using radioactive laundry drainage. The decontamination factor of the UF system was 25 or more. The laundry drainage treatment capacity and concentration ratio of the UF system, as well as the service life of the UF membrane were also checked by examination using simulated non-radioactive laundry drainage. Even though laundry drainage was concentrated 1000 times, the UF system showed good permeated water quality and permeated water flux. (author)

  2. Detection of User Independent Single Trial ERPs in Brain Computer Interfaces: An Adaptive Spatial Filtering Approach

    DEFF Research Database (Denmark)

    Leza, Cristina; Puthusserypady, Sadasivan

    2017-01-01

    Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, specifically suited for single-trial detection of Event...

  3. Study of Robust H∞ Filtering Application in Loosely Coupled INS/GPS System

    Directory of Open Access Journals (Sweden)

    Lin Zhao

    2014-01-01

    model, unstable model case is considered. We give an explanation for Kalman filter divergence under uncertain dynamic system and simultaneously investigate the relationship between H∞ filter and Kalman filter. A loosely coupled INS/GPS simulation system is given here to verify this application. Result shows that the robust H∞ filter has a better performance when system suffers uncertainty; also it is more robust compared to the conventional Kalman filter.

  4. A decoupled approach to filter design for stochastic systems

    Science.gov (United States)

    Barbata, A.; Zasadzinski, M.; Ali, H. Souley; Messaoud, H.

    2016-08-01

    This paper presents a new theorem to guarantee the almost sure exponential stability for a class of stochastic triangular systems by studying only the stability of each diagonal subsystems. This result allows to solve the filtering problem of the stochastic systems with multiplicative noises by using the almost sure exponential stability concept. Two kinds of observers are treated: the full-order and reduced-order cases.

  5. Adaptive partial median filter for early CT signs of acute cerebral infarction

    International Nuclear Information System (INIS)

    Lee, Yongbum; Tsai, Du-Yih; Takahashi, Noriyuki; Ishii, Kiyoshi

    2007-01-01

    Purpose: Detection of early CT signs of infarct in non- enhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normal head CT images. The average A z values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using non-enhanced CT images. (orig.)

  6. Stock market modeling and forecasting a system adaptation approach

    CERN Document Server

    Zheng, Xiaolian

    2013-01-01

    Stock Market Modeling translates experience in system adaptation gained in an engineering context to the modeling of financial markets with a view to improving the capture and understanding of market dynamics. The modeling process is considered as identifying a dynamic system in which a real stock market is treated as an unknown plant and the identification model proposed is tuned by feedback of the matching error. Like a physical system, a stock market exhibits fast and slow dynamics corresponding to internal (such as company value and profitability) and external forces (such as investor sentiment and commodity prices) respectively. The framework presented here, consisting of an internal model and an adaptive filter, is successful at considering both fast and slow market dynamics. A double selection method is efficacious in identifying input factors influential in market movements, revealing them to be both frequency- and market-dependent.   The authors present work on both developed and developing markets ...

  7. Visual Cues for an Adaptive Expert System.

    Science.gov (United States)

    Miller, Helen B.

    NCR (National Cash Register) Corporation is pursuing opportunities to make their point of sale (POS) terminals easy to use and easy to learn. To approach the goal of making the technology invisible to the user, NCR has developed an adaptive expert prototype system for a department store POS operation. The structure for the adaptive system, the…

  8. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

    Science.gov (United States)

    Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram

    2014-12-01

    Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

  9. Method for cleaning the filter pockets of dust gas filter systems

    Energy Technology Data Exchange (ETDEWEB)

    Margraf, A

    1975-05-07

    The invention deals with a method to clean filter pockets filled with dust gas. By a periodic to and fro air jet attached to a scavenging blower, a pulsed fluttering movement of the filter surface is obtained which releases the outer layers of dust. The charging of the filter pockets with scavenging air to clean the filter material can be carried out immediately on the pulsed admission with suitable time control.

  10. Operator adaptation to changes in system reliability under adaptable automation.

    Science.gov (United States)

    Chavaillaz, Alain; Sauer, Juergen

    2017-09-01

    This experiment examined how operators coped with a change in system reliability between training and testing. Forty participants were trained for 3 h on a complex process control simulation modelling six levels of automation (LOA). In training, participants either experienced a high- (100%) or low-reliability system (50%). The impact of training experience on operator behaviour was examined during a 2.5 h testing session, in which participants either experienced a high- (100%) or low-reliability system (60%). The results showed that most operators did not often switch between LOA. Most chose an LOA that relieved them of most tasks but maintained their decision authority. Training experience did not have a strong impact on the outcome measures (e.g. performance, complacency). Low system reliability led to decreased performance and self-confidence. Furthermore, complacency was observed under high system reliability. Overall, the findings suggest benefits of adaptable automation because it accommodates different operator preferences for LOA. Practitioner Summary: The present research shows that operators can adapt to changes in system reliability between training and testing sessions. Furthermore, it provides evidence that each operator has his/her preferred automation level. Since this preference varies strongly between operators, adaptable automation seems to be suitable to accommodate these large differences.

  11. Cascade ultrafiltering of 210Pb and 210Po in freshwater using a tangential flow filtering system

    International Nuclear Information System (INIS)

    Ohtsuka, Y.; Takaku, Y.; Hisamatsu, S.; Inaba, J.; Yamamoto, M.

    2006-01-01

    A rapid method was developed using ultrafilters with a tangential flow filtering system for molecular size separation of naturally occurring 210 Pb and 210 Po in a freshwater sample. Generally, ultrafiltering of a large volume water sample for measuring the nuclides was too time consuming and not practical. The tangential flow filtering system made the filtering time short enough to adapt for in-situ ultrafiltering the large volume sample. In this method, a 20 liter water sample was at first passed through the 0.45 μm pore size membrane filter immediately after sample collection to obtain suspended particle matter [>0.45 μm particulate fraction (PRT)]. Two ultrafilters (Millipore Pellicon 2 R ) were used sequentially. The nuclides in the filtrate were separated into three fractions: high molecular mass (100 kDa-0.45μm; HMM), low molecular mass (10 k-100 kDa; LMM) and ionic ( 210 Pb and 210 Po in an oligotrophic lake, Lake Towada located in the northern area of Japan. (author)

  12. Evaluation of an Acoustic Charge Transport (ACT) device for adaptive interference suppression in spread spectrum communications systems

    Science.gov (United States)

    Mills, Michael S.

    1993-12-01

    Analytical results have shown that adaptive filtering can be a powerful tool for the rejection of narrowband interference in a direct sequence spread spectrum receiver. However, the complexity of adaptive filtering hardware has hindered the experimental validation of these results. This thesis describes a unique adaptive filter architecture for implementing the Widrow-Hoff least mean square (LMS) algorithm using two state of the art acoustic charge transport (ACT) programmable transversal filters (PTF's). Signal to noise ratio improvement measurements demonstrate the effectiveness of the adaptive filter for suppressing single- and dual-tone jammers at jammer to signal ratios (JSR's) of up to 30 dB. It is shown that the ACT adaptive interference rejection system can consistently produce 55 dB notch depths with 3-dB bandwidths as low as 300 kHz with minimal degradation to the spread spectrum signal. It is also shown that the adaptive system can eliminate single tone jammers at any frequency within the spread spectrum bandwidth at any of 10, 20, or 30 dB JSRs within 10 to 15 iterations of the adaptive algorithm. The only drawback with the adaptive system as tested is the amount of time taken to perform an iteration because of the requirement to update the PTF tap weights sequentially. Suggestions are given as to how this particular parameter of the adaptive interference system could be optimized.

  13. A practical approach to harmonic compensation in power systems-series connection of passive and active filters

    OpenAIRE

    Fujita, Hideaki; Akagi, Hirofumi

    1991-01-01

    The authors present a combined system with a passive filter and a small-rated active filter, both connected in series with each other. The passive filter removes load produced harmonics just as a conventional filter does. The active filter plays a role in improving the filtering characteristics of the passive filter. This results in a great reduction of the required rating of the active filter and in eliminating all the limitations faced by using only the passive filter, leading to a practica...

  14. Iodine filter imaging system for subtraction angiography using synchrotron radiation

    Science.gov (United States)

    Umetani, K.; Ueda, K.; Takeda, T.; Itai, Y.; Akisada, M.; Nakajima, T.

    1993-11-01

    A new type of real-time imaging system was developed for transvenous coronary angiography. A combination of an iodine filter and a single energy broad-bandwidth X-ray produces two-energy images for the iodine K-edge subtraction technique. X-ray images are sequentially converted to visible images by an X-ray image intensifier. By synchronizing the timing of the movement of the iodine filter into and out of the X-ray beam, two output images of the image intensifier are focused side by side on the photoconductive layer of a camera tube by an oscillating mirror. Both images are read out by electron beam scanning of a 1050-scanning-line video camera within a camera frame time of 66.7 ms. One hundred ninety two pairs of iodine-filtered and non-iodine-filtered images are stored in the frame memory at a rate of 15 pairs/s. In vivo subtracted images of coronary arteries in dogs were obtained in the form of motion pictures.

  15. Nonlinear stochastic systems with incomplete information filtering and control

    CERN Document Server

    Shen, Bo; Shu, Huisheng

    2013-01-01

    Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. The causes of this problem considered here include: missing measurements; sensor delays and saturation; quantization effects; and signal sampling. Divided into three parts, the text begins with a focus on H∞ filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems. Filtering problems are considered in the second part, and in the third the theory and techniques previously developed are applied to the solution of issues arising in complex networks with the design of sampled-data-based controllers and filters. Among its highlights, the text provides: ·         a unified framework for handling filtering and control problems in complex communication networks with limited bandwidth; ·         new concepts such as random sensor and signal saturations for more realistic modeling; and ·         demonstration of the use of techniques such...

  16. Operational readiness of filtered air discharge monitoring systems

    International Nuclear Information System (INIS)

    Lafortune, J.F.; Jamieson, T.J.

