A filtered backprojection algorithm with characteristics of the iterative landweber algorithm
L. Zeng, Gengsheng
2012-01-01
Purpose: In order to eventually develop an analytical algorithm with noise characteristics of an iterative algorithm, this technical note develops a window function for the filtered backprojection (FBP) algorithm in tomography that behaves as an iterative Landweber algorithm.
Filtered back-projection algorithm for Compton telescopes
Gunter, Donald L.
2008-03-18
A method for the conversion of Compton camera data into a 2D image of the incident-radiation flux on the celestial sphere includes detecting coincident gamma radiation flux arriving from various directions of a 2-sphere. These events are mapped by back-projection onto the 2-sphere to produce a convolution integral that is subsequently stereographically projected onto a 2-plane to produce a second convolution integral which is deconvolved by the Fourier method to produce an image that is then projected onto the 2-sphere.
Luo, Shouhua; Wu, Huazhen; Sun, Yi; Li, Jing; Li, Guang; Gu, Ning
2017-03-01
The beam hardening effect can induce strong artifacts in CT images, which result in severely deteriorated image quality with incorrect intensities (CT numbers). This paper develops an effective and efficient beam hardening correction algorithm incorporated in a filtered back-projection based maximum a posteriori (BHC-FMAP). In the proposed algorithm, the beam hardening effect is modeled and incorporated into the forward-projection of the MAP to suppress beam hardening induced artifacts, and the image update process is performed by Feldkamp–Davis–Kress method based back-projection to speed up the convergence. The proposed BHC-FMAP approach does not require information about the beam spectrum or the material properties, or any additional segmentation operation. The proposed method was qualitatively and quantitatively evaluated using both phantom and animal projection data. The experimental results demonstrate that the BHC-FMAP method can efficiently provide a good correction of beam hardening induced artefacts.
Energy Technology Data Exchange (ETDEWEB)
Chatziioannou, A.; Qi, J.; Moore, A.; Annala, A.; Nguyen, K.; Leahy, R.M.; Cherry, S.R.
2000-01-01
We have evaluated the performance of two three dimensional reconstruction algorithms with data acquired from microPET, a high resolution tomograph dedicated to small animal imaging. The first was a linear filtered-backprojection algorithm (FBP) with reprojection of the missing data and the second was a statistical maximum-aposteriori probability algorithm (MAP). The two algorithms were evaluated in terms of their resolution performance, both in phantoms and in vivo. Sixty independent realizations of a phantom simulating the brain of a baby monkey were acquired, each containing 3 million counts. Each of these realizations was reconstructed independently with both algorithms. The ensemble of the sixty reconstructed realizations was used to estimate the standard deviation as a measure of the noise for each reconstruction algorithm. More detail was recovered in the MAP reconstruction without an increase in noise relative to FBP. Studies in a simple cylindrical compartment phantom demonstrated improved recovery of known activity ratios with MAP. Finally in vivo studies also demonstrated a clear improvement in spatial resolution using the MAP algorithm. The quantitative accuracy of the MAP reconstruction was also evaluated by comparison with autoradiography and direct well counting of tissue samples and was shown to be superior.
Composite cone-beam filtered backprojection algorithm based on nutating line
Institute of Scientific and Technical Information of China (English)
WANG Yu; OU Zong-ying; SU Tie-ming; WANG Feng
2006-01-01
The FDK algorithm is the most popular cone beam algorithm in the medical and industrial imaging field.Due to data insufficiency acquired from a circular trajectory,the images reconstructed by the FDK algorithm suffer from the intensity droping with increasing cone angle.To overcome the drawback,a modified FDK algorithm is presented by convert the 1D ramp filtering direction from along the horizontal lines to along the nutating lines based on the result of Turbell.Unlike Turbell's method,there is no need for our algorithm to rebin the cone-beam data into 3D parallel-beam data before reconstructing.Moreover pre-weighting of the projection data is corrected by compensating for the cone angle effect.In addition,another correction term derived from the result of Hu is also induced into our algorithm.The simulation experiments demonstrate that the final algorithm can suppress the intensity drop associated with the FDK algorithm.
Tang, Xiangyang; Hsieh, Jiang; Hagiwara, Akira; Nilsen, Roy A.; Thibault, Jean-Baptiste; Drapkin, Evgeny
2005-08-01
The original FDK algorithm proposed for cone beam (CB) image reconstruction under a circular source trajectory has been extensively employed in medical and industrial imaging applications. With increasing cone angle, CB artefacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few 'circular plus' trajectories have been proposed in the past to help the original FDK algorithm to reduce CB artefacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as head imaging, breast imaging, cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artefacts existing in the original FDK algorithm is the inconsistency between conjugate rays that are 180° apart in view angle (namely conjugate ray inconsistency). The conjugate ray inconsistency is pixel dependent, varying dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artefacts that can be avoided if appropriate weighting strategies are exercised. Along with an experimental evaluation and verification, a three-dimensional (3D) weighted axial cone beam filtered backprojection (CB-FBP) algorithm is proposed in this paper for image reconstruction in volumetric CT under a circular source trajectory. Without extra trajectories supplemental to the circular trajectory, the proposed algorithm applies 3D weighting on projection data before 3D backprojection to reduce conjugate ray inconsistency by suppressing the contribution from one of the conjugate rays with a larger cone angle. Furthermore, the 3D weighting is dependent on the distance between the reconstruction plane and the central plane determined by the circular trajectory. The proposed 3D weighted axial CB-FBP algorithm
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In this article we introduce an exact backprojecfion filtered (BPF) type reconstruction algorithm for cone-beam scans based on Zou and Pan's work. The algorithm can reconstruct images using only the projection data passing through the parallel PI-line segments in reduced scans. Computer simulations and practical experiments are carried out to evaluate this algorithm. The BPF algorithm has a higher computational efficiency than the famous FDK algorithm. The BPF algorithm is evaluated using the practical CT projection data on a 450 keV X-ray CT system with a flat-panel detector (FPD). From the practical experiments, we get the spatial resolution of this CT system. The algorithm could achieve the spatial resolution of 2.4 lp/mm and satisfies the practical applications in industrial CT inspection.
The statistical efficiency of filtered backprojection in emission tomography
Energy Technology Data Exchange (ETDEWEB)
Kuruc, A.
1995-09-25
While there has been much interest in developing tomographic reconstruction algorithms that are more statistically efficient than filtered backprojection (FB), the degree of improvement possible has not been well understood. We present an algorithm-independent theory of statistical accuracy attainable in emission tomography that provides a geometrical interpretation of the statistical efficiency of FB. Our analysis shows that, in general, one can build unbiased estimators with smaller variance than FB. The improvement in performance is obtained by exploiting the range properties of the Radon transform.
Filtered backprojection proton CT reconstruction along most likely paths
Energy Technology Data Exchange (ETDEWEB)
Rit, Simon; Dedes, George; Freud, Nicolas; Sarrut, David; Letang, Jean Michel [Universite de Lyon, CREATIS, CNRS UMR5220, Inserm U1044, INSA-Lyon, Universite Lyon 1, Centre Leon Berard, 69008 Lyon (France)
2013-03-15
Purpose: Proton CT (pCT) has the potential to accurately measure the electron density map of tissues at low doses but the spatial resolution is prohibitive if the curved paths of protons in matter is not accounted for. The authors propose to account for an estimate of the most likely path of protons in a filtered backprojection (FBP) reconstruction algorithm. Methods: The energy loss of protons is first binned in several proton radiographs at different distances to the proton source to exploit the depth-dependency of the estimate of the most likely path. This process is named the distance-driven binning. A voxel-specific backprojection is then used to select the adequate radiograph in the distance-driven binning in order to propagate in the pCT image the best achievable spatial resolution in proton radiographs. The improvement in spatial resolution is demonstrated using Monte Carlo simulations of resolution phantoms. Results: The spatial resolution in the distance-driven binning depended on the distance of the objects from the source and was optimal in the binned radiograph corresponding to that distance. The spatial resolution in the reconstructed pCT images decreased with the depth in the scanned object but it was always better than previous FBP algorithms assuming straight line paths. In a water cylinder with 20 cm diameter, the observed range of spatial resolutions was 0.7 - 1.6 mm compared to 1.0 - 2.4 mm at best with a straight line path assumption. The improvement was strongly enhanced in shorter 200 Degree-Sign scans. Conclusions: Improved spatial resolution was obtained in pCT images with filtered backprojection reconstruction using most likely path estimates of protons. The improvement in spatial resolution combined with the practicality of FBP algorithms compared to iterative reconstruction algorithms makes this new algorithm a candidate of choice for clinical pCT.
Roerdink, Jos B.T.M.; Westenberg, Michel A.
1998-01-01
We consider the parallelization of two standard 2D reconstruction algorithms, filtered backprojection and direct Fourier reconstruction, using the data-parallel programming style. The algorithms are implemented on a Connection Machine CM-5 with 16 processors and a peak performance of 2 Gflop/s. (C)
Wang, Zhengzi; Ren, Zhong; Liu, Guodong
2016-10-01
In this paper, the wavelet threshold denoising method was used into the filtered back-projection algorithm of imaging reconstruction. To overcome the drawbacks of the traditional soft- and hard-threshold functions, a modified wavelet threshold function was proposed. The modified wavelet threshold function has two threshold values and two variants. To verify the feasibility of the modified wavelet threshold function, the standard test experiments were performed by using the software platform of MATLAB. Experimental results show that the filtered back-projection reconstruction algorithm based on the modified wavelet threshold function has better reconstruction effect because of more flexible advantage.
An adaptive filtered back-projection for photoacoustic image reconstruction
Energy Technology Data Exchange (ETDEWEB)
Huang, He; Bustamante, Gilbert; Peterson, Ralph; Ye, Jing Yong, E-mail: jingyong.ye@utsa.edu [Department of Biomedical Engineering, University of Texas at San Antonio, San Antonio, Texas 78249 (United States)
2015-05-15
Purpose: The purpose of this study is to develop an improved filtered-back-projection (FBP) algorithm for photoacoustic tomography (PAT), which allows image reconstruction with higher quality compared to images reconstructed through traditional algorithms. Methods: A rigorous expression of a weighting function has been derived directly from a photoacoustic wave equation and used as a ramp filter in Fourier domain. The authors’ new algorithm utilizes this weighting function to precisely calculate each photoacoustic signal’s contribution and then reconstructs the image based on the retarded potential generated from the photoacoustic sources. In addition, an adaptive criterion has been derived for selecting the cutoff frequency of a low pass filter. Two computational phantoms were created to test the algorithm. The first phantom contained five spheres with each sphere having different absorbances. The phantom was used to test the capability for correctly representing both the geometry and the relative absorbed energy in a planar measurement system. The authors also used another phantom containing absorbers of different sizes with overlapping geometry to evaluate the performance of the new method for complicated geometry. In addition, random noise background was added to the simulated data, which were obtained by using an arc-shaped array of 50 evenly distributed transducers that spanned 160° over a circle with a radius of 65 mm. A normalized factor between the neighbored transducers was applied for correcting measurement signals in PAT simulations. The authors assumed that the scanned object was mounted on a holder that rotated over the full 360° and the scans were set to a sampling rate of 20.48 MHz. Results: The authors have obtained reconstructed images of the computerized phantoms by utilizing the new FBP algorithm. From the reconstructed image of the first phantom, one can see that this new approach allows not only obtaining a sharp image but also showing
Liu, Zhi-chao; Yang, Jin-hua
2014-07-01
In order to obtain clear two-dimensional image under the conditions without using heterodyne interferometry by inverse synthetic aperture lidar(ISAL), designed imaging algorithms based on filtered back projection tomography technique, and the target "A" was reconstructed with simulation algorithm by the system in the turntable model. Analyzed the working process of ISAL, and the function of the reconstructed image was given. Detail analysis of the physical meaning of the various parameters in the process of echo data, and its parameters affect the reconstructed image. The image in test area was reconstructed by the one-dimensional distance information with filtered back projection tomography technique. When the measured target rotated, the sum of the echo light intensity at the same distance was constituted by the different position of the measured target. When the total amount collected is large enough, multiple equations can be solved change. Filtered back-projection image of the ideal image is obtained through MATLAB simulation, and analyzed that the angle intervals affected the reconstruction of image. The ratio of the intensity of echo light and loss light affected the reconstruction of image was analyzed. Simulation results show that, when the sampling angle is smaller, the resolution of the reconstructed image of measured target is higher. And the ratio of the intensity of echo light and loss light is greater, the resolution of the reconstructed image of measured target is higher. In conclusion after some data processing, the reconstructed image basically meets be effective identification requirements.
Bin, Yan; Yu, Han; Feng, Zhang; Chao, Wang Xian; Lei, Li
2013-01-01
High radiation dose in computed tomography (CT) scans increases the lifetime risk of cancer, which become a major clinical concern. The backprojection-filtration (BPF) algorithm could reduce radiation dose by reconstructing images from truncated data in a short scan. In dental CT, it could reduce radiation dose for the teeth by using the projection acquired in a short scan, and could avoid irradiation to other part by using truncated projection. However, the limit of integration for backprojection varies per PI-line, resulting in low calculation efficiency and poor parallel performance. Recently, a tent BPF (T-BPF) has been proposed to improve calculation efficiency by rearranging projection. However, the memory-consuming data rebinning process is included. Accordingly, the chose-BPF (C-BPF) algorithm is proposed in this paper. In this algorithm, the derivative of projection is backprojected to the points whose x coordinate is less than that of the source focal spot to obtain the differentiated backprojection...
Regularized Iterative Weighted Filtered Back-Projection for Few-View Data Photoacoustic Imaging
Peng, Dong
2016-01-01
Photoacoustic imaging is an emerging noninvasive imaging technique with great potential for a wide range of biomedical imaging applications. However, with few-view data the filtered back-projection method will create streak artifacts. In this study, the regularized iterative weighted filtered back-projection method was applied to our photoacoustic imaging of the optical absorption in phantom from few-view data. This method is based on iterative application of a nonexact 2DFBP. By adding a regularization operation in the iterative loop, the streak artifacts have been reduced to a great extent and the convergence properties of the iterative scheme have been improved. Results of numerical simulations demonstrated that the proposed method was superior to the iterative FBP method in terms of both accuracy and robustness to noise. The quantitative image evaluation studies have shown that the proposed method outperforms conventional iterative methods. PMID:27594896
Filtered back-projection reconstruction for attenuation proton CT along most likely paths.
Quiñones, C T; Létang, J M; Rit, S
2016-05-07
This work investigates the attenuation of a proton beam to reconstruct the map of the linear attenuation coefficient of a material which is mainly caused by the inelastic interactions of protons with matter. Attenuation proton computed tomography (pCT) suffers from a poor spatial resolution due to multiple Coulomb scattering (MCS) of protons in matter, similarly to the conventional energy-loss pCT. We therefore adapted a recent filtered back-projection algorithm along the most likely path (MLP) of protons for energy-loss pCT (Rit et al 2013) to attenuation pCT assuming a pCT scanner that can track the position and the direction of protons before and after the scanned object. Monte Carlo simulations of pCT acquisitions of density and spatial resolution phantoms were performed to characterize the new algorithm using Geant4 (via Gate). Attenuation pCT assumes an energy-independent inelastic cross-section, and the impact of the energy dependence of the inelastic cross-section below 100 MeV showed a capping artifact when the residual energy was below 100 MeV behind the object. The statistical limitation has been determined analytically and it was found that the noise in attenuation pCT images is 411 times and 278 times higher than the noise in energy-loss pCT images for the same imaging dose at 200 MeV and 300 MeV, respectively. Comparison of the spatial resolution of attenuation pCT images with a conventional straight-line path binning showed that incorporating the MLP estimates during reconstruction improves the spatial resolution of attenuation pCT. Moreover, regardless of the significant noise in attenuation pCT images, the spatial resolution of attenuation pCT was better than that of conventional energy-loss pCT in some studied situations thanks to the interplay of MCS and attenuation known as the West-Sherwood effect.
Fieselmann, Andreas; Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan; Fahrig, Rebecca; Hornegger, Joachim
2011-06-01
Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.
Energy Technology Data Exchange (ETDEWEB)
Fieselmann, Andreas; Hornegger, Joachim [Department of Computer Science, Pattern Recognition Lab, Friedrich-Alexander University of Erlangen-Nuremberg, Martensstr. 3, 91058 Erlangen (Germany); Dennerlein, Frank; Deuerling-Zheng, Yu; Boese, Jan [Siemens AG, Healthcare Sector, Angiography and Interventional X-Ray Systems, Siemensstr. 1, 91301 Forchheim (Germany); Fahrig, Rebecca, E-mail: andreas.fieselmann@informatik.uni-erlangen.de [Department of Radiology, Lucas MRS Center, Stanford University, 1201 Welch Road, Palo Alto, CA 94305 (United States)
2011-06-21
Filtered backprojection is the basis for many CT reconstruction tasks. It assumes constant attenuation values of the object during the acquisition of the projection data. Reconstruction artifacts can arise if this assumption is violated. For example, contrast flow in perfusion imaging with C-arm CT systems, which have acquisition times of several seconds per C-arm rotation, can cause this violation. In this paper, we derived and validated a novel spatio-temporal model to describe these kinds of artifacts. The model separates the temporal dynamics due to contrast flow from the scan and reconstruction parameters. We introduced derivative-weighted point spread functions to describe the spatial spread of the artifacts. The model allows prediction of reconstruction artifacts for given temporal dynamics of the attenuation values. Furthermore, it can be used to systematically investigate the influence of different reconstruction parameters on the artifacts. We have shown that with optimized redundancy weighting function parameters the spatial spread of the artifacts around a typical arterial vessel can be reduced by about 70%. Finally, an inversion of our model could be used as the basis for novel dynamic reconstruction algorithms that further minimize these artifacts.
Randomized Filtering Algorithms
DEFF Research Database (Denmark)
Katriel, Irit; Van Hentenryck, Pascal
2008-01-01
of AllDifferent and is generalization, the Global Cardinality Constraint. The first delayed filtering scheme is a Monte Carlo algorithm: its running time is superior, in the worst case, to that of enforcing are consistency after every domain event, while its filtering effectiveness is analyzed...... in the expected sense. The second scheme is a Las Vegas algorithm using filtering triggers: Its effectiveness is the same as enforcing are consistency after every domain event, while in the expected case it is faster by a factor of m/n, where n and m are, respectively, the number of nodes and edges...
Image-domain sampling properties of the Hotelling Observer in CT using filtered back-projection
Sanchez, Adrian A.; Sidky, Emil Y.; Pan, Xiaochuan
2015-03-01
The Hotelling Observer (HO),1 along with its channelized variants,2 has been proposed for image quality evaluation in x-ray CT.3,4 In this work, we investigate HO performance for a detection task in parallel-beam FBP as a function of two image-domain sampling parameters, namely pixel size and field-of-view. These two parameters are of central importance in adapting HO methods to use in CT, since the large number of pixels in a single image makes direct computation of HO performance for a full image infeasible in most cases. Reduction of the number of image pixels and/or restriction of the image to a region-of-interest (ROI) has the potential to make direct computation of HO statistics feasible in CT, provided that the signal and noise properties lead to redundant information in some regions of the image. For small signals, we hypothesize that reduction of image pixel size and enlargement of the image field-of-view are approximately equivalent means of gaining additional information relevant to a detection task. The rationale for this hypothesis is that the backprojection operation in FBP introduces long range correlations so that, for small signals, the reconstructed signal outside of a small ROI is not linearly independent of the signal within the ROI. In this work, we perform a preliminary investigation of this hypothesis by sweeping these two sampling parameters and computing HO performance for a signal detection task.
Filter selection using genetic algorithms
Patel, Devesh
1996-03-01
Convolution operators act as matched filters for certain types of variations found in images and have been extensively used in the analysis of images. However, filtering through a bank of N filters generates N filtered images, consequently increasing the amount of data considerably. Moreover, not all these filters have the same discriminatory capabilities for the individual images, thus making the task of any classifier difficult. In this paper, we use genetic algorithms to select a subset of relevant filters. Genetic algorithms represent a class of adaptive search techniques where the processes are similar to natural selection of biological evolution. The steady state model (GENITOR) has been used in this paper. The reduction of filters improves the performance of the classifier (which in this paper is the multi-layer perceptron neural network) and furthermore reduces the computational requirement. In this study we use the Laws filters which were proposed for the analysis of texture images. Our aim is to recognize the different textures on the images using the reduced filter set.
Filtering algorithms using shiftable kernels
Chaudhury, Kunal Narayan
2011-01-01
It was recently demonstrated in [4][arxiv:1105.4204] that the non-linear bilateral filter \\cite{Tomasi} can be efficiently implemented using an O(1) or constant-time algorithm. At the heart of this algorithm was the idea of approximating the Gaussian range kernel of the bilateral filter using trigonometric functions. In this letter, we explain how the idea in [4] can be extended to few other linear and non-linear filters [18,21,2]. While some of these filters have received a lot of attention in recent years, they are known to be computationally intensive. To extend the idea in \\cite{Chaudhury2011}, we identify a central property of trigonometric functions, called shiftability, that allows us to exploit the redundancy inherent in the filtering operations. In particular, using shiftable kernels, we show how certain complex filtering can be reduced to simply that of computing the moving sum of a stack of images. Each image in the stack is obtained through an elementary pointwise transform of the input image. Thi...
Filtering algorithm for dotted interferences
Energy Technology Data Exchange (ETDEWEB)
Osterloh, K., E-mail: kurt.osterloh@bam.de [Federal Institute for Materials Research and Testing (BAM), Division VIII.3, Radiological Methods, Unter den Eichen 87, 12205 Berlin (Germany); Buecherl, T.; Lierse von Gostomski, Ch. [Technische Universitaet Muenchen, Lehrstuhl fuer Radiochemie, Walther-Meissner-Str. 3, 85748 Garching (Germany); Zscherpel, U.; Ewert, U. [Federal Institute for Materials Research and Testing (BAM), Division VIII.3, Radiological Methods, Unter den Eichen 87, 12205 Berlin (Germany); Bock, S. [Technische Universitaet Muenchen, Lehrstuhl fuer Radiochemie, Walther-Meissner-Str. 3, 85748 Garching (Germany)
2011-09-21
An algorithm has been developed to remove reliably dotted interferences impairing the perceptibility of objects within a radiographic image. This particularly is a major challenge encountered with neutron radiographs collected at the NECTAR facility, Forschungs-Neutronenquelle Heinz Maier-Leibnitz (FRM II): the resulting images are dominated by features resembling a snow flurry. These artefacts are caused by scattered neutrons, gamma radiation, cosmic radiation, etc. all hitting the detector CCD directly in spite of a sophisticated shielding. This makes such images rather useless for further direct evaluations. One approach to resolve this problem of these random effects would be to collect a vast number of single images, to combine them appropriately and to process them with common image filtering procedures. However, it has been shown that, e.g. median filtering, depending on the kernel size in the plane and/or the number of single shots to be combined, is either insufficient or tends to blur sharp lined structures. This inevitably makes a visually controlled processing image by image unavoidable. Particularly in tomographic studies, it would be by far too tedious to treat each single projection by this way. Alternatively, it would be not only more comfortable but also in many cases the only reasonable approach to filter a stack of images in a batch procedure to get rid of the disturbing interferences. The algorithm presented here meets all these requirements. It reliably frees the images from the snowy pattern described above without the loss of fine structures and without a general blurring of the image. It consists of an iterative, within a batch procedure parameter free filtering algorithm aiming to eliminate the often complex interfering artefacts while leaving the original information untouched as far as possible.
Torres-Xirau, I.; Olaciregui-Ruiz, I.; Rozendaal, R. A.; González, P.; Mijnheer, B. J.; Sonke, J.-J.; van der Heide, U. A.; Mans, A.
2017-08-01
In external beam radiotherapy, electronic portal imaging devices (EPIDs) are frequently used for pre-treatment and for in vivo dose verification. Currently, various MR-guided radiotherapy systems are being developed and clinically implemented. Independent dosimetric verification is highly desirable. For this purpose we adapted our EPID-based dose verification system for use with the MR-Linac combination developed by Elekta in cooperation with UMC Utrecht and Philips. In this study we extended our back-projection method to cope with the presence of an extra attenuating medium between the patient and the EPID. Experiments were performed at a conventional linac, using an aluminum mock-up of the MRI scanner housing between the phantom and the EPID. For a 10 cm square field, the attenuation by the mock-up was 72%, while 16% of the remaining EPID signal resulted from scattered radiation. 58 IMRT fields were delivered to a 20 cm slab phantom with and without the mock-up. EPID reconstructed dose distributions were compared to planned dose distributions using the γ -evaluation method (global, 3%, 3 mm). In our adapted back-projection algorithm the averaged {γmean} was 0.27+/- 0.06 , while in the conventional it was 0.28+/- 0.06 . Dose profiles of several square fields reconstructed with our adapted algorithm showed excellent agreement when compared to TPS.
Olaciregui-Ruiz, Igor; Rozendaal, Roel; van Oers, René F M; Mijnheer, Ben; Mans, Anton
2017-05-01
At our institute, a transit back-projection algorithm is used clinically to reconstruct in vivo patient and in phantom 3D dose distributions using EPID measurements behind a patient or a polystyrene slab phantom, respectively. In this study, an extension to this algorithm is presented whereby in air EPID measurements are used in combination with CT data to reconstruct 'virtual' 3D dose distributions. By combining virtual and in vivo patient verification data for the same treatment, patient-related errors can be separated from machine, planning and model errors. The virtual back-projection algorithm is described and verified against the transit algorithm with measurements made behind a slab phantom, against dose measurements made with an ionization chamber and with the OCTAVIUS 4D system, as well as against TPS patient data. Virtual and in vivo patient dose verification results are also compared. Virtual dose reconstructions agree within 1% with ionization chamber measurements. The average γ-pass rate values (3% global dose/3mm) in the 3D dose comparison with the OCTAVIUS 4D system and the TPS patient data are 98.5±1.9%(1SD) and 97.1±2.9%(1SD), respectively. For virtual patient dose reconstructions, the differences with the TPS in median dose to the PTV remain within 4%. Virtual patient dose reconstruction makes pre-treatment verification based on deviations of DVH parameters feasible and eliminates the need for phantom positioning and re-planning. Virtual patient dose reconstructions have additional value in the inspection of in vivo deviations, particularly in situations where CBCT data is not available (or not conclusive). Copyright © 2017 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
Adaptive Filtering Algorithms and Practical Implementation
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...
DSA cone beam reconstruction algorithm based on backprojection weight FDK%基于FDK反投影权重的锥束DSA重建算法
Institute of Scientific and Technical Information of China (English)
杨宏成; 高欣; 张涛
2013-01-01
To solve the problem of cone beam artifacts resulting from the large cone angle in cone beam digital subtraction angiography of DSA, a novel backprojection weight reconstruction algorithm based on the frame work of FDK(BPW-FDK) was proposed. The cause of the cone beam artifacts away from the rotating track was analyzed. To solve the data deficiency in Randon space, a new backprojection weight function based on distance was designed and incorporated into the original FDK algorithm as a constraint condition for data compensation in the region far away from the rotating track to expand the reconstruction region. The reconstructing experiments were conducted on the images from simulated projections with noise or without noise and the real projections from a self-development DSA scanner. The results show that the proposed algorithm has obvious superiority over the Parker-FDK algorithm in suppression of cone beam artifacts for large cone angle projections. Compared with the Parker-FDK, the normalized mean square distance criterion and the normalized mean absolute distance criterion of the proposed algorithm are decreased by 5%.%针对锥束数字减影血管造影成像系统(DSA)锥角增大而导致锥束伪影严重的问题,提出了一种基于FDK的反投影权重锥束DSA重建算法.分析了圆扫描轨迹远端伪影的成因,针对短扫描阴影区域导致的Radon空间数据缺失,提出了一种距离变量的反投影权重函数,并将其作为约束条件引入到FDK算法中,实现扫描轨迹远端区域的数据补偿,扩大图像重建区域.应用该算法对无噪声和有噪声的模拟投影数据,及自行研发的锥束DSA的实际扫描数据分别进行了重建试验.结果表明,文中算法较FDK类算法(Parker-FDK)对大锥角投影数据可明显抑制锥角伪影,其归一化均方距离判据和归一化平均绝对距离判据比Parker-FDK均降低了5％.
IMM Iterated Extended Particle Filter Algorithm
Yang Wan; Shouyong Wang; Xing Qin
2013-01-01
In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEH...
A new adaptive filtering algorithm for systems with multiplicative noise
Institute of Scientific and Technical Information of China (English)
WANG Hui-li; CHEN Xi-xin; LU Qian-hao
2005-01-01
Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm.
A New Filtering Algorithm Utilizing Radial Velocity Measurement
Institute of Scientific and Technical Information of China (English)
LIU Yan-feng; DU Zi-cheng; PAN Quan
2005-01-01
Pulse Doppler radar measurements consist of range, azimuth, elevation and radial velocity. Most of the radar tracking algorithms in engineering only utilize position measurement. The extended Kalman filter with radial velocity measureneut is presented, then a new filtering algorithm utilizing radial velocity measurement is proposed to improve tracking results and the theoretical analysis is also given. Simulation results of the new algorithm, converted measurement Kalman filter, extended Kalman filter are compared. The effectiveness of the new algorithm is verified by simulation results.
Explicit filtering of building blocks for genetic algorithms
Kemenade, C.H.M. van
1996-01-01
Genetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic algorithms re
Explicit filtering of building blocks for genetic algorithms
C.H.M. van Kemenade
1996-01-01
textabstractGenetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic
Interpolating and filtering decoding algorithm for convolution codes
Directory of Open Access Journals (Sweden)
O. O. Shpylka
2010-01-01
Full Text Available There has been synthesized interpolating and filtering decoding algorithm for convolution codes on maximum of a posteriori probability criterion, in which combined filtering coder state and interpolation of information signs on sliding interval are processed
Tang, Shaojie; Tang, Xiangyang
2016-03-01
Axial cone beam (CB) computed tomography (CT) reconstruction is still the most desirable in clinical applications. As the potential candidates with analytic form for the task, the back projection-filtration (BPF) and the derivative backprojection filtered (DBPF) algorithms, in which Hilbert filtering is the common algorithmic feature, are originally derived for exact helical and axial reconstruction from CB and fan beam projection data, respectively. These two algorithms have been heuristically extended for axial CB reconstruction via adoption of virtual PI-line segments. Unfortunately, however, streak artifacts are induced along the Hilbert filtering direction, since these algorithms are no longer accurate on the virtual PI-line segments. We have proposed to cascade the extended BPF/DBPF algorithm with orthogonal butterfly filtering for image reconstruction (namely axial CB-BPP/DBPF cascaded with orthogonal butterfly filtering), in which the orientation-specific artifacts caused by post-BP Hilbert transform can be eliminated, at a possible expense of losing the BPF/DBPF's capability of dealing with projection data truncation. Our preliminary results have shown that this is not the case in practice. Hence, in this work, we carry out an algorithmic analysis and experimental study to investigate the performance of the axial CB-BPP/DBPF cascaded with adequately oriented orthogonal butterfly filtering for three-dimensional (3D) reconstruction in region of interest (ROI).
Fast Backprojection Techniques for High Resolution Tomography
Koshev, Nikolay; Miqueles, Eduardo X
2016-01-01
Fast image reconstruction techniques are becoming important with the increasing number of scientific cases in high resolution micro and nano tomography. The processing of the large scale three-dimensional data demands new mathematical tools for the tomographic reconstruction task because of the big computational complexity of most current algorithms as the sizes of tomographic data grow with the development of more powerful acquisition hardware and more refined scientific needs. In the present paper we propose a new fast back-projection operator for the processing of tomographic data and compare it against other fast reconstruction techniques.
A Simple and Fast Spline Filtering Algorithm for Surface Metrology.
Zhang, Hao; Ott, Daniel; Song, John; Tong, Mingsi; Chu, Wei
2015-01-01
Spline filters and their corresponding robust filters are commonly used filters recommended in ISO (the International Organization for Standardization) standards for surface evaluation. Generally, these linear and non-linear spline filters, composed of symmetric, positive-definite matrices, are solved in an iterative fashion based on a Cholesky decomposition. They have been demonstrated to be relatively efficient, but complicated and inconvenient to implement. A new spline-filter algorithm is proposed by means of the discrete cosine transform or the discrete Fourier transform. The algorithm is conceptually simple and very convenient to implement.