    1993-08-01

    An assessment of the operational readiness of the Filtered Air Discharge (FAD) Stack Monitoring systems, installed in Canadian CANDU nuclear power plants, was performed in this project. Relevant Canadian and foreign standards and regulatory requirements have been reviewed and documentation on FAD stack monitoring system design, operation, testing and maintenance have been assessed to identify likely causes and potential failures of FAD stack monitoring systems and their components under both standby and accident conditions. Recommendations have also been provided in this report for design and performance review guidelines for CANDU stations. A case study of the FAD stack monitoring system at Pickering NGS is also documented in this report

  17. Self-Adaptive Systems for Machine Intelligence

    CERN Document Server

    He, Haibo

    2011-01-01

    This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide application

  18. Fuzzy filter for state estimation of a glucoregulatory system.

    Science.gov (United States)

    Trajanoski, Z; Wach, P

    1996-08-01

    A filter based on fuzzy logic for state estimation of a glucoregulatory system is presented. A published non-linear model for the dynamics of glucose and its hormonal control including a single glucose compartment, five insulin compartments and a glucagon compartment was used for simulation. The simulated data were corrupted by an additive white noise with zero mean and a coefficient of variation (CV) of between 2 and 20% and then submitted to the state estimation procedure using a fuzzy filter (FF). The performance of the FF was compared with an extended Kalman filter (EKF) for state estimation. Both the FF and the EKF were evaluated in the following cases: (a) five state variables are measurable; three plasma variables are measurable; only plasma glucose is measurable; (b) for different measurement noise levels (CV of 2-20%); and (c) a mismatch between the glucoregulatory system and the given mathematical model (uncertain or approximate model). In contrast to the FF, in the case of approximate model of the glucose system, the EKF failed to achieve useful state estimation. Moreover, the performance of the FF was independent of the noise level. In conclusion, the FF approach is a viable alternative for state estimation in a noisy environment and with an uncertain mathematical model of the glucoregulatory system.

  19. Fungal colonization of air filters for use in heating, ventilating, and air conditioning (HVAC) systems.

    Science.gov (United States)

    Simmons, R B; Crow, S A

    1995-01-01

    New and used cellulosic air filters for HVAC systems including those treated with antimicrobials were suspended in vessels with a range of relative humidities (55-99%) and containing non-sterile potting soil which stimulates fungal growth. Most filters yielded fungi prior to suspension in the chambers but only two of 14 nontreated filters demonstrated fungal colonization following use in HVAC systems. Filters treated with antimicrobials, particularly a phosphated amine complex, demonstrated markedly less fungal colonization than nontreated filters. In comparison with nontreated cellulosic filters, fungal colonization of antimicrobial-treated cellulosic filters was selective and delayed.

  20. Flexible Riser Monitoring Using Hybrid Magnetic/Optical Strain Gage Techniques through RLS Adaptive Filtering

    Directory of Open Access Journals (Sweden)

    Daniel Pipa

    2010-01-01

    Full Text Available Flexible riser is a class of flexible pipes which is used to connect subsea pipelines to floating offshore installations, such as FPSOs (floating production/storage/off-loading unit and SS (semisubmersible platforms, in oil and gas production. Flexible risers are multilayered pipes typically comprising an inner flexible metal carcass surrounded by polymer layers and spiral wound steel ligaments, also referred to as armor wires. Since these armor wires are made of steel, their magnetic properties are sensitive to the stress they are subjected to. By measuring their magnetic properties in a nonintrusive manner, it is possible to compare the stress in the armor wires, thus allowing the identification of damaged ones. However, one encounters several sources of noise when measuring electromagnetic properties contactlessly, such as movement between specimen and probe, and magnetic noise. This paper describes the development of a new technique for automatic monitoring of armor layers of flexible risers. The proposed approach aims to minimize these current uncertainties by combining electromagnetic measurements with optical strain gage data through a recursive least squares (RLSs adaptive filter.

  1. RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: a Monte Carlo study

    International Nuclear Information System (INIS)

    Zhang, Y; Sun, J W; Rolfe, P

    2012-01-01

    The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source–detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source–detector separation less than 9 mm is robust to superficial layer thickness variation. (paper)

  2. Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

    Directory of Open Access Journals (Sweden)

    Ghalib R. Ibrahim

    2012-01-01

    Full Text Available Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio, which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal and fault 2 (50% tooth removal under loads (0%, and 80% of the total load. The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

  3. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  4. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  5. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  6. Distributed Systems and Applications of Information Filtering and Retrieval

    CERN Document Server

    Giuliani, Alessandro; Semeraro, Giovanni; DART 2012

    2014-01-01

    This volume focuses on new challenges in distributed Information Filtering and Retrieval. It collects invited chapters and extended research contributions from the special session on Information Filtering and Retrieval: Novel Distributed Systems and Applications (DART) of the 4th International Conference on Knowledge Discovery and Information Retrieval (KDIR 2012), held in Barcelona, Spain, on 4-7 October 2012. The main focus of DART was to discuss and compare suitable novel solutions based on intelligent techniques and applied to real-world applications. The chapters of this book present a comprehensive review of related works and state of the art. Authors, both practitioners and researchers, shared their results in several topics such as "Multi-Agent Systems", "Natural Language Processing", "Automatic Advertisement", "Customer Interaction Analytics", "Opinion Mining". Contributions have been careful reviewed by experts in the area, who also gave useful suggestions to improve the quality of the volume.

  7. Adaptive Multiuser Detectors for DS-CDMA Systems

    Directory of Open Access Journals (Sweden)

    Paul Jean Etienne Jeszensky

    2006-02-01

    Full Text Available This work makes a review of the main Adaptives Multi-user Detectors (MuD-Adpt for Direct Sequence - Code Division Multiple Access (DS-CDMA systems. The MuD-Adpt based on Minimum Mean Square Error (MMSE and Decorrelator (MuD-Dec are focused. Multi-user detectors show great resistance to the near-far effect and combat effectively the Multiple Access Interference (MAI. Comparative numeric results characterize the substantial performance improvement of those detectors in relation to the matched filter conventional receiver (Conv.

  8. Hybrid power filter for advanced power quality in industrial systems

    Czech Academy of Sciences Publication Activity Database

    Švec, J.; Müller, Z.; Kasembe, A. G.; Tlustý, J.; Valouch, Viktor

    2013-01-01

    Roč. 103, october 2013 (2013), s. 157-167 ISSN 0378-7796 R&D Projects: GA AV ČR IAA200760703 Institutional research plan: CEZ:AV0Z20570509 Institutional support: RVO:61388998 Keywords : hybrid power filter * power quality * industrial system Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.595, year: 2013 http://www.sciencedirect.com/science/article/pii/S0378779613001417

  9. Load compensation for single phase system using series active filter

    African Journals Online (AJOL)

    user

    Keywords: Active power filter (APF), current source type of harmonic load ... Single phase active filters could attract less attention than three phase due to its low ..... Generalised single-phase p-q theory for active power filtering: simulation and.

  10. Design Criteria for DC Link Filters in a Synchronous Generator-Phase Controlled Rectifier-Filter-Load System

    National Research Council Canada - National Science Library

    Greseth, Gregory

    1999-01-01

    .... The proposed Navy DC Zonal Electrical Distribution System (DC ZEDS) being designed for the new DD-21 utilizes a rectified ac generator output which is filtered and stepped to usable voltages by local dc-dc converters...

  11. AN EFFECTIVE SPAM FILTERING FOR DYNAMIC MAIL MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    S. Arun Mozhi Selvi

    2012-04-01

    Full Text Available Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. The economics of spam details that the spammer has to target several recipients with identical and similar email messages. As a result a dynamic knowledge sharing effective defense against a substantial fraction of spam has to be designed which can alternate the burdens of frequent training stand alone spam filter. A weighted email attribute based classification is proposed to mainly focus to encounter the issues in normal email system. These type of classification helps to formulate an effective utilization of our email system by combining the concepts of Bayesian Spam Filtering Algorithm, Iterative Dichotmiser 3(ID3 Algorithm and Bloom Filter. The details captured by the system are processed to track the original sender causing disturbances and prefer them to block further mails from them. We have tested the effectiveness of our scheme by collecting offline data from Yahoo mail & Gmail dumps. This proposal is implemented using .net and sample user-Id for knowledge base.

  12. Numerical Analysis of a Passive Containment Filtered Venting System

    International Nuclear Information System (INIS)

    Kim, Taejoon; Ha, Huiun; Heo, Sun

    2014-01-01

    The passive Containment Filtered Venting system (CFVS) does not have principally any kind of isolation valves or filtering devices which need periodic maintenance. In this study, the hydro-thermal analysis is presented to investigate the existence of flow instability in the passive CFVS and its performance under the pressure change of APR+ containment building with LB-LOCA M/E data. The Passive Containment Filtered Venting System was suggested as a part in i-Power development project and the operation mechanism was investigated by numerical modeling and simulation using GOTHIC8.0 system code. There are four Phases for consideration to investigate the pressurization of the containment building, loss of hydrostatic head in the pipe line of CFVS, opening of pipe line and gas ejection to the coolant tank, and the head recovery inside the pipe as the containment gas exhausted. The simulation results show that gas generation rate determine the timing of head recovery in the CFVS pipe line and that the equipment of various devices inducing pressure loss at the pipe can give the capacity of Phase control of the passive CFVS operation

  13. Quantum demolition filtering and optimal control of unstable systems.

    Science.gov (United States)

    Belavkin, V P

    2012-11-28

    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.

  14. Adaptive Hypermedia Systems for E-Learning

    Directory of Open Access Journals (Sweden)

    Aammou Souhaib

    2010-11-01

    Full Text Available The domain of traditional hypermedia is revolutionized by the arrival of the concept of adaptation. Currently the domain of Adaptive Hypermedia Systems (AHS is constantly growing. A major goal of current research is to provide a personalized educational experience that meets the needs specific to each learner (knowledge level, goals, motivation etc.... In this article we have studied the possibility of implementing traditional features of adaptive hypermedia in an open environment, and discussed the standards for describing learning objects and architectural models based on the use of ontologies as a prerequisite for such an adaptation.

  15. Westinghouse containment filtered venting system wet scrubber technology

    International Nuclear Information System (INIS)

    Kristensson, S.; Nilsson, P-O.