Distortion Parameters Analysis Method Based on Improved Filtering Algorithm
Directory of Open Access Journals (Sweden)
ZHANG Shutuan
2013-10-01
Full Text Available In order to realize the accurate distortion parameters test of aircraft power supply system, and satisfy the requirement of corresponding equipment in the aircraft, the novel power parameters test system based on improved filtering algorithm is introduced in this paper. The hardware of the test system has the characters of s portable and high-speed data acquisition and processing, and the software parts utilize the software Labwindows/CVI as exploitation software, and adopt the pre-processing technique and adding filtering algorithm. Compare with the traditional filtering algorithm, the test system adopted improved filtering algorithm can help to increase the test accuracy. The application shows that the test system with improved filtering algorithm can realize the accurate test results, and reach to the design requirements.
Directory of Open Access Journals (Sweden)
Siddhartha Mukherjee
2014-04-01
Full Text Available This paper gives a detailed study on the performance of image filter algorithm with various parameters applied on an image of RGB model. There are various popular image filters, which consumes large amount of computing resources for processing. Oil paint image filter is one of the very interesting filters, which is very performance hungry. Current research tries to find improvement in oil paint image filter algorithm by using parallel pattern library. With increasing kernel-size, the processing time of oil paint image filter algorithm increases exponentially. I have also observed in various blogs and forums, the questions for faster oil paint have been asked repeatedly.
A backtracking algorithm that deals with particle filter degeneracy
Baarsma, Rein; Schmitz, Oliver; Karssenberg, Derek
2016-04-01
Particle filters are an excellent way to deal with stochastic models incorporating Bayesian data assimilation. While they are computationally demanding, the particle filter has no problem with nonlinearity and it accepts non-Gaussian observational data. In the geoscientific field it is this computational demand that creates a problem, since dynamic grid-based models are often already quite computationally demanding. As such it is of the utmost importance to keep the amount of samples in the filter as small as possible. Small sample populations often lead to filter degeneracy however, especially in models with high stochastic forcing. Filter degeneracy renders the sample population useless, as the population is no longer statistically informative. We have created an algorithm in an existing data assimilation framework that reacts to and deals with filter degeneracy based on Spiller et al. [2008]. During the Bayesian updating step of the standard particle filter, the algorithm tests the sample population for filter degeneracy. If filter degeneracy has occurred, the algorithm resets to the last time the filter did work correctly and recalculates the failed timespan of the filter with an increased sample population. The sample population is then reduced to its original size and the particle filter continues as normal. This algorithm was created in the PCRaster Python framework, an open source tool that enables spatio-temporal forward modelling in Python [Karssenberg et al., 2010] . The framework already contains several data assimilation algorithms, including a standard particle filter and a Kalman filter. The backtracking particle filter algorithm has been added to the framework, which will make it easy to implement in other research. The performance of the backtracking particle filter is tested against a standard particle filter using two models. The first is a simple nonlinear point model, and the second is a more complex geophysical model. The main testing
Resampling Algorithms for Particle Filters: A Computational Complexity Perspective
Directory of Open Access Journals (Sweden)
Miodrag Bolić
2004-11-01
Full Text Available Newly developed resampling algorithms for particle filters suitable for real-time implementation are described and their analysis is presented. The new algorithms reduce the complexity of both hardware and DSP realization through addressing common issues such as decreasing the number of operations and memory access. Moreover, the algorithms allow for use of higher sampling frequencies by overlapping in time the resampling step with the other particle filtering steps. Since resampling is not dependent on any particular application, the analysis is appropriate for all types of particle filters that use resampling. The performance of the algorithms is evaluated on particle filters applied to bearings-only tracking and joint detection and estimation in wireless communications. We have demonstrated that the proposed algorithms reduce the complexity without performance degradation.
Performance analysis of Non Linear Filtering Algorithms for underwater images
Padmavathi, Dr G; Kumar, Mr M Muthu; Thakur, Suresh Kumar
2009-01-01
Image filtering algorithms are applied on images to remove the different types of noise that are either present in the image during capturing or injected in to the image during transmission. Underwater images when captured usually have Gaussian noise, speckle noise and salt and pepper noise. In this work, five different image filtering algorithms are compared for the three different noise types. The performances of the filters are compared using the Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The modified spatial median filter gives desirable results in terms of the above two parameters for the three different noise. Forty underwater images are taken for study.
Energy Technology Data Exchange (ETDEWEB)
Becce, Fabio [University of Lausanne, Department of Diagnostic and Interventional Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Universite Catholique Louvain, Department of Radiology, Cliniques Universitaires Saint-Luc, Brussels (Belgium); Ben Salah, Yosr; Berg, Bruno C. vande; Lecouvet, Frederic E.; Omoumi, Patrick [Universite Catholique Louvain, Department of Radiology, Cliniques Universitaires Saint-Luc, Brussels (Belgium); Verdun, Francis R. [University of Lausanne, Institute of Radiation Physics, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland); Meuli, Reto [University of Lausanne, Department of Diagnostic and Interventional Radiology, Centre Hospitalier Universitaire Vaudois, Lausanne (Switzerland)
2013-07-15
To compare image quality of a standard-dose (SD) and a low-dose (LD) cervical spine CT protocol using filtered back-projection (FBP) and iterative reconstruction (IR). Forty patients investigated by cervical spine CT were prospectively randomised into two groups: SD (120 kVp, 275 mAs) and LD (120 kVp, 150 mAs), both applying automatic tube current modulation. Data were reconstructed using both FBP and sinogram-affirmed IR. Image noise, signal-to-noise (SNR) and contrast-to-noise (CNR) ratios were measured. Two radiologists independently and blindly assessed the following anatomical structures at C3-C4 and C6-C7 levels, using a four-point scale: intervertebral disc, content of neural foramina and dural sac, ligaments, soft tissues and vertebrae. They subsequently rated overall image quality using a ten-point scale. For both protocols and at each disc level, IR significantly decreased image noise and increased SNR and CNR, compared with FBP. SNR and CNR were statistically equivalent in LD-IR and SD-FBP protocols. Regardless of the dose and disc level, the qualitative scores with IR compared with FBP, and with LD-IR compared with SD-FBP, were significantly higher or not statistically different for intervertebral discs, neural foramina and ligaments, while significantly lower or not statistically different for soft tissues and vertebrae. The overall image quality scores were significantly higher with IR compared with FBP, and with LD-IR compared with SD-FBP. LD-IR cervical spine CT provides better image quality for intervertebral discs, neural foramina and ligaments, and worse image quality for soft tissues and vertebrae, compared with SD-FBP, while reducing radiation dose by approximately 40 %. (orig.)
An Adaptive Filtering Algorithm using Mean Field Annealing Techniques
Persson, Per; Nordebo, Sven; Claesson, Ingvar
2002-01-01
We present a new approach to discrete adaptive filtering based on the mean field annealing algorithm. The main idea is to find the discrete filter vector that minimizes the matrix form of the Wiener-Hopf equations in a least-squares sense by a generalized mean field annealing algorithm. It is indicated by simulations that this approach, with complexity O(M^2) where M is the filter length, finds a solution comparable to the one obtained by the recursive least squares (RLS) algorithm but withou...
New control algorithm for shunt active filters, based on self-tuned vector filter
Perales Esteve, Manuel Ángel; Mora Jiménez, José Luis; Carrasco Solís, Juan Manuel; García Franquelo, Leopoldo
2001-01-01
A new, improved, method for calculating the reference of a shunt active filter is presented. This method lays on a filter, which is able to extract the main component of a vector signal. This filter acts as a Phase-Locked Loop, capturing a particular frequency. The output of this filter is in phase with the frequency isolated, and has its amplitude. Simulation and experimental results confirms the validity of the proposed algorithm.
Research of Collaborative Filtering Recommendation Algorithm based on Network Structure
Directory of Open Access Journals (Sweden)
Hui PENG
2013-10-01
Full Text Available This paper combines the classic collaborative filtering algorithm with personalized recommendation algorithm based on network structure. For the data sparsity and malicious behavior problems of traditional collaborative filtering algorithm, the paper introduces a new kind of social network-based collaborative filtering algorithm. In order to improve the accuracy of the personalized recommendation technology, we first define empty state in the state space of multi-dimensional semi-Markov processes and obtain extended multi-dimensional semi-Markov processes which are combined with social network analysis theory, and then we get social network information flow model. The model describes the flow of information between the members of the social network. So, we propose collaborative filtering algorithm based on social network information flow model. The algorithm uses social network information and combines user trust with user interest and find nearest neighbors of the target user and then forms a project recommended to improve the accuracy of recommended. Compared with the traditional collaborative filtering algorithm, the algorithm can effectively alleviate the sparsity and malicious behavior problem, and significantly improve the quality of the recommendation system recommended.
An enhancement algorithm for low quality fingerprint image based on edge filter and Gabor filter
Xue, Jun-tao; Liu, Jie; Liu, Zheng-guang
2009-07-01
On account of restriction of man-made and collection environment, the fingerprint image generally has low quality, especially a contaminated background. In this paper, an enhancement algorithm based on edge filter and Gabor filter is proposed to solve this kind of fingerprint image. Firstly, a gray-based algorithm is used to enhance the edge and segment the image. Then, a multilevel block size method is used to extract the orientation field from segmented fingerprint image. Finally, Gabor filter is used to fulfill the enhancement of the fingerprint image. The experiment results show that the proposed enhancement algorithm is effective than the normal Gabor filter algorithm. The fingerprint image enhance by our algorithm has better enhancement effect, so it is helpful for the subsequent research, such as classification, feature exaction and identification.
Practice Utilization of Algorithms for Analog Filter Group Delay Optimization
Directory of Open Access Journals (Sweden)
K. Hajek
2007-04-01
Full Text Available This contribution deals with an application of three different algorithms which optimize a group delay of analog filters. One of them is a part of the professional circuit simulator Micro Cap 7 and the others two original algorithms are developed in the MATLAB environment. An all-pass network is used to optimize the group delay of an arbitrary analog filter. Introduced algorithms look for an optimal order and optimal coefficients of an all-pass network transfer function. Theoretical foundations are introduced and all algorithms are tested using the optimization of testing analog filter. The optimization is always run three times because the second, third and fourth-order all-pass network is used. An equalization of the original group delay is a main objective of these optimizations. All outputs of all algorithms are critically evaluated and also described.
Acceleration of Directional Medain Filter Based Deinterlacing Algorithm (DMFD
Directory of Open Access Journals (Sweden)
Addanki Purna Ramesh
2011-12-01
Full Text Available This paper presents a novel directional median filter based deinterlacing algorithm (DMFD. DMFD is a content adaptive spatial deinterlacing algorithm that finds the direction of the edge and applies the median filtering along the edge to interpolate the odd pixels from the 5 pixels from the upper and 5 pixels from the lower even lines of the field. The proposed algorithm gives a significance improvement of 3db for baboon standard test image that has high textured content compared to CADEM, DOI, and MELA and also gives improved average PSNR compared previous algorithms. The algorithm written and tested in C and ported onto Altera’s NIOS II embedded soft processor and configured in CYCLONE-II FPGA. The ISA of Nios-II processor has extended with two additional instructions for calculation of absolute difference and minimum of four numbers to accelerate the FPGA implementation of the algorithms by 3.2 times
SAR focusing of P-band ice sounding data using back-projection
DEFF Research Database (Denmark)
Kusk, Anders; Dall, Jørgen
2010-01-01
accommodated at the expense of computation time. The back-projection algorithm can be easily parallelized however, and can advantageously be implemented on a graphics processing unit (GPU). Results from using the back-projection algorithm on POLARIS ice sounder data from North Greenland shows that the quality...... of data is improved by the processing, and the performance of the GPU implementation allows for very fast focusing....
Information filtering via weighted heat conduction algorithm
Liu, Jian-Guo; Guo, Qiang; Zhang, Yi-Cheng
2011-06-01
In this paper, by taking into account effects of the user and object correlations on a heat conduction (HC) algorithm, a weighted heat conduction (WHC) algorithm is presented. We argue that the edge weight of the user-object bipartite network should be embedded into the HC algorithm to measure the object similarity. The numerical results indicate that both the accuracy and diversity could be improved greatly compared with the standard HC algorithm and the optimal values reached simultaneously. On the Movielens and Netflix datasets, the algorithmic accuracy, measured by the average ranking score, can be improved by 39.7% and 56.1% in the optimal case, respectively, and the diversity could reach 0.9587 and 0.9317 when the recommendation list equals to 5. Further statistical analysis indicates that, in the optimal case, the distributions of the edge weight are changed to the Poisson form, which may be the reason why HC algorithm performance could be improved. This work highlights the effect of edge weight on a personalized recommendation study, which maybe an important factor affecting personalized recommendation performance.
An Efficient Conflict Detection Algorithm for Packet Filters
Lee, Chun-Liang; Lin, Guan-Yu; Chen, Yaw-Chung
Packet classification is essential for supporting advanced network services such as firewalls, quality-of-service (QoS), virtual private networks (VPN), and policy-based routing. The rules that routers use to classify packets are called packet filters. If two or more filters overlap, a conflict occurs and leads to ambiguity in packet classification. This study proposes an algorithm that can efficiently detect and resolve filter conflicts using tuple based search. The time complexity of the proposed algorithm is O(nW+s), and the space complexity is O(nW), where n is the number of filters, W is the number of bits in a header field, and s is the number of conflicts. This study uses the synthetic filter databases generated by ClassBench to evaluate the proposed algorithm. Simulation results show that the proposed algorithm can achieve better performance than existing conflict detection algorithms both in time and space, particularly for databases with large numbers of conflicts.
Mobashsher, Ahmed Toaha; Mahmoud, A.; Abbosh, A. M.
2016-02-01
Intracranial hemorrhage is a medical emergency that requires rapid detection and medication to restrict any brain damage to minimal. Here, an effective wideband microwave head imaging system for on-the-spot detection of intracranial hemorrhage is presented. The operation of the system relies on the dielectric contrast between healthy brain tissues and a hemorrhage that causes a strong microwave scattering. The system uses a compact sensing antenna, which has an ultra-wideband operation with directional radiation, and a portable, compact microwave transceiver for signal transmission and data acquisition. The collected data is processed to create a clear image of the brain using an improved back projection algorithm, which is based on a novel effective head permittivity model. The system is verified in realistic simulation and experimental environments using anatomically and electrically realistic human head phantoms. Quantitative and qualitative comparisons between the images from the proposed and existing algorithms demonstrate significant improvements in detection and localization accuracy. The radiation and thermal safety of the system are examined and verified. Initial human tests are conducted on healthy subjects with different head sizes. The reconstructed images are statistically analyzed and absence of false positive results indicate the efficacy of the proposed system in future preclinical trials.
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.
Filtered refocusing: a volumetric reconstruction algorithm for plenoptic-PIV
Fahringer, Timothy W.; Thurow, Brian S.
2016-09-01
A new algorithm for reconstruction of 3D particle fields from plenoptic image data is presented. The algorithm is based on the technique of computational refocusing with the addition of a post reconstruction filter to remove the out of focus particles. This new algorithm is tested in terms of reconstruction quality on synthetic particle fields as well as a synthetically generated 3D Gaussian ring vortex. Preliminary results indicate that the new algorithm performs as well as the MART algorithm (used in previous work) in terms of the reconstructed particle position accuracy, but produces more elongated particles. The major advantage to the new algorithm is the dramatic reduction in the computational cost required to reconstruct a volume. It is shown that the new algorithm takes 1/9th the time to reconstruct the same volume as MART while using minimal resources. Experimental results are presented in the form of the wake behind a cylinder at a Reynolds number of 185.
Filtered gradient reconstruction algorithm for compressive spectral imaging
Mejia, Yuri; Arguello, Henry
2017-04-01
Compressive sensing matrices are traditionally based on random Gaussian and Bernoulli entries. Nevertheless, they are subject to physical constraints, and their structure unusually follows a dense matrix distribution, such as the case of the matrix related to compressive spectral imaging (CSI). The CSI matrix represents the integration of coded and shifted versions of the spectral bands. A spectral image can be recovered from CSI measurements by using iterative algorithms for linear inverse problems that minimize an objective function including a quadratic error term combined with a sparsity regularization term. However, current algorithms are slow because they do not exploit the structure and sparse characteristics of the CSI matrices. A gradient-based CSI reconstruction algorithm, which introduces a filtering step in each iteration of a conventional CSI reconstruction algorithm that yields improved image quality, is proposed. Motivated by the structure of the CSI matrix, Φ, this algorithm modifies the iterative solution such that it is forced to converge to a filtered version of the residual ΦTy, where y is the compressive measurement vector. We show that the filtered-based algorithm converges to better quality performance results than the unfiltered version. Simulation results highlight the relative performance gain over the existing iterative algorithms.
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.
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.
Filtered-X Affine Projection Algorithms for Active Noise Control Using Volterra Filters
Directory of Open Access Journals (Sweden)
Sicuranza Giovanni L
2004-01-01
Full Text Available We consider the use of adaptive Volterra filters, implemented in the form of multichannel filter banks, as nonlinear active noise controllers. In particular, we discuss the derivation of filtered-X affine projection algorithms for homogeneous quadratic filters. According to the multichannel approach, it is then easy to pass from these algorithms to those of a generic Volterra filter. It is shown in the paper that the AP technique offers better convergence and tracking capabilities than the classical LMS and NLMS algorithms usually applied in nonlinear active noise controllers, with a limited complexity increase. This paper extends in two ways the content of a previous contribution published in Proc. IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03, Grado, Italy, June 2003. First of all, a general adaptation algorithm valid for any order of affine projections is presented. Secondly, a more complete set of experiments is reported. In particular, the effects of using multichannel filter banks with a reduced number of channels are investigated and relevant results are shown.
A novel iris localization algorithm using correlation filtering
Pohit, Mausumi; Sharma, Jitu
2015-06-01
Fast and efficient segmentation of iris from the eye images is a primary requirement for robust database independent iris recognition. In this paper we have presented a new algorithm for computing the inner and outer boundaries of the iris and locating the pupil centre. Pupil-iris boundary computation is based on correlation filtering approach, whereas iris-sclera boundary is determined through one dimensional intensity mapping. The proposed approach is computationally less extensive when compared with the existing algorithms like Hough transform.
Filter algorithm for airborne LIDAR data
Li, Qi; Ma, Hongchao; Wu, Jianwei; Tian, Liqiao; Qiu, Feng
2007-11-01
Airborne laser scanning data has become an accepted data source for highly automated acquisition of digital surface models(DSM) as well as for the generation of digital terrain models(DTM). To generate a high quality DTM using LIDAR data, 3D off-terrain points have to be separated from terrain points. Even though most LIDAR system can measure "last-return" data points, these "last-return" point often measure ground clutter like shrubbery, cars, buildings, and the canopy of dense foliage. Consequently, raw LIDAR points must be post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping application. Various techniques have been proposed to extract the ground surface from airborne LIDAR data. The basic problem is the separation of terrain points from off-terrain points which are both recorded by the LIDAR sensor. In this paper a new method, combination of morphological filtering and TIN densification, is proposed to separate 3D off-terrain points.
Fixed Scan Area Tracking with Track Splitting Filtering Algorithm
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar; Ahmed, Zaki
2006-01-01
to develop procedures that would enable a more general performance assessment. Therefore, a non-deterministic scenario is adopted, which basically provide a more appropriate approach for the evaluation of a tracking system based on track splitting filter algorithm. The objects are generated within a fixed...
IIR Filter Modeling Using an Algorithm Inspired on Electromagnetism
Directory of Open Access Journals (Sweden)
Cuevas-Jiménez E.
2013-01-01
Full Text Available Infinite-impulse-response (IIR filtering provides a powerful approach for solving a variety of problems. However, its design represents a very complicated task, since the error surface of IIR filters is generally multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a new method based on the Electromagnetism-Like Optimization Algorithm (EMO is proposed for IIR filter modeling. EMO originates from the electro-magnetism theory of physics by assuming potential solutions as electrically charged particles which spread around the solution space. The charge of each particle depends on its objective function value. This algorithm employs a collective attraction-repulsion mechanism to move the particles towards optimality. The experimental results confirm the high performance of the proposed method in solving various benchmark identification problems.
Collaborative Filtering Algorithms Based on Kendall Correlation in Recommender Systems
Institute of Scientific and Technical Information of China (English)
YAO Yu; ZHU Shanfeng; CHEN Xinmeng
2006-01-01
In this work, Kendall correlation based collaborative filtering algorithms for the recommender systems are proposed. The Kendall correlation method is used to measure the correlation amongst users by means of considering the relative order of the users' ratings. Kendall based algorithm is based upon a more general model and thus could be more widely applied in e-commerce. Another discovery of this work is that the consideration of only positive correlated neighbors in prediction, in both Pearson and Kendall algorithms, achieves higher accuracy than the consideration of all neighbors, with only a small loss of coverage.
Relevance Feedback Algorithm Based on Collaborative Filtering in Image Retrieval
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Yan Sun
2010-12-01
Full Text Available Content-based image retrieval is a very dynamic study field, and in this field, how to improve retrieval speed and retrieval accuracy is a hot issue. The retrieval performance can be improved when applying relevance feedback to image retrieval and introducing the participation of people to the retrieval process. However, as for many existing image retrieval methods, there are disadvantages of relevance feedback with information not being fully saved and used, and their accuracy and flexibility are relatively poor. Based on this, the collaborative filtering technology was combined with relevance feedback in this study, and an improved relevance feedback algorithm based on collaborative filtering was proposed. In the method, the collaborative filtering technology was used not only to predict the semantic relevance between images in database and the retrieval samples, but to analyze feedback log files in image retrieval, which can make the historical data of relevance feedback be fully used by image retrieval system, and further to improve the efficiency of feedback. The improved algorithm presented has been tested on the content-based image retrieval database, and the performance of the algorithm has been analyzed and compared with the existing algorithms. The experimental results showed that, compared with the traditional feedback algorithms, this method can obviously improve the efficiency of relevance feedback, and effectively promote the recall and precision of image retrieval.
Study of a new fast adaptive filtering algorithm
Institute of Scientific and Technical Information of China (English)
WANG Zhen-li; ZHANG Xiong-wei; YANG Ji-bin; CHEN Gong
2006-01-01
A new fast adaptive filtering algorithm was presented by using the correlations between the signal's former and latter sampling times.The proof of the new algorithm was also presented,which showed that its optimal weight vector was the solution of generalized Wiener equation.The new algorithm was of simple structure,fast convergence,and less stable maladjustment.It can handle many signals including both uncorrelated signal and strong correlation signal.However,its computational complexity was comparable to that of the normalized least-mean-square (NLMS) algorithm.Simulation results show that for uncorrelated signals,the stable maladjustment of the proposed algorithm is less than that of the VS-NLMS algorithm,and its convergence is comparable to that of the algorithm proposed in references but faster than that of L.E-LMS algorithm.For strong correlation signal,its performance is superior to those of the NLMS algorithm and DCR-LMS algorithm.
Automatic Data Filter Customization Using a Genetic Algorithm
Mandrake, Lukas
2013-01-01
This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.
An Ant Colony Optimization Algorithm for Microwave Corrugated Filters Design
Directory of Open Access Journals (Sweden)
Ivan A. Mantilla-Gaviria
2013-01-01
Full Text Available A practical and useful application of the Ant Colony Optimization (ACO method for microwave corrugated filter design is shown. The classical, general purpose ACO method is adapted to deal with the microwave filter design problem. The design strategy used in this paper is an iterative procedure based on the use of an optimization method along with an electromagnetic simulator. The designs of high-pass and band-pass microwave rectangular waveguide filters working in the C-band and X-band, respectively, for communication applications, are shown. The average convergence performance of the ACO method is characterized by means of Monte Carlo simulations and compared with that obtained with the well-known Genetic Algorithm (GA. The overall performance, for the simulations presented herein, of the ACO is found to be better than that of the GA.
E-mail Spam Filtering Using Adaptive Genetic Algorithm
Directory of Open Access Journals (Sweden)
Jitendra Nath Shrivastava
2014-01-01
Full Text Available Now a day’s everybody email inbox is full with spam mails. The problem with spam mails is that they are not malicious in nature so generally don’t get blocked with firewall or filters etc., however, they are unwanted mails received by any internet users. In 2012, more that 50% emails of the total emails were spam emails. In this paper, a genetic algorithm based method for spam email filtering is discussed with its advantages and dis-advantages. The results presented in the paper are promising and suggested that GA can be a good option in conjunction with other e-mail filtering techniques can provide more robust solution.
Gravitation search algorithm: Application to the optimal IIR filter design
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Suman Kumar Saha
2014-01-01
Full Text Available This paper presents a global heuristic search optimization technique known as Gravitation Search Algorithm (GSA for the design of 8th order Infinite Impulse Response (IIR, low pass (LP, high pass (HP, band pass (BP and band stop (BS filters considering various non-linear characteristics of the filter design problems. This paper also adopts a novel fitness function in order to improve the stop band attenuation to a great extent. In GSA, law of gravity and mass interactions among different particles are adopted for handling the non-linear IIR filter design optimization problem. In this optimization technique, searcher agents are the collection of masses and interactions among them are governed by the Newtonian gravity and the laws of motion. The performances of the GSA based IIR filter designs have proven to be superior as compared to those obtained by real coded genetic algorithm (RGA and standard Particle Swarm Optimization (PSO. Extensive simulation results affirm that the proposed approach using GSA outperforms over its counterparts not only in terms of quality output, i.e., sharpness at cut-off, smaller pass band ripple, higher stop band attenuation, but also the fastest convergence speed with assured stability.
Development of adaptive IIR filtered-e LMS algorithm for active noise control
Institute of Scientific and Technical Information of China (English)
SUN Xu; MENG Guang; TENG Pengxiao; CHEN Duanshi
2003-01-01
Compared to finite impulse response (FIR) filters, infinite impulse response (IIR)filters can match the system better with much fewer coefficients, and hence the computationload is saved and the performance improves. Therefore, it is attractive to use IIR filters insteadof FIR filters in active noise control (ANC). However, filtered-U LMS (FULMS) algorithm, theIIR filter-based algorithm commonly used so far cannot ensure global convergence. A new IIRfilter based adaptive algorithm, which can ensure global convergence with computation loadonly slightly increasing, is proposed in this paper. The new algorithm is called as filtered-eLMS algorithm since the error signal of which need to be filtered. Simulation results show thatthe FELMS algorithm presents better performance than the FULMS algorithm.
Theory of affine projection algorithms for adaptive filtering
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...
Kalman filter based algorithms for PANDA rate at FAIR
Energy Technology Data Exchange (ETDEWEB)
Prencipe, Elisabetta; Ritman, James [IKP, Forschungszentrum Juelich (Germany); Rauch, Johannes [E18, Technische Universitaet Muenchen (Germany); Collaboration: PANDA-Collaboration
2015-07-01
PANDA at the future FAIR facility in Darmstadt is an experiment with a cooled antiproton beam in a range between 1.5 and 15 GeV/c, allowing a wide physics program in nuclear and particle physics. High average reaction rates up to 2.10{sup 7} interactions/s are expected. PANDA is the only experiment worldwide, which combines a solenoid field and a dipole field in an experiment with a fixed target topology. The tracking system must be able to reconstruct high momenta in the laboratory frame. The tracking system of PANDA involves the presence of a high performance silicon vertex detector, a GEM detector, a Straw-Tubes central tracker, a forward tracking system, and a luminosity monitor. The first three of those, are inserted in a solenoid homogeneous magnetic field (B=2 T), the latter two are inside a dipole magnetic field (B=2 Tm), The offline tracking algorithm is developed within the PandaRoot framework, which is a part of the FAIRRoot project. The algorithm is based on a tool containing the Kalman Filter equations and a deterministic annealing filter (GENFIT). The Kalman-Filter-based routines can perform extrapolations of track parameters and covariance matrices. In GENFIT2, the Runge-Kutta track representation is available. First results of an implementation of GENFIT2 in PandaRoot are presented. Resolutions and efficiencies for different beam momenta and different track hypotheses are shown.
Singh, R.; Verma, H. K.
2013-12-01
This paper presents a teaching-learning-based optimization (TLBO) algorithm to solve parameter identification problems in the designing of digital infinite impulse response (IIR) filter. TLBO based filter modelling is applied to calculate the parameters of unknown plant in simulations. Unlike other heuristic search algorithms, TLBO algorithm is an algorithm-specific parameter-less algorithm. In this paper big bang-big crunch (BB-BC) optimization and PSO algorithms are also applied to filter design for comparison. Unknown filter parameters are considered as a vector to be optimized by these algorithms. MATLAB programming is used for implementation of proposed algorithms. Experimental results show that the TLBO is more accurate to estimate the filter parameters than the BB-BC optimization algorithm and has faster convergence rate when compared to PSO algorithm. TLBO is used where accuracy is more essential than the convergence speed.
Genetic Algorithm Applied to the Eigenvalue Equalization Filtered-x LMS Algorithm (EE-FXLMS
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Stephan P. Lovstedt
2008-01-01
Full Text Available The FXLMS algorithm, used extensively in active noise control (ANC, exhibits frequency-dependent convergence behavior. This leads to degraded performance for time-varying tonal noise and noise with multiple stationary tones. Previous work by the authors proposed the eigenvalue equalization filtered-x least mean squares (EE-FXLMS algorithm. For that algorithm, magnitude coefficients of the secondary path transfer function are modified to decrease variation in the eigenvalues of the filtered-x autocorrelation matrix, while preserving the phase, giving faster convergence and increasing overall attenuation. This paper revisits the EE-FXLMS algorithm, using a genetic algorithm to find magnitude coefficients that give the least variation in eigenvalues. This method overcomes some of the problems with implementing the EE-FXLMS algorithm arising from finite resolution of sampled systems. Experimental control results using the original secondary path model, and a modified secondary path model for both the previous implementation of EE-FXLMS and the genetic algorithm implementation are compared.
Improvement of S/N ratio of seismic data by hyperbolic filter algorithm
Institute of Scientific and Technical Information of China (English)
Xue Hao; Yue Li; Baojun Yang
2006-01-01
This paper deals with the implementation of the hyperbolic filter algorithm for noise suppression of seismic data. Known the velocity of reflection event, utilizes the resemblance of reflection signal in each seismic trace, the hyperbolic filter algorithm is effective in enhance reflection event and suppress the random noise. This algorithm is used to CDP gathers also is compared with the algorithm of τ-p transform. Simulation shows the hyperbolic filter is effective and better than τ-p transform.
Parallel Backprojection: A Case Study in High-Performance Reconfigurable Computing
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Cordes Ben
2009-01-01
Full Text Available High-performance reconfigurable computing (HPRC is a novel approach to provide large-scale computing power to modern scientific applications. Using both general-purpose processors and FPGAs allows application designers to exploit fine-grained and coarse-grained parallelism, achieving high degrees of speedup. One scientific application that benefits from this technique is backprojection, an image formation algorithm that can be used as part of a synthetic aperture radar (SAR processing system. We present an implementation of backprojection for SAR on an HPRC system. Using simulated data taken at a variety of ranges, our implementation runs over 200 times faster than a similar software program, with an overall application speedup better than 50x. The backprojection application is easily parallelizable, achieving near-linear speedup when run on multiple nodes of a clustered HPRC system. The results presented can be applied to other systems and other algorithms with similar characteristics.
Parallel Backprojection: A Case Study in High-Performance Reconfigurable Computing
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available High-performance reconfigurable computing (HPRC is a novel approach to provide large-scale computing power to modern scientific applications. Using both general-purpose processors and FPGAs allows application designers to exploit fine-grained and coarse-grained parallelism, achieving high degrees of speedup. One scientific application that benefits from this technique is backprojection, an image formation algorithm that can be used as part of a synthetic aperture radar (SAR processing system. We present an implementation of backprojection for SAR on an HPRC system. Using simulated data taken at a variety of ranges, our implementation runs over 200 times faster than a similar software program, with an overall application speedup better than 50x. The backprojection application is easily parallelizable, achieving near-linear speedup when run on multiple nodes of a clustered HPRC system. The results presented can be applied to other systems and other algorithms with similar characteristics.