    2014-01-01

    Following the Fukushima event Westinghouse has further developed and enhanced its filtered containment venting system (FCVS) product line. The filtration efficiency of the proven FILTRA-MVSS system installed at all Swedish NPPs as well as at the Muhelberg plant in Switzerland has been enhanced and a new wet scrubber design, SVEN (Safety Venting), based on the FILTRA-MVSS tradition, developed. To meet increased filtration requirements for organic iodine these two wet scrubber products have been complemented with a zeolite module. The offering of a select choice of products allows for a better adjustment to the specific constraints and needs of each nuclear power station that is planning for the installation of such a system. The FILTRA-MVSS (MVSS=Multi Venturi Scrubber System) is a wet containment filtered vent system that uses multiple venturies to create an interaction between the vent gases and the scrubber media allowing for removal of aerosols and gaseous iodines in a very efficient manner. The FILTRA-MVSS was originally developed to meet stringent requirements on autonomy and maintained filtration efficiency over a wide range of venting conditions. The system was jointly developed in the late 80's by ABB Atom and ABB Flaekt, today Westinghouse and Alstom. Following installations in Sweden and Switzerland the system was further developed by replacement of the gravel-bed moisture separator with a standard demister and by addition of a set of sintered metal fibre filter cartridges placed after the moisture separator step. The system is today offered as a modular steel tank design to simplify installation at site. To reduce complexity and delivery time Westinghouse has developed an alternative design in which the venturi module is replaced by a submerged metal fibre filter cartridges module. This new wet scrubber design, SVEN (patent pending), provides a flexible, compact, and lower weight system, while still preserving and even enhancing the filtration

  16. Hybrid Plasma Reactor/Filter for Transportable Collective Protection Systems

    Energy Technology Data Exchange (ETDEWEB)

    Josephson, Gary B.; Tonkyn, Russell G.; Frye, J. G.; Riley, Brian J.; Rappe, Kenneth G.

    2011-04-06

    Pacific Northwest National Laboratory (PNNL) has performed an assessment of a Hybrid Plasma/Filter system as an alternative to conventional methods for collective protection. The key premise of the hybrid system is to couple a nonthermal plasma (NTP) reactor with reactive adsorption to provide a broader envelope of protection than can be provided through a single-solution approach. The first step uses highly reactive species (e.g. oxygen radicals, hydroxyl radicals, etc.) created in a nonthermal plasma (NTP) reactor to destroy the majority (~75% - 90%) of an incoming threat. Following the NTP reactor an O3 reactor/filter uses the O3 created in the NTP reactor to further destroy the remaining organic materials. This report summarizes the laboratory development of the Hybrid Plasma Reactor/Filter to protect against a ‘worst-case’ simulant, methyl bromide (CH3Br), and presents a preliminary engineering assessment of the technology to Joint Expeditionary Collective Protection performance specifications for chemical vapor air purification technologies.

  17. Optical filtering in directly modulated/detected OOFDM systems.

    Science.gov (United States)

    Sánchez, C; Ortega, B; Wei, J L; Capmany, J

    2013-12-16

    This work presents a theoretical investigation on the performance of directly modulated/detected (DM/DD) optical orthogonal frequency division multiplexed (OOFDM) systems subject to optical filtering. The impact of both linear and nonlinear distortion effects are taken into account to calculate the effective signal-to-noise ratio of each subcarrier. These results are then employed to optimize the design parameters of two simple optical filtering structures: a Mach Zehnder interferometer and a uniform fiber Bragg grating, leading to a significant optical power budget improvement given by 3.3 and 3dB, respectively. These can be further increased to 5.5 and 4.2dB respectively when balanced detection configurations are employed. We find as well that this improvement is highly dependent on the clipping ratio.

  18. An adaptive digital suppression filter for direct-sequence spread-spectrum communications

    Science.gov (United States)

    Saulnier, G. J.; Das, P. K.; Milstein, L. B.

    1985-09-01

    This paper describes the structure of a digital implementation of the Widrow-Hoff LMS algorithm which uses a burst processing technique to obtain some hardware simplification. This adaptive system is used to suppress narrow-band interference in a direct-sequence spread-spectrum communication system. Several different narrow-band interferers are considered, and probability of error results are presented for all cases. While, in general, the results show significant improvement in performance when the LMS algorithm is used, certain disadvantages are also present and are discussed in this paper.

  19. Adaptation in CRISPR-Cas Systems.

    Science.gov (United States)

    Sternberg, Samuel H; Richter, Hagen; Charpentier, Emmanuelle; Qimron, Udi

    2016-03-17

    Clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins constitute an adaptive immune system in prokaryotes. The system preserves memories of prior infections by integrating short segments of foreign DNA, termed spacers, into the CRISPR array in a process termed adaptation. During the past 3 years, significant progress has been made on the genetic requirements and molecular mechanisms of adaptation. Here we review these recent advances, with a focus on the experimental approaches that have been developed, the insights they generated, and a proposed mechanism for self- versus non-self-discrimination during the process of spacer selection. We further describe the regulation of adaptation and the protein players involved in this fascinating process that allows bacteria and archaea to harbor adaptive immunity. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model

    Directory of Open Access Journals (Sweden)

    Yuanyuan Liu

    2013-08-01

    Full Text Available Accurate estimation of the state of charge (SOC of batteries is one of the key problems in a battery management system. This paper proposes an adaptive SOC estimation method based on unscented Kalman filter algorithms for lithium (Li-ion batteries. First, an enhanced battery model is proposed to include the impacts due to different discharge rates and temperatures. An adaptive joint estimation of the battery SOC and battery internal resistance is then presented to enhance system robustness with battery aging. The SOC estimation algorithm has been developed and verified through experiments on different types of Li-ion batteries. The results indicate that the proposed method provides an accurate SOC estimation and is computationally efficient, making it suitable for embedded system implementation.

  1. Manufacturing a low-cost ceramic water filter and filter system for the elimination of common pathogenic bacteria

    Science.gov (United States)

    Simonis, J. J.; Basson, A. K.

    Africa is one of the most water-scarce continents in the world but it is the lack of potable water which results in diarrhoea being the leading cause of death amongst children under the age of five in Africa (696 million children under 5 years old in Africa contract diarrhoea resulting in 2000 deaths per day: WHO and UNICEF, 2009). Most potable water treatment methods use bulk water treatment not suitable or available to the majority of rural poor in Sub-Saharan Africa. One simple but effective way of making sure that water is of good quality is by purifying it by means of a household ceramic water filter. The making and supply of water filters suitable for the removal of suspended solids, pathogenic bacteria and other toxins from drinking water is therefore critical. A micro-porous ceramic water filter with micron-sized pores was developed using the traditional slip casting process. This locally produced filter has the advantage of making use of less raw materials, cost, labour, energy and expertise and being more effective and efficient than other low cost produced filters. The filter is fitted with a silicone tube inserted into a collapsible bag that acts as container and protection for the filter. Enhanced flow is obtained through this filter system. The product was tested using water inoculated with high concentrations of different bacterial cultures as well as with locally polluted stream water. The filter is highly effective (log10 > 4 with 99.99% reduction efficiency) in providing protection from bacteria and suspended solids found in natural water. With correct cleaning and basic maintenance this filter technology can effectively provide drinking water to rural families affected by polluted surface water sources. This is an African solution for the more than 340 million people in Africa without access to clean drinking water (WHO and UNICEF, 2008).

  2. 40 CFR 141.560 - Is my system subject to individual filter turbidity requirements?

    Science.gov (United States)

    2010-07-01

    ... filter turbidity requirements? 141.560 Section 141.560 Protection of Environment ENVIRONMENTAL PROTECTION... Filtration and Disinfection-Systems Serving Fewer Than 10,000 People Individual Filter Turbidity Requirements § 141.560 Is my system subject to individual filter turbidity requirements? If your system is a subpart...

  3. Decentralized control and filtering in interconnected dynamical systems

    CERN Document Server

    Mahmoud, Magdi S

    2011-01-01

    Based on the many approaches available for dealing with large-scale systems (LSS), Decentralized Control and Filtering in Interconnected Dynamical Systems supplies a rigorous framework for studying the analysis, stability, and control problems of LSS. Providing an overall assessment of LSS theories, it addresses model order reduction, parametric uncertainties, time delays, and control estimator gain perturbations. Taking readers on a guided tour through LSS, the book examines recent trends and approaches and reviews past methods and results from a contemporary perspective. It traces the progre

  4. Adaptive feedback synchronization of Lue system

    International Nuclear Information System (INIS)

    Han, X.; Lu, J.-A.; Wu, X.

    2004-01-01

    This letter further improves and extends the works of Chen and Lue [Chaos, Solitons and Fractals 14 (2002) 643] and Wang et al. [Phys. Lett. A 312 (2003) 34]. In detail, the linear feedback synchronization and adaptive feedback synchronization for Lue system are discussed. And the lower bound of the feedback gain in linear feedback synchronization is presented. The adaptive feedback synchronization with only one controller is designed, which improves the proof in the work by Wang et al. The adaptive synchronization with two controllers for completely uncertain Lue system is also discussed, which extends the work of Chen and Lue. Also, numerical simulations show the effectiveness of these methods

  5. Sensorless Control of Interior Permanent Magnet Synchronous Motor in Low-Speed Region Using Novel Adaptive Filter

    Directory of Open Access Journals (Sweden)

    Lisi Tian

    2016-12-01

    Full Text Available This paper presents a novel position and speed estimation method for low-speed sensorless control of interior permanent-magnet synchronous machines (IPMSMs. The parameter design of the position and speed estimator is based on the sampled current rather than the motor electrical parameters. The proposed method not only simplifies the parameter design, it enables the estimator to work normally even in the condition that the electrical parameters are uncertain or varied. The adaptive filters are adopted to extract the desired high frequency current. The structure and corresponding transfer function are analyzed. To address the shortage of insufficient stop-band attenuation, the structure of the adaptive filter is modified to provide suitable bandwidth and stop-band attenuation simultaneously. The effectiveness of the proposed sensorless control strategy has been verified by simulations and experiments.

  6. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in brain CT

    International Nuclear Information System (INIS)

    Ren, Qingguo; Dewan, Sheilesh Kumar; Li, Ming; Li, Jianying; Mao, Dingbiao; Wang, Zhenglei; Hua, Yanqing

    2012-01-01

    Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI vol ) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique

  7. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in brain CT

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Qingguo, E-mail: renqg83@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Dewan, Sheilesh Kumar, E-mail: sheilesh_d1@hotmail.com [Department of Geriatrics, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Ming, E-mail: minli77@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Jianying, E-mail: Jianying.Li@med.ge.com [CT Imaging Research Center, GE Healthcare China, Beijing (China); Mao, Dingbiao, E-mail: maodingbiao74@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Wang, Zhenglei, E-mail: Williswang_doc@yahoo.com.cn [Department of Radiology, Shanghai Electricity Hospital, Shanghai 200050 (China); Hua, Yanqing, E-mail: cjr.huayanqing@vip.163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China)

    2012-10-15

    Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI{sub vol}) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique.