Application of particle filtering algorithm in image reconstruction of EMT
Wang, Jingwen; Wang, Xu
2015-07-01
To improve the image quality of electromagnetic tomography (EMT), a new image reconstruction method of EMT based on a particle filtering algorithm is presented. Firstly, the principle of image reconstruction of EMT is analyzed. Then the search process for the optimal solution for image reconstruction of EMT is described as a system state estimation process, and the state space model is established. Secondly, to obtain the minimum variance estimation of image reconstruction, the optimal weights of random samples obtained from the state space are calculated from the measured information. Finally, simulation experiments with five different flow regimes are performed. The experimental results have shown that the average image error of reconstruction results obtained by the method mentioned in this paper is 42.61%, and the average correlation coefficient with the original image is 0.8706, which are much better than corresponding indicators obtained by LBP, Landweber and Kalman Filter algorithms. So, this EMT image reconstruction method has high efficiency and accuracy, and provides a new method and means for EMT research.
Wesselink, J.M.; Berkhoff, A.P.
2008-01-01
In this paper, real-time results are given for broadband multichannel active noise control using the regularized modified filtered-error algorithm. As compared to the standard filtered-error algorithm, the improved convergence rate and stability of the algorithm are obtained by using an inner-outer
Directory of Open Access Journals (Sweden)
Wei Zhu
2016-06-01
Full Text Available In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF, interacting multiple models unscented Kalman filter (IMMUKF, 5CKF and the optimal mode transition matrix IMM (OMTM-IMM.
Zhu, Wei; Wang, Wei; Yuan, Gannan
2016-06-01
In order to improve the tracking accuracy, model estimation accuracy and quick response of multiple model maneuvering target tracking, the interacting multiple models five degree cubature Kalman filter (IMM5CKF) is proposed in this paper. In the proposed algorithm, the interacting multiple models (IMM) algorithm processes all the models through a Markov Chain to simultaneously enhance the model tracking accuracy of target tracking. Then a five degree cubature Kalman filter (5CKF) evaluates the surface integral by a higher but deterministic odd ordered spherical cubature rule to improve the tracking accuracy and the model switch sensitivity of the IMM algorithm. Finally, the simulation results demonstrate that the proposed algorithm exhibits quick and smooth switching when disposing different maneuver models, and it also performs better than the interacting multiple models cubature Kalman filter (IMMCKF), interacting multiple models unscented Kalman filter (IMMUKF), 5CKF and the optimal mode transition matrix IMM (OMTM-IMM).
Design of Low Pass Digital FIR Filter Using Cuckoo Search Algorithm
Taranjit Singh; Harvinder Singh Josan
2014-01-01
This paper presents a novel approach of designing linear phase FIR low pass filter using cuckoo Search Algorithm (CSA). FIR filter design is a multi-modal optimization problem. The conventional optimization techniques are not efficient for digital filter design. An iterative method is introduced to find the best solution of FIR filter design problem.Flat passband and high stopband attenuation are the major characteristics required in FIR filter design. To achieve these charact...
New Collaborative Filtering Algorithms Based on SVD++ and Differential Privacy
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Zhengzheng Xian
2017-01-01
Full Text Available Collaborative filtering technology has been widely used in the recommender system, and its implementation is supported by the large amount of real and reliable user data from the big-data era. However, with the increase of the users’ information-security awareness, these data are reduced or the quality of the data becomes worse. Singular Value Decomposition (SVD is one of the common matrix factorization methods used in collaborative filtering, which introduces the bias information of users and items and is realized by using algebraic feature extraction. The derivative model SVD++ of SVD achieves better predictive accuracy due to the addition of implicit feedback information. Differential privacy is defined very strictly and can be proved, which has become an effective measure to solve the problem of attackers indirectly deducing the personal privacy information by using background knowledge. In this paper, differential privacy is applied to the SVD++ model through three approaches: gradient perturbation, objective-function perturbation, and output perturbation. Through theoretical derivation and experimental verification, the new algorithms proposed can better protect the privacy of the original data on the basis of ensuring the predictive accuracy. In addition, an effective scheme is given that can measure the privacy protection strength and predictive accuracy, and a reasonable range for selection of the differential privacy parameter is provided.
Accurate two-dimensional IMRT verification using a back-projection EPID dosimetry method.
Wendling, M.; Louwe, R.J.W.; McDermott, L.N.; Sonke, J.J.; Herk, M. van; Mijnheer, B.J.
2006-01-01
The use of electronic portal imaging devices (EPIDs) is a promising method for the dosimetric verification of external beam, megavoltage radiation therapy-both pretreatment and in vivo. In this study, a previously developed EPID back-projection algorithm was modified for IMRT techniques and applied
A Novel Robust Interval Kalman Filter Algorithm for GPS/INS Integrated Navigation
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Chen Jiang
2016-01-01
Full Text Available Kalman filter is widely applied in data fusion of dynamic systems under the assumption that the system and measurement noises are Gaussian distributed. In literature, the interval Kalman filter was proposed aiming at controlling the influences of the system model uncertainties. The robust Kalman filter has also been proposed to control the effects of outliers. In this paper, a new interval Kalman filter algorithm is proposed by integrating the robust estimation and the interval Kalman filter in which the system noise and the observation noise terms are considered simultaneously. The noise data reduction and the robust estimation methods are both introduced into the proposed interval Kalman filter algorithm. The new algorithm is equal to the standard Kalman filter in terms of computation, but superior for managing with outliers. The advantage of the proposed algorithm is demonstrated experimentally using the integrated navigation of Global Positioning System (GPS and the Inertial Navigation System (INS.
Denoising of Noisy Pixels in Video by Neighborhood Correlation Filtering Algorithm
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P.Karunakaran
2012-07-01
Full Text Available A fast filtering algorithm for color video based on Neighborhood Correlation Filtering is presented. By utilizing a 3 × 3 pixel template, the algorithm can discriminate and filter various patterns of noise spots or blocks. In contrast with many kinds of median filtering algorithm, which may cause image blurring, it has much higher edge preserving ability. Furthermore, this algorithm is able to synchronously reflect image quality via amount, location and density statistics. Filtering of detected pixels is done by NCF algorithm based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE, the peak-signal-to-noise ratio (PSNR and the normalized color difference (NCD.
A Novel Approach to Fast Image Filtering Algorithm of Infrared Images based on Intro Sort Algorithm
Gupta, Kapil Kumar; Niranjan, Jitendra Kumar
2012-01-01
In this study we investigate the fast image filtering algorithm based on Intro sort algorithm and fast noise reduction of infrared images. Main feature of the proposed approach is that no prior knowledge of noise required. It is developed based on Stefan- Boltzmann law and the Fourier law. We also investigate the fast noise reduction approach that has advantage of less computation load. In addition, it can retain edges, details, text information even if the size of the window increases. Intro sort algorithm begins with Quick sort and switches to heap sort when the recursion depth exceeds a level based on the number of elements being sorted. This approach has the advantage of fast noise reduction by reducing the comparison time. It also significantly speed up the noise reduction process and can apply to real-time image processing. This approach will extend the Infrared images applications for medicine and video conferencing.
Wang, Zhenwu; Hut, Rolf; van de Giesen, Nick
2017-04-01
Particle filtering is a nonlinear and non-Gaussian dynamical filtering system. It has found widespread applications in hydrological data assimilation. In order to solve the loss of particle diversity exiting in resampling process of particle filter, this research proposes an improved particle filter algorithm using genetic algorithm optimization and Gamma test. This method combines the genetic algorithm and Gamma test into the resampling procedure of particle filter to improve the adaptability and performance of particle filter in data assimilation. First, the particles are classified to three different groups based on resampling method. The particles with high weight values remain unchanged. Then genetic algorithm is used to cross and variate the rest of the particles. In the process of the optimization, the Gamma test method is applied for monitoring the quality of the new generated particles. When the gamma statistic stays stable, the algorithm will end the optimization and continue to perturb next observations in particle algorithm. The algorithm is illustrated for the three-dimensional Lorenz model and the much more complex 40-dimensional Lorenz model. The results demonstrate this method can keep the diversity of the particles and enhance the performance of the particle filter, leading to the promising conjecture that the method is applicable to realistic hydrological problems.
Wang, Youhua; Nakayama, Kenji
1995-01-01
In this letter, we introduce a predictor based least square (PLS) algorithm. By involving both order- and time-update recursions, the PLS algorithm is found to have a more stable performance compared with the stable version (Version II) of the RLS algorithm shown in Ref. [1]. Nevertheless, the computational requirement is about 50% of that of the RLS algorithm. As an application, the PLS algorithm can be applied to the fast newton transversal filters (FNTF) [2]. The FNTF algorithms suffer fro...
Study on Performance Improvement of Oil Paint Image Filter Algorithm Using Parallel Pattern Library
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Siddhartha Mukherjee
2014-03-01
Full Text Available This paper gives a detailed study on the performanc e of oil paint image filter algorithm with various parameters applied on an image of RGB model . Oil Paint image processing, being very performance hungry, current research tries to find improvement using parallel pattern library. With increasing kernel-size, the processing time of oil paint image filter algorithm increases exponentially.
Implementation of FFT Algorithm using DSP TMS320F28335 for Shunt Active Power Filter
Patel, Pinkal Jashvantbhai; Patel, Rajesh M.; Patel, Vinod
2016-07-01
This work presents simulation, analysis and experimental verification of Fast Fourier Transform (FFT) algorithm for shunt active power filter based on three-level inverter. Different types of filters can be used for elimination of harmonics in the power system. In this work, FFT algorithm for reference current generation is discussed. FFT control algorithm is verified using PSIM simulation results with DLL block and C-code. Simulation results are compared with experimental results for FFT algorithm using DSP TMS320F28335 for shunt active power filter application.
Implementation of FFT Algorithm using DSP TMS320F28335 for Shunt Active Power Filter
Patel, Pinkal Jashvantbhai; Patel, Rajesh M.; Patel, Vinod
2017-06-01
This work presents simulation, analysis and experimental verification of Fast Fourier Transform (FFT) algorithm for shunt active power filter based on three-level inverter. Different types of filters can be used for elimination of harmonics in the power system. In this work, FFT algorithm for reference current generation is discussed. FFT control algorithm is verified using PSIM simulation results with DLL block and C-code. Simulation results are compared with experimental results for FFT algorithm using DSP TMS320F28335 for shunt active power filter application.
The CA3 "backprojection" to the dentate gyrus.
Scharfman, Helen E
2007-01-01
it by activating dentate gyrus GABAergic neurons. Thus, GABAergic inhibition normally controls the backprojection to dentate granule cells, analogous to the way GABAergic inhibition appears to control the perforant path input to granule cells. From this perspective, the dentate gyrus has two robust glutamatergic inputs, entorhinal cortex and CA3, and two "gates," or inhibitory filters that reduce the efficacy of both inputs, keeping granule cells relatively quiescent. When GABAergic inhibition is reduced experimentally, or under pathological conditions, CA3 pyramidal cells activate granule cells reliably, and do so primarily by disynaptic excitation that is mediated by mossy cells. We suggest that the backprojection has important functions normally that are dynamically regulated by nonprincipal cells of the dentate gyrus. Slightly reduced GABAergic input would lead to increased polysynaptic associative processing between CA3 and the dentate gyrus. Under pathological conditions associated with loss of GABAergic interneurons, the backprojection may support reverberatory excitatory activity between CA3, mossy cells, and granule cells, possibly enhanced by mossy fiber sprouting. In this case, the backprojection could be important to seizure activity originating in hippocampus, and help explain the seizure susceptibility of ventral hippocampus.
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.
Algorithm for Design of Digital Notch Filter Using Simulation
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Amit Verma
2013-08-01
Full Text Available A smooth waveform is generated of low frequency signal can be achieved through the Digital Notch Filter. Noise can be easily eliminated from a speech signal by using a Notch filter. In this paper the design of notch filter using MATLAB has been designed and implemented. The performance and characteristics of the filter has been shown in the waveform in the conclusion part of the paper.
Institute of Scientific and Technical Information of China (English)
YANGGuo-Sheng; WENCheng-Lin; TANMin
2004-01-01
A new multisensor distributed track fusion algorithm is put forward based on combiningthe feedback integration with the strong tracking Kalman filter. Firstly, an effective tracking gateis constructed by taking the intersection of the tracking gates formed before and after feedback.Secondly, on the basis of the constructed effective tracking gate, probabilistic data association andstrong tracking Kalman filter are combined to form the new multisensor distributed track fusionalgorithm. At last, simulation is performed on the original algorithm and the algorithm presented.
RSTFC: A Novel Algorithm for Spatio-Temporal Filtering and Classification of Single-Trial EEG.
Qi, Feifei; Li, Yuanqing; Wu, Wei
2015-12-01
Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain-computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an l2 -regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.
PSO Algorithm based Adaptive Median Filter for Noise Removal in Image Processing Application
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Ruby Verma
2016-07-01
Full Text Available A adaptive Switching median filter for salt and pepper noise removal based on genetic algorithm is presented. Proposed filter consist of two stages, a noise detector stage and a noise filtering stage. Particle swarm optimization seems to be effective for single objective problem. Noise Dictation stage works on it. In contrast to the standard median filter, the proposed algorithm generates the noise map of corrupted Image. Noise map gives information about the corrupted and non-corrupted pixels of Image. In filtering, filter calculates the median of uncorrupted neighbouring pixels and replaces the corrupted pixels. Extensive simulations are performed to validate the proposed filter. Simulated results show refinement both in Peak signal to noise ratio (PSNR and Image Quality Index value (IQI. Experimental results shown that proposed method is more effective than existing methods.
Design of Low Pass Digital FIR Filter Using Cuckoo Search Algorithm
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Taranjit Singh
2014-08-01
Full Text Available This paper presents a novel approach of designing linear phase FIR low pass filter using cuckoo Search Algorithm (CSA. FIR filter design is a multi-modal optimization problem. The conventional optimization techniques are not efficient for digital filter design. An iterative method is introduced to find the best solution of FIR filter design problem.Flat passband and high stopband attenuation are the major characteristics required in FIR filter design. To achieve these characteristics, a Cuckoo Search algorithm (CSA is proposed in this paper. CSA have been used here for the design of linear phase finite impulse response (FIR filters. Results are presented in this paper that seems to be promising tool for FIR filter design
A gradient-constrained morphological filtering algorithm for airborne LiDAR
Li, Yong; Wu, Huayi; Xu, Hanwei; An, Ru; Xu, Jia; He, Qisheng
2013-12-01
This paper presents a novel gradient-constrained morphological filtering algorithm for bare-earth extraction from light detection and ranging (LiDAR) data. Based on the gradient feature points determined by morphological half-gradients, the potential object points are located prior to filtering. Innovative gradient-constrained morphological operations are created, which are executed only for the potential object points. Compared with the traditional morphological operations, the new operations reduce many meaningless operations for object removal and consequently decrease the possibility of losing terrain to a great extent. The applicability and reliability of this algorithm are demonstrated by evaluating the filtering performance for fifteen sample datasets in various complex scenes. The proposed algorithm is found to achieve a high level of accuracy compared with eight other filtering algorithms tested by the International Society for Photogrammetry and Remote Sensing. Moreover, the proposed algorithm has minimal error oscillation for different landscapes, which is important for quality control of digital terrain model generation.
Huang, Quanzhen; Luo, Jun; Li, Hengyu; Wang, Xiaohua
2013-08-01
With the wide application of large-scale flexible structures in spacecraft, vibration control problems in these structures have become important design issues. The filtered-X least mean square (FXLMS) algorithm is the most popular one in current active vibration control using adaptive filtering. It assumes that the source of interference can be measured and the interference source is considered as the reference signal input to the controller. However, in the actual control system, this assumption is not accurate, because it does not consider the impact of the reference signal on the output feedback signal. In this paper, an adaptive vibration active control algorithm based on an infinite impulse response (IIR) filter structure (FULMS, filtered-U least mean square) is proposed. The algorithm is based on an FXLMS algorithm framework, which replaces the finite impulse response (FIR) filter with an IIR filter. This paper focuses on the structural design of the controller, the process of the FULMS filtering control method, the design of the experimental model object, and the experimental platform construction for the entire control system. The comparison of the FXLMS algorithm with FULMS is theoretically analyzed and experimentally validated. The results show that the FULMS algorithm converges faster and controls better. The design of the FULMS controller is feasible and effective and has greater value in practical applications of aerospace engineering.