  8. Grid-Connected Photovoltaic System with Active Power Filtering Functionality

    Directory of Open Access Journals (Sweden)

    Joaquín Vaquero

    2018-01-01

    Full Text Available Solar panels are an attractive and growing source of renewable energy in commercial and residential applications. Its use connected to the grid by means of a power converter results in a grid-connected photovoltaic system. In order to optimize this system, it is interesting to integrate several functionalities into the power converter, such as active power filtering and power factor correction. Nonlinear loads connected to the grid generate current harmonics, which deteriorates the mains power quality. Active power filters can compensate these current harmonics. A photovoltaic system with added harmonic compensation and power factor correction capabilities is proposed in this paper. A sliding mode controller is employed to control the power converter, implemented on the CompactRIO digital platform from National Instruments Corporation, allowing user friendly operation and easy tuning. The power system consists of two stages, a DC/DC boost converter and a single-phase inverter, and it is able to inject active power into the grid while compensating the current harmonics generated by nonlinear loads at the point of common coupling. The operation, design, simulation, and experimental results for the proposed system are discussed.

  9. Study of the Algorithm of Backtracking Decoupling and Adaptive Extended Kalman Filter Based on the Quaternion Expanded to the State Variable for Underwater Glider Navigation

    Directory of Open Access Journals (Sweden)

    Haoqian Huang

    2014-12-01

    Full Text Available High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF based on the quaternion expanded to the state variable (BD-AEKF. The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

  10. Study of the algorithm of backtracking decoupling and adaptive extended Kalman filter based on the quaternion expanded to the state variable for underwater glider navigation.

    Science.gov (United States)

    Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping

    2014-12-03

    High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method.

  11. Study of the Algorithm of Backtracking Decoupling and Adaptive Extended Kalman Filter Based on the Quaternion Expanded to the State Variable for Underwater Glider Navigation

    Science.gov (United States)

    Huang, Haoqian; Chen, Xiyuan; Zhou, Zhikai; Xu, Yuan; Lv, Caiping

    2014-01-01

    High accuracy attitude and position determination is very important for underwater gliders. The cross-coupling among three attitude angles (heading angle, pitch angle and roll angle) becomes more serious when pitch or roll motion occurs. This cross-coupling makes attitude angles inaccurate or even erroneous. Therefore, the high accuracy attitude and position determination becomes a difficult problem for a practical underwater glider. To solve this problem, this paper proposes backing decoupling and adaptive extended Kalman filter (EKF) based on the quaternion expanded to the state variable (BD-AEKF). The backtracking decoupling can eliminate effectively the cross-coupling among the three attitudes when pitch or roll motion occurs. After decoupling, the adaptive extended Kalman filter (AEKF) based on quaternion expanded to the state variable further smoothes the filtering output to improve the accuracy and stability of attitude and position determination. In order to evaluate the performance of the proposed BD-AEKF method, the pitch and roll motion are simulated and the proposed method performance is analyzed and compared with the traditional method. Simulation results demonstrate the proposed BD-AEKF performs better. Furthermore, for further verification, a new underwater navigation system is designed, and the three-axis non-magnetic turn table experiments and the vehicle experiments are done. The results show that the proposed BD-AEKF is effective in eliminating cross-coupling and reducing the errors compared with the conventional method. PMID:25479331

  12. Adaptive, full-spectrum solar energy system

    Science.gov (United States)

    Muhs, Jeffrey D.; Earl, Dennis D.

    2003-08-05

    An adaptive full spectrum solar energy system having at least one hybrid solar concentrator, at least one hybrid luminaire, at least one hybrid photobioreactor, and a light distribution system operably connected to each hybrid solar concentrator, each hybrid luminaire, and each hybrid photobioreactor. A lighting control system operates each component.

  13. Adaptive Dialogue Systems for Assistive Living Environments

    Science.gov (United States)

    Papangelis, Alexandros

    2013-01-01

    Adaptive Dialogue Systems (ADS) are intelligent systems, able to interact with users via multiple modalities, such as speech, gestures, facial expressions and others. Such systems are able to make conversation with their users, usually on a specific, narrow topic. Assistive Living Environments are environments where the users are by definition not…

  14. Adaptive bilateral filter for image denoising and its application to in-vitro Time-of-Flight data

    Science.gov (United States)

    Seitel, Alexander; dos Santos, Thiago R.; Mersmann, Sven; Penne, Jochen; Groch, Anja; Yung, Kwong; Tetzlaff, Ralf; Meinzer, Hans-Peter; Maier-Hein, Lena

    2011-03-01

    Image-guided therapy systems generally require registration of pre-operative planning data with the patient's anatomy. One common approach to achieve this is to acquire intra-operative surface data and match it to surfaces extracted from the planning image. Although increasingly popular for surface generation in general, the novel Time-of-Flight (ToF) technology has not yet been applied in this context. This may be attributed to the fact that the ToF range images are subject to considerable noise. The contribution of this study is two-fold. Firstly, we present an adaption of the well-known bilateral filter for denoising ToF range images based on the noise characteristics of the camera. Secondly, we assess the quality of organ surfaces generated from ToF range data with and without bilateral smoothing using corresponding high resolution CT data as ground truth. According to an evaluation on five porcine organs, the root mean squared (RMS) distance between the denoised ToF data points and the reference computed tomography (CT) surfaces ranged from 3.0 mm (lung) to 9.0 mm (kidney). This corresponds to an error-reduction of up to 36% compared to the error of the original ToF surfaces.

  15. Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot

    Directory of Open Access Journals (Sweden)

    Yuan Xu

    2014-01-01

    Full Text Available As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF which used the noise statistics estimator in the iterated extended Kalman (IEKF, and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS/wireless sensors networks (WSNs-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF.

  16. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

    Directory of Open Access Journals (Sweden)

    Álvaro Moreno

    2014-08-01

    Full Text Available Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted local regression filter (LOESS and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG, smoothing spline (SSP, Interpolation for Data Reconstruction (IDR and adaptive Savitzky-Golay (ASG. This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20% reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal

  17. Complex and adaptive dynamical systems a primer

    CERN Document Server

    Gros, Claudius

    2007-01-01

    We are living in an ever more complex world, an epoch where human actions can accordingly acquire far-reaching potentialities. Complex and adaptive dynamical systems are ubiquitous in the world surrounding us and require us to adapt to new realities and the way of dealing with them. This primer has been developed with the aim of conveying a wide range of "commons-sense" knowledge in the field of quantitative complex system science at an introductory level, providing an entry point to this both fascinating and vitally important subject. The approach is modular and phenomenology driven. Examples of emerging phenomena of generic importance treated in this book are: -- The small world phenomenon in social and scale-free networks. -- Phase transitions and self-organized criticality in adaptive systems. -- Life at the edge of chaos and coevolutionary avalanches resulting from the unfolding of all living. -- The concept of living dynamical systems and emotional diffusive control within cognitive system theory. Techn...

  18. Complex and Adaptive Dynamical Systems A Primer

    CERN Document Server

    Gros, Claudius

    2011-01-01

    We are living in an ever more complex world, an epoch where human actions can accordingly acquire far-reaching potentialities. Complex and adaptive dynamical systems are ubiquitous in the world surrounding us and require us to adapt to new realities and the way of dealing with them. This primer has been developed with the aim of conveying a wide range of "commons-sense" knowledge in the field of quantitative complex system science at an introductory level, providing an entry point to this both fascinating and vitally important subject. The approach is modular and phenomenology driven. Examples of emerging phenomena of generic importance treated in this book are: -- The small world phenomenon in social and scale-free networks. -- Phase transitions and self-organized criticality in adaptive systems. -- Life at the edge of chaos and coevolutionary avalanches resulting from the unfolding of all living. -- The concept of living dynamical systems and emotional diffusive control within cognitive system theory. Techn...

  19. Electrically heated particulate filter regeneration methods and systems for hybrid vehicles

    Science.gov (United States)

    Gonze, Eugene V.; Paratore, Jr., Michael J.

    2010-10-12

    A control system for controlling regeneration of a particulate filter for a hybrid vehicle is provided. The system generally includes a regeneration module that controls current to the particulate filter to initiate regeneration. An engine control module controls operation of an engine of the hybrid vehicle based on the control of the current to the particulate filter.

  20. System health monitoring using multiple-model adaptive estimation techniques

    Science.gov (United States)

    Sifford, Stanley Ryan

    Monitoring system health for fault detection and diagnosis by tracking system parameters concurrently with state estimates is approached using a new multiple-model adaptive estimation (MMAE) method. This novel method is called GRid-based Adaptive Parameter Estimation (GRAPE). GRAPE expands existing MMAE methods by using new techniques to sample the parameter space. GRAPE expands on MMAE with the hypothesis that sample models can be applied and resampled without relying on a predefined set of models. GRAPE is initially implemented in a linear framework using Kalman filter models. A more generalized GRAPE formulation is presented using extended Kalman filter (EKF) models to represent nonlinear systems. GRAPE can handle both time invariant and time varying systems as it is designed to track parameter changes. Two techniques are presented to generate parameter samples for the parallel filter models. The first approach is called selected grid-based stratification (SGBS). SGBS divides the parameter space into equally spaced strata. The second approach uses Latin Hypercube Sampling (LHS) to determine the parameter locations and minimize the total number of required models. LHS is particularly useful when the parameter dimensions grow. Adding more parameters does not require the model count to increase for LHS. Each resample is independent of the prior sample set other than the location of the parameter estimate. SGBS and LHS can be used for both the initial sample and subsequent resamples. Furthermore, resamples are not required to use the same technique. Both techniques are demonstrated for both linear and nonlinear frameworks. The GRAPE framework further formalizes the parameter tracking process through a general approach for nonlinear systems. These additional methods allow GRAPE to either narrow the focus to converged values within a parameter range or expand the range in the appropriate direction to track the parameters outside the current parameter range boundary

  1. Adaptive processes in economic systems

    CERN Document Server

    Murphy, Roy E

    1965-01-01

    In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;. methods for low-rank m

  2. Filter Bank Approach to the Estimation of Flexible Modes in Dynamic Systems

    National Research Council Canada - National Science Library

    Tzellos, Konstantinos

    2007-01-01

    .... In this thesis the problem of identifying frequencies of disturbances in flexible systems using advanced Digital Signal Processing techniques such as filter banks and Quadrature Mirror Filters is addressed...