A curvature filter and PDE based non-uniformity correction algorithm
Cheng, Kuanhong; Zhou, Huixin; Qin, Hanlin; Zhao, Dong; Qian, Kun; Rong, Shenghui; Yin, Shimin
2016-10-01
In this paper, a curvature filter and PDE based non-uniformity correction algorithm is proposed, the key point of this algorithm is the way to estimate FPN. We use anisotropic diffusion to smooth noise and Gaussian curvature filter to extract the details of original image. Then combine these two parts together by guided image filter and subtract the result from original image to get the crude approximation of FPN. After that, a Temporal Low Pass Filter (TLPF) is utilized to filter out random noise and get the accurate FPN. Finally, subtract the FPN from original image to achieve non-uniformity correction. The performance of this algorithm is tested with two infrared image sequences, and the experimental results show that the proposed method achieves a better non-uniformity correction performance.
AN ITERATIVE ALGORITHM FOR OPTIMAL DESIGN OF NON-FREQUENCY-SELECTIVE FIR DIGITAL FILTERS
Institute of Scientific and Technical Information of China (English)
Duan Miyi; Sun Chunlai; Liu Xin; Tian Xinguang
2008-01-01
This paper proposes a novel iterative algorithm for optimal design of non-frequency-se-lective Finite Impulse Response (FIR) digital filters based on the windowing method. Different from the traditional optimization concept of adjusting the window or the filter order in the windowing design of an FIR digital filter,the key idea of the algorithm is minimizing the approximation error by succes-sively modifying the design result through an iterative procedure under the condition of a fixed window length. In the iterative procedure,the known deviation of the designed frequency response in each iteration from the ideal frequency response is used as a reference for the next iteration. Because the approximation error can be specified variably,the algorithm is applicable for the design of FIR digital filters with different technical requirements in the frequency domain. A design example is employed to illustrate the efficiency of the algorithm.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
Institute of Scientific and Technical Information of China (English)
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Genetic algorithms and smoothing filters in solving the geophysical inversion problem
Directory of Open Access Journals (Sweden)
Šešum Vesna
2002-01-01
Full Text Available The combination of genetic algorithms, smoothing filters and geophysical tomography is used in solving the geophysical inversion problem. This hybrid technique is developed to improve the results obtained by using genetic algorithm sonly. The application of smoothing filters can improve the performance of GA implementation for solving the geophysical inversion problem. Some test-examples and the obtained comparative results are presented.
Institute of Scientific and Technical Information of China (English)
Li Zhuo; Chen Geng-Hua; Zhang Li-Hua; Yang Qian-Sheng; Feng Ji
2005-01-01
We present acomplementary least-mean-square algorithm of adaptive filtering for SQUID-based magnetocardiography, in which both rapid convergence and fine tracking are realized by switching the weight parameters back and forth between two filters according to the least mean square principle.
Glentis, George-Othon; Slump, Cornelis H.; Hermann, Otto E.
2000-01-01
In this paper a novel algorithm is presented for the efficient two-dimensional (2-D), mean squared error (MSE), FIR filtering and system identification. Filter masks of general boundaries are allowed. Efficient order updating recursions are developed by exploiting the spatial shift invariance
Federated unscented particle filtering algorithm for SINS/CNS/GPS system
Institute of Scientific and Technical Information of China (English)
HU Hai-dong; HUANG Xian-lin; LI Ming-ming; SONG Zhuo-yue
2010-01-01
To solve the problem of information fusion in the strapdown inertial navigation system(SINS)/celestial navigation system(CNS)/global positioning system(GPS)integrated navigation system described by the nonlinear/non-Gaussian error models,a new algorithm called the federated unscented particle filtering(FUPF)algorithm was introduced.In this algorithm,the unscented particle filter(UPF)served as the local filter,the federated filter was used to fuse outputs of all local filters,and the global filter result was obtained.Because the algorithm was not confined to the assumption of Gaussian noise,it was of great significance to integrated navigation systems described by the non-Gaussian noise.The proposed algorithm was tested in a vehicle's maneuvering trajectory,which included six flight phases: climbing,level flight,left turning,level flight,right turning and level flight.Simulation results are presented to demonstrate the improved performance of the FUPF over conventional federated unscented Kalman filter(FUKF).For instance,the mean of position-error decreases from(0.640×10 6 rad,0.667×10 6 rad,4.25 m)of FUKF to(0.403×10-6 rad,0.251 × 10 6 rad,1.36 m)of FUPF.In comparison of the FUKF,the FUPF performs more accurate in the SINS/CNS/GPS system described by the nonlinear/non-Gaussian error models.
A gate size estimation algorithm for data association filters
Institute of Scientific and Technical Information of China (English)
WANG MingHui; WAN Qun; YOU ZhiSheng
2008-01-01
The problem of forming validation regions or gates for new sensor measurements obtained when tracking targets in clutter is considered. Since the gate size is an integral part of the data association filter, this paper is intended to describe a way of estimating the gate size via the performance of the data association filter. That is, the gate size can be estimated by looking for the optimal performance of the data association filter. Simulations show that this estimation method of the gate size offers advantages over the common and classical estimation methods of the gate size, especially in a heavy clutter and/or false alarm environment.
FPGA-Based Architecture for a Generalized Parallel 2-D MRI Filtering Algorithm
Directory of Open Access Journals (Sweden)
Sami Hasan
2011-01-01
Full Text Available Problem statement: Current Neuroimaging developments, in biological research and diagnostics, demand an edge-defined and noise-free MRI scans. Thus, this study presents a generalized parallel 2-D MRI filtering algorithm with their FPGA-based implementation in a single unified architecture. The parallel 2-D MRI filtering algorithms are Edge, Sobel X, Sobel Y, Sobel X-Y, Blur, Smooth, Sharpen, Gaussian and Beta (HYB. Then, the nine MRI image filtering algorithm, has empirically improved to generate enhanced MRI scans filtering results without significantly affecting the developed performance indices of high throughput and low power consumption at maximum operating frequency. Approach: The parallel 2-d MRI filtering algorithms are developed and FPGA implemented using Xilinx System Generator tool within the ISE 12.3 development suite. Two unified architectures are behaviorally developed, depending on the abstraction level of implementation. For performance indices comparison, two Virtex-6 FPGA boards, namely, xc6vlX240Tl-1lff1759 and xc6vlX130Tl-1lff1156 are behaviorally targeted. Results: The improved parallel 2-D filtering algorithms enhanced the filtered MRI scans to be edge-defined and noise free grayscale imaging. The single architecture is efficiently prototyped to achieve: high filtering performance of (11230 frames/second throughput for 64*64 MRI grayscale scan, minimum power consumption of 0.86 Watt with a junction temperature of 52°C and a maximum frequency of up to (230 MHz. Conclusion: The improved parallel MRI filtering algorithms which are developed as a single unified architecture provide visibility enhancement within the filtered MRI scan to aid the physician in detecting brain diseases, e.g., trauma or intracranial haemorrhage. The high filtering throughput is feasibly nominee the nine parallel MRI filtering algorithms for applications such as real-time MRI potential future applications. Future Work: a set of parallel 3-D f
FPGA Implementation of Optimal Filtering Algorithm for TileCal ROD System
Torres, J; Castillo, V; Cuenca, C; Ferrer, A; Fullana, E; González, V; Higón, E; Poveda, J; Ruiz-Martinez, A; Salvachúa, B; Sanchis, E; Solans, C; Valero, A; Valls, J A
2008-01-01
Traditionally, Optimal Filtering Algorithm has been implemented using general purpose programmable DSP chips. Alternatively, new FPGAs provide a highly adaptable and flexible system to develop this algorithm. TileCal ROD is a multi-channel system, where similar data arrives at very high sampling rates and is subject to simultaneous tasks. It include different FPGAs with high I/O and with parallel structures that provide a benefit at a data analysis. The Optical Multiplexer Board is one of the elements presents in TileCal ROD System. It has FPGAs devices that present an ideal platform for implementing Optimal Filtering Algorithm. Actually this algorithm is performing in the DSPs included at ROD Motherboard. This work presents an alternative to implement Optimal Filtering Algorithm.
New Approach for IIR Adaptive Lattice Filter Structure Using Simultaneous Perturbation Algorithm
Martinez, Jorge Ivan Medina; Nakano, Kazushi; Higuchi, Kohji
Adaptive infinite impulse response (IIR), or recursive, filters are less attractive mainly because of the stability and the difficulties associated with their adaptive algorithms. Therefore, in this paper the adaptive IIR lattice filters are studied in order to devise algorithms that preserve the stability of the corresponding direct-form schemes. We analyze the local properties of stationary points, a transformation achieving this goal is suggested, which gives algorithms that can be efficiently implemented. Application to the Steiglitz-McBride (SM) and Simple Hyperstable Adaptive Recursive Filter (SHARF) algorithms is presented. Also a modified version of Simultaneous Perturbation Stochastic Approximation (SPSA) is presented in order to get the coefficients in a lattice form more efficiently and with a lower computational cost and complexity. The results are compared with previous lattice versions of these algorithms. These previous lattice versions may fail to preserve the stability of stationary points.
Weng, Jing-Feng; Lo, Yu-Lung
2012-05-07
For 3D objects with height discontinuities, the image reconstruction performance of interferometric systems is adversely affected by the presence of noise in the wrapped phase map. Various schemes have been proposed for detecting residual noise, speckle noise and noise at the lateral surfaces of the discontinuities. However, in most schemes, some noisy pixels are missed and noise detection errors occur. Accordingly, this paper proposes two robust filters (designated as Filters A and B, respectively) for improving the performance of the phase unwrapping process for objects with height discontinuities. Filter A comprises a noise and phase jump detection scheme and an adaptive median filter, while Filter B replaces the detected noise with the median phase value of an N × N mask centered on the noisy pixel. Filter A enables most of the noise and detection errors in the wrapped phase map to be removed. Filter B then detects and corrects any remaining noise or detection errors during the phase unwrapping process. Three reconstruction paths are proposed, Path I, Path II and Path III. Path I combines the path-dependent MACY algorithm with Filters A and B, while Paths II and III combine the path-independent cellular automata (CA) algorithm with Filters A and B. In Path II, the CA algorithm operates on the whole wrapped phase map, while in Path III, the CA algorithm operates on multiple sub-maps of the wrapped phase map. The simulation and experimental results confirm that the three reconstruction paths provide a robust and precise reconstruction performance given appropriate values of the parameters used in the detection scheme and filters, respectively. However, the CA algorithm used in Paths II and III is relatively inefficient in identifying the most suitable unwrapping paths. Thus, of the three paths, Path I yields the lowest runtime.
Backprojection by upsampled Fourier series expansion and interpolated FFT.
Tabei, M; Ueda, M
1992-01-01
A fast backprojection method through the use of interpolated fast Fourier transform (FFT) is presented. The computerized tomography (CT) reconstruction by the convolution backprojection (CBP) method has produced precise images. However, the backprojection part of the conventional CBP method is not very efficient. The authors propose an alternative approach to interpolating and backprojecting the convolved projections onto the image frame. First, the upsampled Fourier series expansion of the convolved projection is calculated. Then, using a Gaussian function, it is projected by the aliasing-free interpolation of FFT bins onto a rectangular grid in the frequency domain. The total amount of computation in this procedure for a 512x512 image is 1/5 of the conventional backprojection method with linear interpolation. This technique also allows the arbitrary control of the frequency characteristics.
Spatial mask filtering algorithm for partial discharge pulse extraction of large generators
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
A spatial mask filter algorithm (SMF) for partial discharge (PD) pulse extraction is proposed in this then direct multiplication of coefficients at two adjacent scales is used to detect singularity points of the signal tain the last spatial mask filter. By multiplication of wavelet coefficients with the final mask filter and wavelet reconstruction process, partial discharge pulses are extracted. The results of digital simulation and practical experiment show that this method is superior to traditional wavelet shrinkage method (TWS). This algorithm not only can increase the signal to noise ratio (SNR), but also can preserve the energy and pulse amplitude.
TV-constrained incremental algorithms for low-intensity CT image reconstruction
DEFF Research Database (Denmark)
Rose, Sean D.; Andersen, Martin S.; Sidky, Emil Y.
2015-01-01
constraint can be guided by an image reconstructed by filtered backprojection (FBP). We apply our algorithm to low-dose synchrotron X-ray CT data from the Advanced Photon Source (APS) at Argonne National Labs (ANL) to demonstrate its potential utility. We find that the algorithm provides a means of edge......-preserving regularization with the potential to generate useful images at low iteration number in low-dose CT....
A SLAM Algorithm Based on Adaptive Cubature Kalman Filter
Directory of Open Access Journals (Sweden)
Fei Yu
2014-01-01
CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved. It verifies that the proposed ACKF-SLAM algorithm is valid and feasible.
Schlifske, Daniel; Medeiros, Henry
2016-03-01
Modern CT image reconstruction algorithms rely on projection and back-projection operations to refine an image estimate in iterative image reconstruction. A widely-used state-of-the-art technique is distance-driven projection and back-projection. While the distance-driven technique yields superior image quality in iterative algorithms, it is a computationally demanding process. This has a detrimental effect on the relevance of the algorithms in clinical settings. A few methods have been proposed for enhancing the distance-driven technique in order to take advantage of modern computer hardware. This paper explores a two-dimensional extension of the branchless method proposed by Samit Basu and Bruno De Man. The extension of the branchless method is named "pre-integration" because it achieves a significant performance boost by integrating the data before the projection and back-projection operations. It was written with Nvidia's CUDA platform and carefully designed for massively parallel GPUs. The performance and the image quality of the pre-integration method were analyzed. Both projection and back-projection are significantly faster with preintegration. The image quality was analyzed using cone beam image reconstruction algorithms within Jeffrey Fessler's Image Reconstruction Toolbox. Images produced from regularized, iterative image reconstruction algorithms using the pre-integration method show no significant impact to image quality.
Development of a noise reduction filter algorithm for pediatric body images in multidetector CT.
Nishimaru, Eiji; Ichikawa, Katsuhiro; Okita, Izumi; Tomoshige, Yukihiro; Kurokawa, Takehiro; Nakamura, Yuko; Suzuki, Masayuki
2010-12-01
Recently, several types of post-processing image filter which was designed to reduce noise allowing a corresponding dose reduction in CT images have been proposed and these were reported to be useful for noise reduction of CT images of adult patients. However, these have not been reported on adaptation for pediatric patients. Because they are not very effective with small (<20 cm) display fields of view, they could not be used for pediatric (e.g., premature babies and infants) body CT images. In order to solve this restriction, we have developed a new noise reduction filter algorithm which can be applicable for pediatric body CT images. This algorithm is based on a three-dimensional post processing, in which output pixel values are calculated by multi-directional, one-dimensional median filters on original volumetric datasets. The processed directions were selected except in in-plane (axial plane) direction, and consequently the in-plane spatial resolution was not affected by the filter. Also, in other directions, the spatial resolutions including slice thickness were almost maintained due to a characteristic of non-linear filtering of the median filter. From the results of phantom studies, the proposed algorithm could reduce standard deviation values as a noise index by up to 30% without affecting the spatial resolution of all directions, and therefore, contrast-to-noise ratio was improved by up to 30%. This newly developed filter algorithm will be useful for the diagnosis and radiation dose reduction of pediatric body CT images.
Seismic data filtering using non-local means algorithm based on structure tensor
Yang, Shuai; Chen, Anqing; Chen, Hongde
2017-05-01
Non-Local means algorithm is a new and effective filtering method. It calculates weights of all similar neighborhoods' center points relative to filtering point within searching range by Gaussian weighted Euclidean distance between neighborhoods, then gets filtering point's value by weighted average to complete the filtering operation. In this paper, geometric distance of neighborhood's center point is taken into account in the distance measure calculation, making the non-local means algorithm more reasonable. Furthermore, in order to better protect the geometry structure information of seismic data, we introduce structure tensor that can depict the local geometrical features of seismic data. The coherence measure, which reflects image local contrast, is extracted from the structure tensor, is integrated into the non-local means algorithm to participate in the weight calculation, the control factor of geometry structure similarity is added to form a non-local means filtering algorithm based on structure tensor. The experimental results prove that the algorithm can effectively restrain noise, with strong anti-noise and amplitude preservation effect, improving PSNR and protecting structure information of seismic image. The method has been successfully applied in seismic data processing, indicating that it is a new and effective technique to conduct the structure-preserved filtering of seismic data.