  3. Adaptive networks as second order governance systems

    NARCIS (Netherlands)

    S.G. Nooteboom (Sibout); P.K. Marks (Peter)

    2010-01-01

    textabstractWe connect the idea of 'levers for change' with 'governance capacity' and propose 'adaptive networks' as an ideal type embedded in, and leveraging change in, governance systems. Discourses connect practices of citizens and companies with that governance system. Aware of

  4. Optimizing Cost of Continuous Overlapping Queries over Data Streams by Filter Adaption

    KAUST Repository

    Xie, Qing; Zhang, Xiangliang; Li, Zhixu; Zhou, Xiaofang

    2016-01-01

    The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially

  5. A hybrid active filter for damping of harmonic resonance in industrial power systems

    OpenAIRE

    Fujita, Hideaki; Yamasaki, Takahiro; Akagi, Hirofumi

    1998-01-01

    This paper proposes a hybrid active filter for damping of harmonic resonance in industrial power systems. The hybrid filter consists of a small-rated active filter and a 5th tuned passive filter. The active filter is characterized by detecting the 5th harmonic current flowing into the passive filter. It is controlled in such a way as to behave as a negative or positive resistor by adjusting a feedback gain from a negative to positive value, and vice versa. The negative resistor presented by t...

  6. Adaptation in the auditory system: an overview

    Directory of Open Access Journals (Sweden)

    David ePérez-González

    2014-02-01

    Full Text Available The early stages of the auditory system need to preserve the timing information of sounds in order to extract the basic features of acoustic stimuli. At the same time, different processes of neuronal adaptation occur at several levels to further process the auditory information. For instance, auditory nerve fiber responses already experience adaptation of their firing rates, a type of response that can be found in many other auditory nuclei and may be useful for emphasizing the onset of the stimuli. However, it is at higher levels in the auditory hierarchy where more sophisticated types of neuronal processing take place. For example, stimulus-specific adaptation, where neurons show adaptation to frequent, repetitive stimuli, but maintain their responsiveness to stimuli with different physical characteristics, thus representing a distinct kind of processing that may play a role in change and deviance detection. In the auditory cortex, adaptation takes more elaborate forms, and contributes to the processing of complex sequences, auditory scene analysis and attention. Here we review the multiple types of adaptation that occur in the auditory system, which are part of the pool of resources that the neurons employ to process the auditory scene, and are critical to a proper understanding of the neuronal mechanisms that govern auditory perception.

  7. Managing adaptively for multifunctionality in agricultural systems

    Science.gov (United States)

    Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig R.; Magda, Danièle

    2016-01-01

    The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn’t reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to

  8. Managing adaptively for multifunctionality in agricultural systems.

    Science.gov (United States)

    Hodbod, Jennifer; Barreteau, Olivier; Allen, Craig; Magda, Danièle

    2016-12-01

    The critical importance of agricultural systems for food security and as a dominant global landcover requires management that considers the full dimensions of system functions at appropriate scales, i.e. multifunctionality. We propose that adaptive management is the most suitable management approach for such goals, given its ability to reduce uncertainty over time and support multiple objectives within a system, for multiple actors. As such, adaptive management may be the most appropriate method for sustainably intensifying production whilst increasing the quantity and quality of ecosystem services. However, the current assessment of performance of agricultural systems doesn't reward ecosystem service provision. Therefore, we present an overview of the ecosystem functions agricultural systems should and could provide, coupled with a revised definition for assessing the performance of agricultural systems from a multifunctional perspective that, when all satisfied, would create adaptive agricultural systems that can increase production whilst ensuring food security and the quantity and quality of ecosystem services. The outcome of this high level of performance is the capacity to respond to multiple shocks without collapse, equity and triple bottom line sustainability. Through the assessment of case studies, we find that alternatives to industrialized agricultural systems incorporate more functional goals, but that there are mixed findings as to whether these goals translate into positive measurable outcomes. We suggest that an adaptive management perspective would support the implementation of a systematic analysis of the social, ecological and economic trade-offs occurring within such systems, particularly between ecosystem services and functions, in order to provide suitable and comparable assessments. We also identify indicators to monitor performance at multiple scales in agricultural systems which can be used within an adaptive management framework to increase

  9. Adaptive PI Controller for a Nonlinear System

    Directory of Open Access Journals (Sweden)

    D. Rathikarani

    2009-10-01

    Full Text Available Most of the industrial processes are inherently nonlinear in their behaviour. Designs of controllers for these nonlinear processes are difficult, as they do not follow superposition theorem. Adaptive controller can change its behaviour in response to changes in the dynamics of the process and disturbances. Hence adaptive controller can be used to control nonlinear processes. Direct Model Reference Adaptive Control is a technique, in which a reference model involving the desired performances is specified. In the present work, a DMRAC is designed and implemented to achieve satisfactory control of a nonlinear system in all its local linear operating regions. The closed loop system is made BIBO stable by proper control techniques. The controller is designed through simulation in Matlab platform and is validated in real time by conducting experiments on the laboratory Air Flow Control System using the dSPACE interface.

  10. Adaptive control of solar energy collector systems

    CERN Document Server

    Lemos, João M; Igreja, José M

    2014-01-01

    This book describes methods for adaptive control of distributed-collector solar fields: plants that collect solar energy and deliver it in thermal form. Controller design methods are presented that can overcome difficulties found in these type of plants:they are distributed-parameter systems, i.e., systems with dynamics that depend on space as well as time;their dynamics is nonlinear, with a bilinear structure;there is a significant level of uncertainty in plant knowledge.Adaptive methods form the focus of the text because of the degree of uncertainty in the knowledge of plant dynamics. Parts

  11. Environmentally-adapted local energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Moe, N; Oefverholm, E [NUTEK, Stockholm (Sweden); Andersson, Owe [EKAN Gruppen (Sweden); Froste, H [Swedish Environmental Protection Agency, Stockholm (Sweden)

    1997-10-01

    Energy companies, municipalities, property companies, firms of consultants, environmental groups and individuals are examples of players working locally to shape environmentally adapted energy systems. These players have needed information making them better able to make decisions on cost-efficient, environmentally-adapted energy systems. This book answers many of the questions they have put. The volume is mainly based on Swedish handbooks produced by the Swedish National Board for Industrial and Technical Development, NUTEK, together with the Swedish Environmental Protection Agency. These handbooks have been used in conjunction with municipal energy planning, local Agenda 21 work, to provide a basis for deciding on concrete local energy systems. The contents in brief: -The book throws new light on the concept of energy efficiency; -A section on the environment compares how air-polluting emissions vary with different methods of energy production; -A section contains more than 40 ideas for measures which can be profitable, reduce energy consumption and the impact on the environment all at the same time; -The book gives concrete examples of new, alternative and environmentally-adapted local energy systems. More efficient use of energy is included as a possible change of energy system; -The greatest emphasis is laid upon alternative energy systems for heating. It may be heating in a house, block of flats, office building or school; -Finally, there are examples of environmentally-adapted local energy planning.

  12. Adaptive Image Transmission Scheme over Wavelet-Based OFDM System

    Institute of Scientific and Technical Information of China (English)

    GAOXinying; YUANDongfeng; ZHANGHaixia

    2005-01-01

    In this paper an adaptive image transmission scheme is proposed over Wavelet-based OFDM (WOFDM) system with Unequal error protection (UEP) by the design of non-uniform signal constellation in MLC. Two different data division schemes: byte-based and bitbased, are analyzed and compared. Different bits are protected unequally according to their different contribution to the image quality in bit-based data division scheme, which causes UEP combined with this scheme more powerful than that with byte-based scheme. Simulation results demonstrate that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality. Furthermore, by considering the tradeoff of complexity and BER performance, Haar wavelet with the shortest compactly supported filter length is the most suitable one among orthogonal Daubechies wavelet series in our proposed system.

  13. Di-nuclear systems studied with the velocity filter SHIP

    Energy Technology Data Exchange (ETDEWEB)

    Heinz, S.; Hessberger, F.P.; Ackermann, D.; Burkhard, H.G.; Khuyagbaatar, J.; Kindler, B.; Lommel, B.; Mann, R.; Maurer, J. [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt (Germany); Comas, V.; Heredia, J. [Institute of Technologies and Applied Sciences, Habana (Cuba); Hofmann, S. [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Darmstadt (Germany); Goethe-Universitaet Frankfurt, Institut fuer Physik, Frankfurt (Germany); Gan, Z. [Chinese Academy of Sciences, Institute of Modern Physics, Lanzhou (China); Nishio, K. [Japan Atomic Energy Agency (JAEA), Tokai, Ibaraki (Japan); Sulignano, B. [DAPNIA / SPhN, CEA Saclay, Gif-sur-Yvette Cedex (France)

    2008-11-15

    Various nuclear reactions like quasi-fission, fusion-fission or particle and cluster evaporation from excited compound nuclei were studied in heavy-ion reactions at the velocity filter SHIP of GSI. The velocity filter offers the possibility to detect all reaction products under zero degree relative to the beam direction. Together with the measurement of the product velocity distribution this allows for an identification of the underlying reaction mechanism. This article is focussed on reactions of {sup 25}Mg and {sup 64}Ni beams on {sup 206,207}Pb targets at energies of 5.9 x A MeV and 8.7 x A MeV. Besides evaporation residues from {sup 25}Mg+{sup 206}Pb collisions we found evidence for rotation and quasi-fission of nuclear molecules formed in the entrance channel after the capture stage. The break-up of the systems showed a preferred clustering leading to isotopes in the region 84{<=}Z{<=}88 and 122{<=}N{<=}127 of the chart of nuclei. (orig.)

  14. Identification of lead acid battery parameters using kalman filtering in photovoltaic system

    International Nuclear Information System (INIS)

    Boutte, Aissa

    2006-01-01

    The conventional methods of battery identification parameters consist in estimating the state of charge (SOC), and in establishing a command adapted to charge or to discharge the battery, based on electrical model developed with fixed parameters, These methods are inefficient. The causes of this ineffectiveness are different: In the first place model does not adapt itself with the battery (fixed parameters, lack of modulated parameters, a big non-linearity ...).Secondly, the impossibility for the developed algorithms, to adapt itself with the change of the battery's parameters. New models of identification are used by combining the conventional methods with adaptive and dynamic techniques. They already used in other domains where they have proved a good efficiency and a robustness. Taking into consideration the problems mentioned, and trying to resolve them, we have chosen among the various methods of estimation, Kalman filter (KF) known for its efficiency, in the field of tracking parameters. In this work we try tp represent new ideas, to identify battery parameters using KF method and make an experimental analysis of the performance of this method by using Lead Acid Battery, which is a part of a photovoltaic system (PV).(Author)

  15. Filter-Adapted Fluorescent In Situ Hybridization (FA-FISH) for Filtration-Enriched Circulating Tumor Cells.

    Science.gov (United States)

    Oulhen, Marianne; Pailler, Emma; Faugeroux, Vincent; Farace, Françoise

    2017-01-01

    Circulating tumor cells (CTCs) may represent an easily accessible source of tumor material to assess genetic aberrations such as gene-rearrangements or gene-amplifications and screen cancer patients eligible for targeted therapies. As the number of CTCs is a critical parameter to identify such biomarkers, we developed fluorescent in situ hybridization (FISH) for CTCs enriched on filters (filter-adapted-FISH, FA-FISH). Here, we describe the FA-FISH protocol, the combination of immunofluorescent staining (DAPI/CD45) and FA-FISH techniques, as well as the semi-automated microscopy method that we developed to improve the feasibility and reliability of FISH analyses in filtration-enriched CTC.