图像去噪算法的研究%Research on Image Filtering Algorithm
Institute of Scientific and Technical Information of China (English)
穆远彪; 于亚龙
2014-01-01
Analyzes the denoising algorithm based on salt, pepper noise and gaussian noise. These algorithms have median filtering, average filter and Wiener filter. The experimental results show that the median filtering has better effect for salt and pepper noise. Compared with medi-an filtering and average filter, the Wiener filter has the better effect for the Gaussian noising. However, the Wiener filter algorithm is easy to loss of edge information and almost invalidly to salt and pepper noise.%主要针对图像的高斯噪声和椒盐噪声的去噪算法进行研究，分别使用到中值滤波、均值滤波和维纳滤波三种滤波算法。实验结果表明中值滤波对于椒盐噪声有更好的去噪效果；维纳滤波对高斯噪声有明显的作用，相比中值滤波和均值滤波，维纳滤波对高斯噪声有很好的抑制效果，与此同时，维纳滤波却容易丢失边缘信息且对椒盐噪声几乎没有去噪作用。
A filter algorithm for multi-measurement nonlinear system with parameter perturbation
Institute of Scientific and Technical Information of China (English)
GUO Yun-fei; WEI Wei; XUE An-ke; MAO Dong-cai
2006-01-01
An improved interacting multiple models particle filter (IMM-PF) algorithm is proposed for multi-measurement nonlinear system with parameter perturbation. It divides the perturbation region into sub-regions and assigns each of them a particle filter. Hence the perturbation problem is converted into a multi-model filters problem. It combines the multiple measurements into a fusion value according to their likelihood function. In the simulation study, we compared it with the IMM-KF and the H-infinite filter; the results testify to its advantage over the other two methods.
Big Bang–Big Crunch Optimization Algorithm for Linear Phase Fir Digital Filter Design
Directory of Open Access Journals (Sweden)
Ms. Rashmi Singh Dr. H. K. Verma
2012-02-01
Full Text Available The Big Bang–Big Crunch (BB–BC optimization algorithm is a new optimization method that relies on the Big Bang and Big Crunch theory, one of the theories of the evolution of the universe. In this paper, a Big Bang–Big Crunch algorithm has been used here for the design of linear phase finite impulse response (FIR filters. Here the experimented fitness function based on the mean squared error between the actual and the ideal filter response. This paper presents the plot of magnitude response of FIR filters and error graph. The BB-BC seems to be promising tool for FIR filter design especially in a dynamic environment where filter coefficients have to be adapted and fast convergence is of importance.
Research on SINS Alignment Algorithm Based on FIR Filters
Institute of Scientific and Technical Information of China (English)
LIAN Jun-xiang; HU De-wen; WU Yuan-xin; HU Xiao-ping
2007-01-01
An inertial frame based alignment (IFBA) method is presented, especially for the applications on a rocking platform, e.g., marine applications. Defining the initial body frame as the inertial frame, the IFBA method achieves the alignment by virtue of a cascade of low-pass FIR filters, which attenuate the disturbing acceleration and maintain the gravity vector. The aligning time rests with the orders of the FIR filter group, and the method is suitable for large initial misali gnment case. An alignment scheme comprising a coarse phase by the IFBA method an d a fine phase by a Kalman filter is presented. Both vehicle-based and ship-based alignment experiments were carried out. The results show that the proposed scheme converges much faster than the traditional method at no cost of precision and also works well under any large initial misalignment.
PERFORMANCE EVALUATION OF DIFFERENT GROUND FILTERING ALGORITHMS FOR UAV-BASED POINT CLOUDS
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C. Serifoglu
2016-06-01
Full Text Available Digital Elevation Model (DEM generation is one of the leading application areas in geomatics. Since a DEM represents the bare earth surface, the very first step of generating a DEM is to separate the ground and non-ground points, which is called ground filtering. Once the point cloud is filtered, the ground points are interpolated to generate the DEM. LiDAR (Light Detection and Ranging point clouds have been used in many applications thanks to their success in representing the objects they belong to. Hence, in the literature, various ground filtering algorithms have been reported to filter the LiDAR data. Since the LiDAR data acquisition is still a costly process, using point clouds generated from the UAV images to produce DEMs is a reasonable alternative. In this study, point clouds with three different densities were generated from the aerial photos taken from a UAV (Unmanned Aerial Vehicle to examine the effect of point density on filtering performance. The point clouds were then filtered by means of five different ground filtering algorithms as Progressive Morphological 1D (PM1D, Progressive Morphological 2D (PM2D, Maximum Local Slope (MLS, Elevation Threshold with Expand Window (ETEW and Adaptive TIN (ATIN. The filtering performance of each algorithm was investigated qualitatively and quantitatively. The results indicated that the ATIN and PM2D algorithms showed the best overall ground filtering performances. The MLS and ETEW algorithms were found as the least successful ones. It was concluded that the point clouds generated from the UAVs can be a good alternative for LiDAR data.
RB Particle Filter Time Synchronization Algorithm Based on the DPM Model
Directory of Open Access Journals (Sweden)
Chunsheng Guo
2015-09-01
Full Text Available Time synchronization is essential for node localization, target tracking, data fusion, and various other Wireless Sensor Network (WSN applications. To improve the estimation accuracy of continuous clock offset and skew of mobile nodes in WSNs, we propose a novel time synchronization algorithm, the Rao-Blackwellised (RB particle filter time synchronization algorithm based on the Dirichlet process mixture (DPM model. In a state-space equation with a linear substructure, state variables are divided into linear and non-linear variables by the RB particle filter algorithm. These two variables can be estimated using Kalman filter and particle filter, respectively, which improves the computational efficiency more so than if only the particle filter was used. In addition, the DPM model is used to describe the distribution of non-deterministic delays and to automatically adjust the number of Gaussian mixture model components based on the observational data. This improves the estimation accuracy of clock offset and skew, which allows achieving the time synchronization. The time synchronization performance of this algorithm is also validated by computer simulations and experimental measurements. The results show that the proposed algorithm has a higher time synchronization precision than traditional time synchronization algorithms.
Leroux, C; Dainty, C
2010-01-18
Most Shack-Hartmann based aberrometers use infrared light, for the comfort of the patients. A large amount of the light that is scattered from the retinal layers is recorded by the detector as background, from which it is not trivial to estimate the centroid of the Shack-Hartmann spot. For a centroiding algorithm, background light can lead to a systematic bias of the centroid positions towards the centre of the software window. We implement a matched filter algorithm for the estimation of the centroid positions of the Shack-Hartmann spots recorded by our aberrometer. We briefly present the performance of our algorithm, and recall the well-known robustness of the matched filter algorithm to background light. Using data collected on 5 human eyes, we parameterise a simple and fast centroiding algorithm and reduce the difference between the two algorithms down to a mean residual wavefront of 0.02 microm rms.
Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
CHU Dongsheng; LIANG Meng; SHI Xin; ZHANG Ling
2004-01-01
A stochastic two-dimensional Fornasini-Marchesini's Model Ⅱ (2-D FMM Ⅱ) with multiplicative noise is given,and a filtering algorithm for this model, which is optimal in the sense of linear minimum-variance, is developed. The stochastic 2-D FMM Ⅱ with multiplicative noise can be reduced to a 1-D model, and the proposed optimal filtering algorithm for the stochastic 2-D FMM Ⅱ with multiplicative noise is obtained by using the state estimation theory of 1-D systems. An example is given to illustrate the validity of this algorithm.
Directory of Open Access Journals (Sweden)
M. Komperød
2011-01-01
Full Text Available The Czochralski (CZ crystallization process is used to produce monocrystalline silicon for solar cell wafers and electronics. Tight temperature control of the molten silicon is most important for achieving high crystal quality. SINTEF Materials and Chemistry operates a CZ process. During one CZ batch, two pyrometers were used for temperature measurement. The silicon pyrometer measures the temperature of the molten silicon. This pyrometer is assumed to be accurate, but has much high-frequency measurement noise. The graphite pyrometer measures the temperature of a graphite material. This pyrometer has little measurement noise. There is quite a good correlation between the two pyrometer measurements. This paper presents a sensor fusion algorithm that merges the two pyrometer signals for producing a temperature estimate with little measurement noise, while having significantly less phase lag than traditional lowpass- filtering of the silicon pyrometer. The algorithm consists of two sub-algorithms: (i A dynamic model is used to estimate the silicon temperature based on the graphite pyrometer, and (ii a lowpass filter and a highpass filter designed as complementary filters. The complementary filters are used to lowpass-filter the silicon pyrometer, highpass-filter the dynamic model output, and merge these filtered signals. Hence, the lowpass filter attenuates noise from the silicon pyrometer, while the graphite pyrometer and the dynamic model estimate those frequency components of the silicon temperature that are lost when lowpass-filtering the silicon pyrometer. The algorithm works well within a limited temperature range. To handle a larger temperature range, more research must be done to understand the process' nonlinear dynamics, and build this into the dynamic model.
Evaluation of the OSC-TV iterative reconstruction algorithm for cone-beam optical CT
Energy Technology Data Exchange (ETDEWEB)
Matenine, Dmitri, E-mail: dmitri.matenine.1@ulaval.ca; Mascolo-Fortin, Julia, E-mail: julia.mascolo-fortin.1@ulaval.ca [Département de physique, de génie physique et d’optique, Université Laval, Québec, Québec G1V 0A6 (Canada); Goussard, Yves, E-mail: yves.goussard@polymtl.ca [Département de génie électrique/Institut de génie biomédical, École Polytechnique de Montréal, C.P. 6079, succ. Centre-ville, Montréal, Québec H3C 3A7 (Canada); Després, Philippe, E-mail: philippe.despres@phy.ulaval.ca [Département de physique, de génie physique et d’optique and Centre de recherche sur le cancer, Université Laval, Québec, Québec G1V 0A6, Canada and Département de radio-oncologie and Centre de recherche du CHU de Québec, Québec, Québec G1R 2J6 (Canada)
2015-11-15
Purpose: The present work evaluates an iterative reconstruction approach, namely, the ordered subsets convex (OSC) algorithm with regularization via total variation (TV) minimization in the field of cone-beam optical computed tomography (optical CT). One of the uses of optical CT is gel-based 3D dosimetry for radiation therapy, where it is employed to map dose distributions in radiosensitive gels. Model-based iterative reconstruction may improve optical CT image quality and contribute to a wider use of optical CT in clinical gel dosimetry. Methods: This algorithm was evaluated using experimental data acquired by a cone-beam optical CT system, as well as complementary numerical simulations. A fast GPU implementation of OSC-TV was used to achieve reconstruction times comparable to those of conventional filtered backprojection. Images obtained via OSC-TV were compared with the corresponding filtered backprojections. Spatial resolution and uniformity phantoms were scanned and respective reconstructions were subject to evaluation of the modulation transfer function, image uniformity, and accuracy. The artifacts due to refraction and total signal loss from opaque objects were also studied. Results: The cone-beam optical CT data reconstructions showed that OSC-TV outperforms filtered backprojection in terms of image quality, thanks to a model-based simulation of the photon attenuation process. It was shown to significantly improve the image spatial resolution and reduce image noise. The accuracy of the estimation of linear attenuation coefficients remained similar to that obtained via filtered backprojection. Certain image artifacts due to opaque objects were reduced. Nevertheless, the common artifact due to the gel container walls could not be eliminated. Conclusions: The use of iterative reconstruction improves cone-beam optical CT image quality in many ways. The comparisons between OSC-TV and filtered backprojection presented in this paper demonstrate that OSC-TV can
Image Recommendation Algorithm Using Feature-Based Collaborative Filtering
Kim, Deok-Hwan
As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.
Accurately approximating algebraic tomographic reconstruction by filtered backprojection
Pelt, D.M.; Batenburg, K.J.; King, M.; Glick, S.; Mueller, K.
2015-01-01
In computed tomography, algebraic reconstruction methods tend to produce reconstructions with higher quality than analytical methods when presented with limited and noisy projection data. The high computational requirements of algebraic methods, however, limit their usefulness in
Accurately approximating algebraic tomographic reconstruction by filtered backprojection
D.M. Pelt (Daniel); K.J. Batenburg (Joost); M. King; S. Glick; K. Mueller
2015-01-01
htmlabstractIn computed tomography, algebraic reconstruction methods tend to produce reconstructions with higher quality than analytical methods when presented with limited and noisy projection data. The high computational requirements of algebraic methods, however, limit
Efficient Backprojection-Based Synthetic Aperture Radar Computation with Many-Core Processors
Directory of Open Access Journals (Sweden)
Jongsoo Park
2013-01-01
Full Text Available Tackling computationally challenging problems with high efficiency often requires the combination of algorithmic innovation, advanced architecture, and thorough exploitation of parallelism. We demonstrate this synergy through synthetic aperture radar (SAR via backprojection, an image reconstruction method that can require hundreds of TFLOPS. Computation cost is significantly reduced by our new algorithm of approximate strength reduction; data movement cost is economized by software locality optimizations facilitated by advanced architecture support; parallelism is fully harnessed in various patterns and granularities. We deliver over 35 billion backprojections per second throughput per compute node on an Intel® Xeon® processor E5-2670-based cluster, equipped with Intel® Xeon Phi™ coprocessors. This corresponds to processing a 3K×3K image within a second using a single node. Our study can be extended to other settings: backprojection is applicable elsewhere including medical imaging, approximate strength reduction is a general code transformation technique, and many-core processors are emerging as a solution to energy-efficient computing.
Stochastic error whitening algorithm for linear filter estimation with noisy data.
Rao, Yadunandana N; Erdogmus, Deniz; Rao, Geetha Y; Principe, Jose C
2003-01-01
Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additive white disturbances. We will briefly motivate the theory behind the new criterion and derive an online stochastic gradient algorithm. Convergence proof of the stochastic gradient algorithm is derived making mild assumptions. Further, we will propose some extensions to the stochastic gradient algorithm to ensure faster, step-size independent convergence. We will perform extensive simulations and compare the results with MSE as well as total-least squares in a parameter estimation problem. The stochastic EWC algorithm has many potential applications. We will use this in designing robust inverse controllers with noisy data.
A Simple, Fast, Filter-Based Algorithm for Approximate Circular Pattern Matching.
Azim, Md Aashikur Rahman; Iliopoulos, Costas S; Rahman, M Sohel; Samiruzzaman, M
2016-03-01
This paper deals with the approximate version of the circular pattern matching (ACPM) problem, which appears as an interesting problem in many biological contexts. The circular pattern matching problem consists in finding all occurrences of the rotations of a pattern P of length m in a text T of length n. In ACPM, we consider occurrences with k -mismatches under the Hamming distance model. In this paper, we present a simple and fast filter-based algorithm to solve the ACPM problem. We compare our algorithm with the state of the art algorithms and the results are found to be excellent. In particular, our algorithm runs almost twice as fast than the state of the art. Much of the efficiency of our algorithm can be attributed to its filters that are effective but extremely simple and lightweight.
Application of the Trend Filtering Algorithm for Photometric Time Series Data
Gopalan, Giri; van Eyken, Julian; Ciardi, David; von Braun, Kaspar; Kane, Stephen R
2016-01-01
Detecting transient light curves (e.g., transiting planets) requires high precision data, and thus it is important to effectively filter systematic trends affecting ground based wide field surveys. We apply an implementation of the Trend Filtering Algorithm (TFA) (Kovacs et al. 2005) to the 2MASS calibration catalog and select Palomar Transient Factory (PTF) photometric time series data. TFA is successful at reducing the overall dispersion of light curves, however it may over filter intrinsic variables and increase "instantaneous" dispersion when a template set is not judiciously chosen. In an attempt to rectify these issues we modify the original literature TFA by including measurement uncertainties in its computation, including ancillary data correlated with noise, and algorithmically selecting a template set using clustering algorithms as suggested by various authors. This approach may be particularly useful for appropriately accounting for variable photometric precision surveys and/or combined data-sets. ...
Directory of Open Access Journals (Sweden)
M. Mohammadi
2015-01-01
Full Text Available This paper presents the optimal planning of harmonic passive filters in distribution system using three intelligent methods including genetic algorithm (GA, particle swarm optimization (PSO, artificial bee colony (ABC and as a new research is compared with biogeography based optimization (BBO algorithm. In this work, the considered objective function is to minimize the value of investment cost of filters and total harmonic distortion of three-phase current. It is shown that through an economical placement and sizing of LC passive filters the total voltage harmonic distortion and cost could be minimized simultaneously. BBO is a novel evolutionary algorithm that is based on the mathematics of biogeography. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. The simulation results show that the proposed method is efficient for solving the presented problem.