  16. Adaptive polymeric system for Hebbian type learning

    OpenAIRE

    2011-01-01

    Abstract We present the experimental realization of an adaptive polymeric system displaying a ?learning behaviour?. The system consists on a statistically organized networks of memristive elements (memory-resitors) based on polyaniline. In a such network the path followed by the current increments its conductivity, a property which makes the system able to mimic Hebbian type learning and have application in hardware neural networks. After discussing the working principle of ...

  17. Improvement of cement plant dust emission by bag filter system

    Science.gov (United States)

    Wahyu Purnomo, Chandra; Budhijanto, Wiratni; Alfisyah, Muziibu; Triyono

    2018-03-01

    The limestone quarry in PT Indocement Tunggal Prakarsa (ITP) in Cirebon is considered as a complex quarry in terms of chemical composition and material hardness. From the beginning of the plant operation up to the end of 2015, the dust removal was rely on electrostatic precipitator (EP) system. Whenever limestone from specific quarry zones were incorporated into Raw Mill (RM) feed or there was an upset condition, the dust emission increased significantly. Beside higher demand of electricity, an EP system requires lower gas inlet temperature in order to remove the dust effectively which requires larger cooling water in the previous gas conditioning tower to cool down gas from 400 °C to about 100 °C. By considering the drawbacks, the EP system was replaced by a bag filter (BF) system. The BF allows higher temperature of gas inlet and it has higher dust removal efficiency. In this study, the efficiency of the two different dust removal systems is compared. The effect of process variables i.e. RM feed, kiln feed, inlet temperature and pressure, and small size particle fraction to the dust emission are studied by multivariate linier regression analysis. It is observed that the BF system can reduce significantly the dust emission from 30 to 6 mg/m3 and in the same time reducing CO2 emission by 0.24 ton/year from the electricity consumption saving.

  18. A novel extended Kalman filter for a class of nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    DONG Zhe; YOU Zheng

    2006-01-01

    Estimation of the state variables of nonlinear systems is one of the fundamental and significant problems in control and signal processing. A new extended Kalman filtering approach for a class of nonlinear discrete-time systems in engineering is presented in this paper. In contrast to the celebrated extended Kalman filter (EKF), there is no linearization operation in the design procedure of the filter, and the parameters of the filter are obtained through minimizing a proper upper bound of the mean-square estimation error. Simulation results show that this filter can provide higher estimation precision than that provided by the EKF.

  19. On complex adaptive systems and terrorism

    International Nuclear Information System (INIS)

    Ahmed, E.; Elgazzar, A.S.; Hegazi, A.S.

    2005-01-01

    Complex adaptive systems (CAS) are ubiquitous in nature. They are basic in social sciences. An overview of CAS is given with emphasize on the occurrence of bad side effects to seemingly 'wise' decisions. Hence application to terrorism is given. Some conclusions on how to deal with this phenomena are proposed

  20. Adaptive radiotherapy using helical tomotherapy system

    International Nuclear Information System (INIS)

    Jeswani, Sam; Ruchala, Kenneth; Olivera, Gustavo; Mackie, T.R.

    2008-01-01

    As commonly known in the field, adaptive radiation therapy (ART) is the use of feedback to modify a radiotherapy treatment. There are numerous ways in which this feedback can be received and used, and this presentation will discuss some of the implementations of ART being investigated with a helical TomoTherapy system

  1. Cross-bandwidth adaptation for ASR systems

    CSIR Research Space (South Africa)

    Kleynhans, N

    2013-12-01

    Full Text Available not be feasible for resource-scarce environments. Utilising limited amounts of in-domain data and a combination of feature normalisation and acoustic model adaptation techniques has therefore found wide use in ASR systems. Various approaches have been proposed...

  2. Two Perspectives on Information System Adaptation

    DEFF Research Database (Denmark)

    Jensen, Tina Blegind; Kjærgaard, Annemette; Svejvig, Per

    Institutional theory has proven to be a central analytical perspective for investigating the role of larger social and historical structures of Information System (IS) adaptation. However, it does not explicitly account for how organizational actors make sense of and enact IS in their local context...

  3. ROBUST KALMAN FILTERING FOR SYSTEMS UNDER NORM BOUNDED UNCERTAINTIES IN ALL SYSTEM MATRICES AND ERROR COVARIANCE CONSTRAINTS

    Institute of Scientific and Technical Information of China (English)

    XIA Yuanqing; HAN Jingqing

    2005-01-01

    This paper concerns robust Kalman filtering for systems under norm bounded uncertainties in all the system matrices and error covariance constraints. Sufficient conditions are given for the existence of such filters in terms of Riccati equations. The solutions to the conditions can be used to design the filters. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed design procedure.

  4. Frequency offset estimation in OFDM systems using Bayesian filtering

    Science.gov (United States)

    Yu, Yihua

    2011-10-01

    Orthogonal frequency division multiplexing (OFDM) is sensitive to carrier frequency offset (CFO) that causes inter-carrier interference (ICI). In this paper, we present two schemes for the CFO estimation, which are based on rejection sampling (RS) and a form of particle filtering (PF) called kernel smoothing technique, respectively. The first scheme is offline estimation, where the observations contained in the OFDM training symbol are treated in the batch mode. The second scheme is online estimation, where the observations in the OFDM training symbol are treated in the sequential manner. Simulations are provided to illustrate the performances of the schemes. Performance comparisons of the two schemes and with other Bayesian methods are provided. Simulation results show that the two schemes are effective when estimating the CFO and can effectively combat the effect of ICI in OFDM systems.

  5. Filter-fluorescer diagnostic system for the National Ignition Facility

    International Nuclear Information System (INIS)

    McDonald, J.W.; Kauffman, R.L.; Celeste, J.R.; Rhodes, M.A.; Lee, F.D.; Suter, L.J.; Lee, A.P.; Foster, J.M.; Slark, G.

    2004-01-01

    An early filter-fluorescer diagnostic system is being fielded at the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) to measure the amount of hard x rays (20< hν<150 keV) generated in laser fusion experiments. From these measurements we hope to quantify the number of hot electrons produced in laser fusion experiments. The measurement of hot electron production is important for ignition experiments because these electrons can preheat the fuel capsule. Hot electrons can also be employed in experimentation by preheating hydrodynamic packages or by driving plasmas out of equilibrium. The experimental apparatus, data collection, analysis and calibration issues are discussed. Expected data signal levels are predicted and discussed

  6. Adaptive Integration of Nonsmooth Dynamical Systems

    Science.gov (United States)

    2017-10-11

    2017 W911NF-12-R-0012-03: Adaptive Integration of Nonsmooth Dynamical Systems The views, opinions and/or findings contained in this report are those of...Integration of Nonsmooth Dynamical Systems Report Term: 0-Other Email: drum@gwu.edu Distribution Statement: 1-Approved for public release; distribution is...classdrake_1_1systems_1_1_integrator_base.html ; 3) a solver for dynamical systems with arbitrary unilateral and bilateral constraints (the key component of the time stepping systems )- see

  7. Adaptive intrusion data system (AIDS) software routines

    International Nuclear Information System (INIS)

    Corlis, N.E.

    1980-07-01

    An Adaptive Intrusion Data System (AIDS) was developed to collect information from intrusion alarm sensors as part of an evaluation system to improve sensor performance. AIDS is a unique digital data-compression, storage, and formatting system; it also incorporates a capability for video selection and recording for assessment of the sensors monitored by the system. The system is software reprogrammable to numerous configurations that may be used for the collection of environmental, bilevel, analog, and video data. This report describes the software routines that control the different AIDS data-collection modes, the diagnostic programs to test the operating hardware, and the data format. Sample data printouts are also included

  8. Conservation of filtering in manufacturing systems with unreliable machines and finished goods buffers

    Directory of Open Access Journals (Sweden)

    2006-01-01

    Full Text Available This paper addresses the issue of reliable satisfaction of customer demand by unreliable production systems. In the framework of a simple production-storage-customer model, we show that this can be accomplished by using an appropriate level of filtering of production randomness. The filtering is ensured by finished goods buffers (filtering in space and shipping periods (filtering in time. The following question is considered: how are filtering in space and filtering in time interrelated? As an answer, we show that there exists a conservation law: in lean manufacturing systems, the amount of filtering in space multiplied by the amount of filteringin time (both measured in appropriate dimensionless units ispractically constant. Along with providing an insight into the nature of manufacturing systems, this law offers a tool for selecting the smallest, that is, lean, finished goods buffering, which is necessary and sufficient to ensure the desired level ofcustomer demand satisfaction.

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

    Science.gov (United States)

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

    2017-02-01

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

  10. Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

    DEFF Research Database (Denmark)

    Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine

    2017-01-01

    The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students’ individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von...... Foerster to shed light on how the educational system has used and understood adaptation. In this context, we point out two different approaches to educational adaptation: 1) students adapting to the educational system and 2) the attempt of the educational system to adapt to students through automatized...... system adaptation and recommendation systems. These different understandings constitute a design framework that is used to analyze two current trends: Adaptive learning systems and learning analytics. Finally, the paper discusses the potential of looking at adaptation as recommendation systems...

  11. Changing Paradigms: From Schooling to Schools as Adaptive Recommendation Systems

    DEFF Research Database (Denmark)

    Christiansen, René Boyer; Gynther, Karsten; Petersen, Anne Kristine

    2017-01-01

    The paper explores a shift in education from educational systems requiring student adaptation to educational recommendation systems adapting to students’ individual needs. The paper discusses the concept of adaptation as addressed in educational research and draws on the system theory of Heinz von...... system adaptation and recommendation systems. These different understandings constitute a design framework that is used to analyze two current trends: Adaptive learning systems and learning analytics. Finally, the paper discusses the potential of looking at adaptation as recommendation systems...... Foerster to shed light on how the educational system has used and understood adaptation. In this context, we point out two different approaches to educational adaptation: 1) students adapting to the educational system and 2) the attempt of the educational system to adapt to students through automatized...

  12. Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems

    International Nuclear Information System (INIS)

    Claassen, J.P.; Patterson, M.M.

    1981-01-01

    Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals

  13. Method for filtering radon from a gas system

    International Nuclear Information System (INIS)

    Sowinski, R.F.

    1992-01-01

    This patent describes a method of filtering, adjacent to an end user-customer's residence, or business in which at least a single gas appliance is located, a natural gas stream in which benz-a-anthracene has been concentrated at sufficient levels to be a health threat in a natural gas gathering and distributing network. It comprises introducing the natural gas stream to a filter selected from a group that includes impingement, passing the filtered natural gas stream to the customer's gas appliance wherein safe use of the energy associated with the stream occurs, periodically and safely removing the filter for disposing of captured benz-a-anthracene, inserting a new filter in place of the removed filter of step

  14. A tool for filtering information in complex systems

    Science.gov (United States)

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

    2005-07-01

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

  15. Processing and Linguistics Properties of Adaptable Systems

    Directory of Open Access Journals (Sweden)

    Dumitru TODOROI

    2006-01-01

    Full Text Available Continuation and development of the research in Adaptable Programming Initialization [Tod-05.1,2,3] is presented. As continuation of [Tod-05.2,3] in this paper metalinguistic tools used in the process of introduction of new constructions (data, operations, instructions and controls are developed. The generalization schemes of evaluation of adaptable languages and systems are discussed. These results analogically with [Tod-05.2,3] are obtained by the team, composed from the researchers D. Todoroi [Tod-05.4], Z. Todoroi [ZTod-05], and D. Micusa [Mic-03]. Presented results will be included in the book [Tod-06].

  16. Indirect adaptive control of discrete chaotic systems

    International Nuclear Information System (INIS)

    Salarieh, Hassan; Shahrokhi, Mohammad

    2007-01-01

    In this paper an indirect adaptive control algorithm is proposed to stabilize the fixed points of discrete chaotic systems. It is assumed that the functionality of the chaotic dynamics is known but the system parameters are unknown. This assumption is usually applicable to many chaotic systems, such as the Henon map, logistic and many other nonlinear maps. Using the recursive-least squares technique, the system parameters are identified and based on the feedback linearization method an adaptive controller is designed for stabilizing the fixed points, or unstable periodic orbits of the chaotic maps. The stability of the proposed scheme has been shown and the effectiveness of the control algorithm has been demonstrated through computer simulations

  17. A tool for filtering information in complex systems

    OpenAIRE

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

    2005-01-01

    We introduce a technique to filter out complex data sets by extracting a subgraph of representative links. Such a filtering can be tuned up to any desired level by controlling the genus of the resulting graph. We show that this technique is especially suitable for correlation-based graphs, giving filtered graphs that preserve the hierarchical organization of the minimum spanning tree but containing a larger amount of information in their internal structure. In particular in the case of planar...

  18. Post Fukushima requirement of containment filtered venting system in NPPS

    International Nuclear Information System (INIS)

    Deo, Anuj Kumar; Bera, S.; Nagrale, D.B.; Lakshmanan, S.P.; Baburajan, P.K.; Paul, U.K.; Gaikwad, A.J.

    2015-01-01

    Post Fukushima safety enhancement through provision of an additional layer of Defence-in-Depth in the existing and new Indian nuclear power plants has led to the need of containment filtered venting system (CFVS). The regulatory review of the design of CFVS is in progress. In order to assess the same, the regulatory knowledge base had to be generated on the current state of the art of the design of such a system by study of the international experience on this system available in the open literature. The regulatory stand on requirements and implementation status of the CFVS in various countries were also studied. The information available on design features of various kinds of venting systems, relevant design basis and/or acceptance criteria were collected for supporting the design safety review of the Indian CFVS under consideration. During the on-going regulatory review process several analyses have been carried out, some more are in progress, to support the deliberations and decision making. This paper presents the above mentioned information and the summary of the analyses carried out including the status and outcome. Important aspects of the design review and associated analyses are also presented in this paper which includes the descriptions of the work on CFD study of venturi atomization, thermal hydraulics studies, shielding analysis and source term estimation studies carried out by the regulatory body. (author)

  19. Floating-point-based hardware accelerator of a beam phase-magnitude detector and filter for a beam phase control system in a heavy-ion synchrotron application

    International Nuclear Information System (INIS)

    Samman, F.A.; Pongyupinpanich Surapong; Spies, C.; Glesner, M.

    2012-01-01

    A hardware implementation of an adaptive phase and magnitude detector and filter of a beam-phase control system in a heavy ion synchrotron application is presented in this paper. The main components of the hardware are adaptive LMS (Least-Mean-Square) filters and phase and magnitude detectors. The phase detectors are implemented by using a CORDIC (Coordinate Rotation Digital Computer) algorithm based on 32-bit binary floating-point arithmetic data formats. The floating-point-based hardware is designed to improve the precision of the past hardware implementation that were based on fixed-point arithmetics. The hardware of the detector and the adaptive LMS filter have been implemented on a programmable logic device (FPGA) for hardware acceleration purpose. The ideal Matlab/Simulink model of the hardware and the VHDL model of the adaptive LMS filter and the phase and magnitude detector are compared. The comparison result shows that the output signal of the floating-point based adaptive FIR filter as well as the phase and magnitude detector agree with the expected output signal of the ideal Matlab/Simulink model. (authors)

  20. Inverter Output Filter Effect on PWM Motor Drives of a Flywheel Energy Storage System

    Science.gov (United States)

    Santiago, Walter

    2004-01-01

    NASA Glenn Research Center (GRC) has been involved in the research and development of high speed flywheel systems for small satellite energy storage and attitude control applications. One research and development area has been the minimization of the switching noise produced by the pulsed width modulated (PWM) inverter that drives the flywheel permanent magnet motor/generator (PM M/G). This noise can interfere with the flywheel M/G hardware and the system avionics hampering the full speed performance of the flywheel system. One way to attenuate the inverter switching noise is by placing an AC filter at the three phase output terminals of the inverter with the filter neutral point connected to the DC link (DC bus) midpoint capacitors. The main benefit of using an AC filter in this fashion is the significant reduction of the inverter s high dv/dt switching and its harmonics components. Additionally, common mode (CM) and differential mode (DM) voltages caused by the inverter s high dv/dt switching are also reduced. Several topologies of AC filters have been implemented and compared. One AC filter topology consists of a two-stage R-L-C low pass filter. The other topology consists of the same two-stage R-L-C low pass filter with a series connected trap filter (an inductor and capacitor connected in parallel). This paper presents the analysis, design and experimental results of these AC filter topologies and the comparison between the no filter case and conventional AC filter.

  1. Software Sub-system in Loading Automatic Test System for the Measurement of Power Line Filters

    Directory of Open Access Journals (Sweden)

    Yu Bo

    2017-01-01

    Full Text Available The loading automatic test system for measurement of power line filters are in urgent demand. So the software sub-system of the whole test system was proposed. Methods: structured the test system based on the virtual instrument framework, which consisted of lower and up computer and adopted the top down approach of design to perform the system and its modules, according to the measurement principle of the test system. Results: The software sub-system including human machine interface, data analysis and process software, expert system, communication software, control software in lower computer, etc. had been designed. Furthermore, it had been integrated into the entire test system. Conclusion: This sub-system provided a fiendly software platform for the whole test system, and had many advantages such as strong functions, high performances, low prices. It not only raises the test efficiency of EMI filters, but also renders some creativities.

  2. High-dynamic range compressive spectral imaging by grayscale coded aperture adaptive filtering

    Directory of Open Access Journals (Sweden)

    Nelson Eduardo Diaz

    2015-09-01

    Full Text Available The coded aperture snapshot spectral imaging system (CASSI is an imaging architecture which senses the three dimensional informa-tion of a scene with two dimensional (2D focal plane array (FPA coded projection measurements. A reconstruction algorithm takes advantage of the compressive measurements sparsity to recover the underlying 3D data cube. Traditionally, CASSI uses block-un-block coded apertures (BCA to spatially modulate the light. In CASSI the quality of the reconstructed images depends on the design of these coded apertures and the FPA dynamic range. This work presents a new CASSI architecture based on grayscaled coded apertu-res (GCA which reduce the FPA saturation and increase the dynamic range of the reconstructed images. The set of GCA is calculated in a real-time adaptive manner exploiting the information from the FPA compressive measurements. Extensive simulations show the attained improvement in the quality of the reconstructed images when GCA are employed.  In addition, a comparison between traditional coded apertures and GCA is realized with respect to noise tolerance.

  3. Adaptive traffic control systems for urban networks

    Directory of Open Access Journals (Sweden)

    Radivojević Danilo

    2017-01-01

    Full Text Available Adaptive traffic control systems represent complex, but powerful tool for improvement of traffic flow conditions in locations or zones where applied. Many traffic agencies, especially those that have a large number of signalized intersections with high variability of the traffic demand, choose to apply some of the adaptive traffic control systems. However, those systems are manufactured and offered by multiple vendors (companies that are competing for the market share. Due to that fact, besides the information available from the vendors themselves, or the information from different studies conducted on different continents, very limited amount of information is available about the details how those systems are operating. The reason for that is the protecting of the intellectual property from plagiarism. The primary goal of this paper is to make a brief analysis of the functionalities, characteristics, abilities and results of the most recognized, but also less known adaptive traffic control systems to the professional public and other persons with interest in this subject.

  4. Study of LCL filter performance for inverter fed grid connected system

    Science.gov (United States)

    Thamizh Thentral, T. M.; Geetha, A.; Subramani, C.

    2018-04-01

    The abandoned use of power electronic converters in the application of grid connected system paves a way for critical injected harmonics. Hence the use of filter becomes a significant play among the present scenario. Higher order passive filter is mostly preferred in this application because of its reduced cost and size. This paper focuses on the design of LCL filter for the reduction of injected harmonics. The reason behind choosing LCL filter is inductor sizing and good ripple component attenuation over the other conventional filters. This work is simulated in MATLAB platform and the results are prominent to the objectives mentioned above. Also, the simulation results are verified with the implemented hardware model.

  5. Hybrid Active Filter with Variable Conductance for Harmonic Resonance Suppression in Industrial Power Systems

    DEFF Research Database (Denmark)

    Lee, Tzung-Lin; Wang, Yen-Ching; Li, Jian-Cheng

    2015-01-01

    Unintentional series and/or parallel resonances, due to the tuned passive filter and the line inductance, may result in severe harmonic distortion in the industrial power system. This paper presents a hybrid active filter to suppress harmonic resonance and reduce harmonic distortion as well...... expensive. A reasonable trade-off between filtering performances and cost is to use the hybrid active filter. Design consideration are presented and experimental results are provided to validate effectiveness of the proposed method. Furthermore, this paper discusses filtering performances on line impedance...

  6. Finite-Time H∞ Filtering for Linear Continuous Time-Varying Systems with Uncertain Observations

    Directory of Open Access Journals (Sweden)

    Huihong Zhao

    2012-01-01

    Full Text Available This paper is concerned with the finite-time H∞ filtering problem for linear continuous time-varying systems with uncertain observations and ℒ2-norm bounded noise. The design of finite-time H∞ filter is equivalent to the problem that a certain indefinite quadratic form has a minimum and the filter is such that the minimum is positive. The quadratic form is related to a Krein state-space model according to the Krein space linear estimation theory. By using the projection theory in Krein space, the finite-time H∞ filtering problem is solved. A numerical example is given to illustrate the performance of the H∞ filter.

  7. Towards collaborative filtering recommender systems for tailored health communications.

    Science.gov (United States)

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

    2013-01-01

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

  8. Performance assessment of containment filtered venting system with Venturi scrubber

    International Nuclear Information System (INIS)

    Adinarayna, K.N.V.; Ali, Seik Mansoor; Balasubramaniyan, V.

    2015-01-01

    Venting through appropriate filtration systems is now being considered as a severe accident management strategy for maintaining the containment integrity and also as a means to reduce the radiological consequences to the public and environment. The option of filtered containment venting appears to have assumed significance in the post- Fukushima accident backdrop. Back-fitting of a suitable Venturi scrubber based CFVS for the Indian BWRs (TAPS- 1 and 2) at Tarapur is now being contemplated. Several key issues need to be carefully addressed for ensuring the desired functional capability of such a system. At the outset, this paper highlights a few thermal hydraulic issues that are of interest from regulatory perspective. This is followed by a detailed description of the mathematical models developed for assessing the depressurization characteristics of CFVS, energy absorption capacity of the Scrubber Tank (ST) water inventory, iodine removal and aerosol retention capability etc. Finally, application of these models to investigate the response of CFVS under twin unit SBO conditions in TAPS-1 and 2 is presented. The studies presented here give insight into the key variables affecting the CFVS performance and would be useful to both the system designer as well as the regulator. (author)

  9. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

    Science.gov (United States)

    Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

    2018-01-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

  10. Improvement of Detection of Hypoattenuation in Acute Ischemic Stroke in Unenhanced Computed Tomography Using an Adaptive Smoothing Filter

    International Nuclear Information System (INIS)

    Takahashi, N.; Lee, Y.; Tsai, D. Y.; Ishii, K.; Kinoshita, T.; Tamura, H.; K imura, M.

    2008-01-01

    Background: Much attention has been directed toward identifying early signs of cerebral ischemia on computed tomography (CT) images. Hypoattenuation of ischemic brain parenchyma has been found to be the most frequent early sign. Purpose: To evaluate the effect of a previously proposed adaptive smoothing filter for improving detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images. Material and Methods: Twenty-six patients with parenchymal hypoattenuation and 49 control subjects without hypoattenuation were retrospectively selected in this study. The adaptive partial median filter (APMF) designed for improving detectability of hypoattenuation areas on unenhanced CT images was applied. Seven radiologists, including four certified radiologists and three radiology residents, indicated their confidence level regarding the presence (or absence) of hypoattenuation on CT images, first without and then with the APMF processed images. Their performances without and with the APMF processed images were evaluated by receiver operating characteristic (ROC) analysis. Results: The mean areas under the ROC curves (AUC) for all observers increased from 0.875 to 0.929 (P=0.002) when the radiologists observed with the APMF processed images. The mean sensitivity in the detection of hypoattenuation significantly improved, from 69% (126 of 182 observations) to 89% (151 of 182 observations), when employing the APMF (P=0.012). The specificity, however, was unaffected by the APMF (P=0.41). Conclusion: The APMF has the potential to improve the detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images

  11. A FPGA-based Fast Converging Digital Adaptive Filter for Real-time RFI Mitigation on Ground Based Radio Telescopes

    Science.gov (United States)

    Finger, R.; Curotto, F.; Fuentes, R.; Duan, R.; Bronfman, L.; Li, D.

    2018-02-01

    Radio Frequency Interference (RFI) is a growing concern in the radio astronomy community. Single-dish telescopes are particularly susceptible to RFI. Several methods have been developed to cope with RF-polluted environments, based on flagging, excision, and real-time blanking, among others. All these methods produce some degree of data loss or require assumptions to be made on the astronomical signal. We report the development of a real-time, digital adaptive filter implemented on a Field Programmable Gate Array (FPGA) capable of processing 4096 spectral channels in a 1 GHz of instantaneous bandwidth. The filter is able to cancel a broad range of interference signals and quickly adapt to changes on the RFI source, minimizing the data loss without any assumption on the astronomical or interfering signal properties. The speed of convergence (for a decrease to a 1%) was measured to be 208.1 μs for a broadband noise-like RFI signal and 125.5 μs for a multiple-carrier RFI signal recorded at the FAST radio telescope.

  12. Feedforward compensation control of rotor imbalance for high-speed magnetically suspended centrifugal compressors using a novel adaptive notch filter

    Science.gov (United States)

    Zheng, Shiqiang; Feng, Rui

    2016-03-01

    This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.

  13. Intelligent Adaptation Process for Case Based Systems

    International Nuclear Information System (INIS)

    Nassar, A.M.; Mohamed, A.H.; Mohamed, A.H.

    2014-01-01

    Case Based Reasoning (CBR) Systems is one of the important decision making systems applied in many fields all over the world. The effectiveness of any CBR system based on the quality of the storage cases in the case library. Similar cases can be retrieved and adapted to produce the solution for the new problem. One of the main issues faced the CBR systems is the difficulties of achieving the useful cases. The proposed system introduces a new approach that uses the genetic algorithm (GA) technique to automate constructing the cases into the case library. Also, it can optimize the best one to be stored in the library for the future uses. However, the proposed system can avoid the problems of the uncertain and noisy cases. Besides, it can simply the retrieving and adaptation processes. So, it can improve the performance of the CBR system. The suggested system can be applied for many real-time problems. It has been applied for diagnosis the faults of the wireless network, diagnosis of the cancer diseases, diagnosis of the debugging of a software as cases of study. The proposed system has proved its performance in this field

  14. 40 CFR 141.551 - What strengthened combined filter effluent turbidity limits must my system meet?

    Science.gov (United States)

    2010-07-01

    ... effluent turbidity limits must my system meet? 141.551 Section 141.551 Protection of Environment... Effluent Requirements § 141.551 What strengthened combined filter effluent turbidity limits must my system meet? Your system must meet two strengthened combined filter effluent turbidity limits. (a) The first...

  15. Potentials and limitations of adaptive plasticity in filtering screen morphology of Daphnia (Crustacea: Cladocera)

    Czech Academy of Sciences Publication Activity Database

    Macháček, Jiří; Seďa, Jaromír

    2016-01-01

    Roč. 38, č. 5 (2016), s. 1269-1280 ISSN 0142-7873 R&D Projects: GA ČR(CZ) GA206/09/1325; GA ČR(CZ) GA15-24309S Institutional support: RVO:60077344 Keywords : Daphnia * filtering screen morphology * transgenerational plasticity * ontogenetic plasticity * taxonspecific trait Subject RIV: EH - Ecology, Behaviour Impact factor: 1.983, year: 2016

  16. Adaptive Mean and Trend Removal of Heart Rate Variability Using Kalman Filtering

    National Research Council Canada - National Science Library

    Schloegl, A

    2001-01-01

    Analysis of heart rate van ability requires the calculation of the mean heart rate, Adaptive methods are important for online and real-time parameter estimation, In this paper we demonstrate the use...

  17. Semantic models for adaptive interactive systems

    CERN Document Server

    Hussein, Tim; Lukosch, Stephan; Ziegler, Jürgen; Calvary, Gaëlle

    2013-01-01

    Providing insights into methodologies for designing adaptive systems based on semantic data, and introducing semantic models that can be used for building interactive systems, this book showcases many of the applications made possible by the use of semantic models.Ontologies may enhance the functional coverage of an interactive system as well as its visualization and interaction capabilities in various ways. Semantic models can also contribute to bridging gaps; for example, between user models, context-aware interfaces, and model-driven UI generation. There is considerable potential for using

  18. Modeling Adaptive Behavior for Systems Design

    DEFF Research Database (Denmark)

    Rasmussen, Jens

    1994-01-01

    Field studies in modern work systems and analysis of recent major accidents have pointed to a need for better models of the adaptive behavior of individuals and organizations operating in a dynamic and highly competitive environment. The paper presents a discussion of some key characteristics.......) The basic difference between the models of system functions used in engineering and design and those evolving from basic research within the various academic disciplines and finally 3.) The models and methods required for closed-loop, feedback system design....

  19. Proposing Wavelet-Based Low-Pass Filter and Input Filter to Improve Transient Response of Grid-Connected Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Bijan Rahmani

    2016-08-01

    Full Text Available Available photovoltaic (PV systems show a prolonged transient response, when integrated into the power grid via active filters. On one hand, the conventional low-pass filter, employed within the integrated PV system, works with a large delay, particularly in the presence of system’s low-order harmonics. On the other hand, the switching of the DC (direct current–DC converters within PV units also prolongs the transient response of an integrated system, injecting harmonics and distortion through the PV-end current. This paper initially develops a wavelet-based low-pass filter to improve the transient response of the interconnected PV systems to grid lines. Further, a damped input filter is proposed within the PV system to address the raised converter’s switching issue. Finally, Matlab/Simulink simulations validate the effectiveness of the proposed wavelet-based low-pass filter and damped input filter within an integrated PV system.

  20. An improved fuzzy Kalman filter for state estimation of nonlinear systems

    International Nuclear Information System (INIS)

    Zhou, Z-J; Hu, C-H; Chen, L; Zhang, B-C

    2008-01-01

    The extended fuzzy Kalman filter (EFKF) is developed recently and used for state estimation of the nonlinear systems with uncertainty. Based on extension of the orthogonality principle and the extended fuzzy Kalman filter, an improved fuzzy Kalman filters (IFKF) is proposed in this paper, which is more applicable and can deal with the state estimation of the nonlinear systems better than the EFKF. A simulation study is provided to verify the efficiency of the proposed method