Gaussian Sum PHD Filtering Algorithm for Nonlinear Non-Gaussian Models
Institute of Scientific and Technical Information of China (English)
Yin Jianjun; Zhang Jianqiu; Zhuang Zesen
2008-01-01
A new multi-target filtering algorithm, termed as the Gaussian sum probability hypothesis density (GSPHD) filter, is proposed for nonlinear non-Gaussian tracking models. Provided that the initial prior intensity of the states is Gaussian or can be identified as a Gaussiaa sum, the analytical results of the algorithm show that the posterior intensity at any subsequent time step remains a Gaussian sum under the assumption that the state noise, the measurement noise, target spawn intensity, new target birth intensity, target survival probability, and detection probability are all Gaussian sums. The analysis also shows that the existing Gaassian mixture probability hypothesis density (GMPHD) filter, which is unsuitable for handling the non-Gaussian noise cases, is no more than a special ease of the proposed algorithm, which fills the shortage of incapability of treating non-Gaussian noise. The multi-target tracking simulation results verify the effectiveness of the proposed GSPHD.
A hand tracking algorithm with particle filter and improved GVF snake model
Sun, Yi-qi; Wu, Ai-guo; Dong, Na; Shao, Yi-zhe
2017-07-01
To solve the problem that the accurate information of hand cannot be obtained by particle filter, a hand tracking algorithm based on particle filter combined with skin-color adaptive gradient vector flow (GVF) snake model is proposed. Adaptive GVF and skin color adaptive external guidance force are introduced to the traditional GVF snake model, guiding the curve to quickly converge to the deep concave region of hand contour and obtaining the complex hand contour accurately. This algorithm realizes a real-time correction of the particle filter parameters, avoiding the particle drift phenomenon. Experimental results show that the proposed algorithm can reduce the root mean square error of the hand tracking by 53%, and improve the accuracy of hand tracking in the case of complex and moving background, even with a large range of occlusion.
Institute of Scientific and Technical Information of China (English)
L(U) Wei-cai; XU Shao-quan
2004-01-01
Using similar single-difference methodology(SSDM) to solve the deformation values of the monitoring points, there is unstability of the deformation information series, at sometimes.In order to overcome this shortcoming, Kalman filtering algorithm for this series is established,and its correctness and validity are verified with the test data obtained on the movable platform in plane. The results show that Kalman filtering can improve the correctness, reliability and stability of the deformation information series.
2011-01-01
Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows ...
Directory of Open Access Journals (Sweden)
Zhenyang Hui
2016-01-01
Full Text Available Filtering is one of the core post-processing steps for airborne LiDAR point cloud. In recent years, the morphology-based filtering algorithms have proven to be a powerful and efficient tool for filtering airborne LiDAR point cloud. However, most traditional morphology-based algorithms have difficulties in preserving abrupt terrain features, especially when using larger filtering windows. In order to suppress the omission error caused by protruding terrain features, this paper proposes an improved morphological algorithm based on multi-level kriging interpolation. This algorithm is essentially a combination of progressive morphological filtering algorithm and multi-level interpolation filtering algorithm. The morphological opening operation is performed with filtering window gradually downsizing, while kriging interpolation is conducted at different levels according to the different filtering windows. This process is iterative in a top to down fashion until the filtering window is no longer greater than the preset minimum filtering window. Fifteen samples provided by the ISPRS commission were chosen to test the performance of the proposed algorithm. Experimental results show that the proposed method can achieve promising results not only in flat urban areas but also in rural areas. Comparing with other eight classical filtering methods, the proposed method obtained the lowest omission error, and preserved protruding terrain features better.
An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis
Directory of Open Access Journals (Sweden)
Qihang Wang
2016-07-01
Full Text Available In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling procedure, which adjusts the position of the particles through simulating attraction–repulsion mechanism between charged particles of the electromagnetism theory. The improved particle filter can solve the particle degradation problem and ensure the diversity of the particle set. Meanwhile, it enhances the ability of tracking abrupt fault due to considering the latest measurement information. Comparison of the proposed method with three different filter algorithms is carried out on a univariate nonstationary growth model. Simulations on a turbofan engine model indicate that compared to the normal particle filter, the improved particle filter can ensure the completion of the fault diagnosis within less sampling period and the root mean square error of parameters estimation is reduced.
Improving the Prediction Accuracy of Multicriteria Collaborative Filtering by Combination Algorithms
Directory of Open Access Journals (Sweden)
Wiranto
2014-05-01
Full Text Available This study focuses on developing the multicriteria collaborative filtering algorithmfor improving the prediction accuracy. The approaches applied were user-item multirating matrix decomposition, the measurement of user similarity using cosine formula and multidimensional distance, individual criteria weight calculation, and rating prediction for the overall criteria by a combination approach. Results of the study show variation in multicriteria collaborative filtering algorithm, which was used for improving the document recommender system with the two following characteristics. First, the rating prediction for four individual criteria using collaborative filtering algorithm by a cosine-based user similarity and a multidimensional distance-based user similarity. Second, the rating prediction for the overall criteria using a combination algorithms. Based on the results of testing, it can be concluded that a variety of models developed for the multicriteria collaborative filtering systems had much better prediction accuracy than for the classic collaborative filtering, which was characterized by the increasingly smaller values of Mean Absolute Error. The best accuracy was achieved by the multicriteria collaborative filtering system with multidimensional distance-based similarity.
Zhu, Wu; Fang, Jian-an; Tang, Yang; Zhang, Wenbing; Du, Wei
2012-01-01
Design of a digital infinite-impulse-response (IIR) filter is the process of synthesizing and implementing a recursive filter network so that a set of prescribed excitations results a set of desired responses. However, the error surface of IIR filters is usually non-linear and multi-modal. In order to find the global minimum indeed, an improved differential evolution (DE) is proposed for digital IIR filter design in this paper. The suggested algorithm is a kind of DE variants with a controllable probabilistic (CPDE) population size. It considers the convergence speed and the computational cost simultaneously by nonperiodic partial increasing or declining individuals according to fitness diversities. In addition, we discuss as well some important aspects for IIR filter design, such as the cost function value, the influence of (noise) perturbations, the convergence rate and successful percentage, the parameter measurement, etc. As to the simulation result, it shows that the presented algorithm is viable and comparable. Compared with six existing State-of-the-Art algorithms-based digital IIR filter design methods obtained by numerical experiments, CPDE is relatively more promising and competitive.
New hybrid genetic particle swarm optimization algorithm to design multi-zone binary filter.
Lin, Jie; Zhao, Hongyang; Ma, Yuan; Tan, Jiubin; Jin, Peng
2016-05-16
The binary phase filters have been used to achieve an optical needle with small lateral size. Designing a binary phase filter is still a scientific challenge in such fields. In this paper, a hybrid genetic particle swarm optimization (HGPSO) algorithm is proposed to design the binary phase filter. The HGPSO algorithm includes self-adaptive parameters, recombination and mutation operations that originated from the genetic algorithm. Based on the benchmark test, the HGPSO algorithm has achieved global optimization and fast convergence. In an easy-to-perform optimizing procedure, the iteration number of HGPSO is decreased to about a quarter of the original particle swarm optimization process. A multi-zone binary phase filter is designed by using the HGPSO. The long depth of focus and high resolution are achieved simultaneously, where the depth of focus and focal spot transverse size are 6.05λ and 0.41λ, respectively. Therefore, the proposed HGPSO can be applied to the optimization of filter with multiple parameters.
Directory of Open Access Journals (Sweden)
V. Elamaran
2012-12-01
Full Text Available In this study, we present Embedded Zerotree Wavelet (EZW algorithm to compress the image using different wavelet filters such as Biorthogonal, Coiflets, Daubechies, Symlets and Reverse Biorthogonal and to remove noise by setting appropriate threshold value while decoding. Compression methods are important in telemedicine applications by reducing number of bits per pixel to adequately represent the image. Data storage requirements are reduced and transmission efficiency is improved because of compressing the image. The EZW algorithm is an effective and computationally efficient technique in image coding. Obtaining the best image quality for a given bit rate and accomplishing this task in an embedded fashion are the two problems addressed by the EZW algorithm. A technique to decompose the image using wavelets has gained a great deal of popularity in recent years. Apart from very good compression performance, EZW algorithm has the property that the bitstream can be truncated at any point and still be decoded with a good quality image. All the standard wavelet filters are used and the results are compared with different thresholds in the encoding section. Bit rate versus PSNR simulation results are obtained for the image 256x256 barbara with different wavelet filters. It shows that the computational overhead involved with Daubechies wavelet filters but are produced better results. Like even missing details i.e., higher frequency components are picked by them which are missed by other family of wavelet filters.
A nonlinear filtering algorithm for denoising HR(S)TEM micrographs
Energy Technology Data Exchange (ETDEWEB)
Du, Hongchu, E-mail: h.du@fz-juelich.de [Ernst Ruska-Centre for Microscopy and Spectroscopy with Electrons, Jülich Research Centre, Jülich, 52425 (Germany); Central Facility for Electron Microscopy (GFE), RWTH Aachen University, Aachen 52074 (Germany); Peter Grünberg Institute, Jülich Research Centre, Jülich 52425 (Germany)
2015-04-15
Noise reduction of micrographs is often an essential task in high resolution (scanning) transmission electron microscopy (HR(S)TEM) either for a higher visual quality or for a more accurate quantification. Since HR(S)TEM studies are often aimed at resolving periodic atomistic columns and their non-periodic deviation at defects, it is important to develop a noise reduction algorithm that can simultaneously handle both periodic and non-periodic features properly. In this work, a nonlinear filtering algorithm is developed based on widely used techniques of low-pass filter and Wiener filter, which can efficiently reduce noise without noticeable artifacts even in HR(S)TEM micrographs with contrast of variation of background and defects. The developed nonlinear filtering algorithm is particularly suitable for quantitative electron microscopy, and is also of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM. - Highlights: • A nonlinear filtering algorithm for denoising HR(S)TEM images is developed. • It can simultaneously handle both periodic and non-periodic features properly. • It is particularly suitable for quantitative electron microscopy. • It is of great interest for beam sensitive samples, in situ analyses, and atomic resolution EFTEM.
Design of the annular binary filters with super-resolution based on the genetic algorithm
Institute of Scientific and Technical Information of China (English)
YU Qi-lei; LE Zi-chun; ZHU Hong-ying
2006-01-01
To improve the density of information storage,this paper introduces a kind of annular binary filters with super-resolution,Several of these filters have been designed based on the genetic algorithm,the simulations demonstrate that the transverse gain of the filters can reach the value of 1.37.Thus they can remarkably decrease the recording spot size,which is helpful to improve the density of information storage and to make the depth of focus longer,and therefore they can avoid the mistake caused by the small undulation of the optical disk in the process of recording/reading the information.
A novel gradient adaptive step size LMS algorithm with dual adaptive filters.
Jiao, Yuzhong; Cheung, Rex Y P; Chow, Winnie W Y; Mok, Mark P C
2013-01-01
Least mean square (LMS) adaptive filter has been used to extract life signals from serious ambient noises and interferences in biomedical applications. However, a LMS adaptive filter with a fixed step size always suffers from slow convergence rate or large signal distortion due to the diversity of the application environments. An ideal adaptive filtering system should be able to adapt different environments and obtain the useful signals with low distortion. Adaptive filter with gradient adaptive step size is therefore more desirable in order to meet the demands of adaptation and convergence rate, which adjusts the step-size parameter automatically by using gradient descent technique. In this paper, a novel gradient adaptive step size LMS adaptive filter is presented. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately, thus achieves good adaptation and performance. Though it uses two LMS adaptive filters, it has a low computational complexity. An active noise cancellation (ANC) system with two applications for extracting heartbeat and lung sound signals from noises is used to simulate the performance of the proposed algorithm.
Digital Signal Processing Filtering Algorithm : Audio Equalization Using Matlab
Chaguaro Aldaz, Daniel
2015-01-01
The contemporary domain of Digital Signal Processing is in constant influx and trying to find new applications that will benefit the everyday life of ordinary people. In modern technology, most of the electronic processes use DSP algorithms in order to collect analogue information that is continually present all around us and convert it into a digital form. The need of understanding the basics of how these processes occur, has inspired to implement a DSP application for educational and testin...
Ren, Hongwu; Dekany, Richard; Britton, Matthew
2005-05-01
We propose a new recursive filtering algorithm for wave-front reconstruction in a large-scale adaptive optics system. An embedding step is used in this recursive filtering algorithm to permit fast methods to be used for wave-front reconstruction on an annular aperture. This embedding step can be used alone with a direct residual error updating procedure or used with the preconditioned conjugate-gradient method as a preconditioning step. We derive the Hudgin and Fried filters for spectral-domain filtering, using the eigenvalue decomposition method. Using Monte Carlo simulations, we compare the performance of discrete Fourier transform domain filtering, discrete cosine transform domain filtering, multigrid, and alternative-direction-implicit methods in the embedding step of the recursive filtering algorithm. We also simulate the performance of this recursive filtering in a closed-loop adaptive optics system.
1981-07-01
1p^^i-J\\\\^3^\\\\^. TECHNICAL LIBRARY AD^y^.q ijg. TECHNICAL REPORT ARBRL-TR-02346 COMPUTER ALGORITHMS FOR THE DESIGN AND IMPLEMENTATION OF LINEAR...INSTRUCTIONS BEFORE COMPLETI?>G FORM 1. REPORT NUMBER TECHNICAL REPORT ARBRL-TR-n2.^46 i. GOVT ACCESSION NO. *. TITLE fand Sijfam;»; COMPUTER ... ALGORITHMS FOR THE DESIGN AND IMPLEMENTATION OF LINEAR PHASE FINPTE IMPULSE RESPONSE DIGITAL FILTERS 7. AUTHORf*; James N. Walbert 9
An improved filter-u least mean square vibration control algorithm for aircraft framework.
Huang, Quanzhen; Luo, Jun; Gao, Zhiyuan; Zhu, Xiaojin; Li, Hengyu
2014-09-01
Active vibration control of aerospace vehicle structures is very a hot spot and in which filter-u least mean square (FULMS) algorithm is one of the key methods. But for practical reasons and technical limitations, vibration reference signal extraction is always a difficult problem for FULMS algorithm. To solve the vibration reference signal extraction problem, an improved FULMS vibration control algorithm is proposed in this paper. Reference signal is constructed based on the controller structure and the data in the algorithm process, using a vibration response residual signal extracted directly from the vibration structure. To test the proposed algorithm, an aircraft frame model is built and an experimental platform is constructed. The simulation and experimental results show that the proposed algorithm is more practical with a good vibration suppression performance.
An improved filter-u least mean square vibration control algorithm for aircraft framework
Huang, Quanzhen; Luo, Jun; Gao, Zhiyuan; Zhu, Xiaojin; Li, Hengyu
2014-09-01
Active vibration control of aerospace vehicle structures is very a hot spot and in which filter-u least mean square (FULMS) algorithm is one of the key methods. But for practical reasons and technical limitations, vibration reference signal extraction is always a difficult problem for FULMS algorithm. To solve the vibration reference signal extraction problem, an improved FULMS vibration control algorithm is proposed in this paper. Reference signal is constructed based on the controller structure and the data in the algorithm process, using a vibration response residual signal extracted directly from the vibration structure. To test the proposed algorithm, an aircraft frame model is built and an experimental platform is constructed. The simulation and experimental results show that the proposed algorithm is more practical with a good vibration suppression performance.
An Approximate Cone Beam Reconstruction Algorithm for Gantry-Tilted CT Using Tangential Filtering
Directory of Open Access Journals (Sweden)
Ming Yan
2006-01-01
Full Text Available FDK algorithm is a well-known 3D (three-dimensional approximate algorithm for CT (computed tomography image reconstruction and is also known to suffer from considerable artifacts when the scanning cone angle is large. Recently, it has been improved by performing the ramp filtering along the tangential direction of the X-ray source helix for dealing with the large cone angle problem. In this paper, we present an FDK-type approximate reconstruction algorithm for gantry-tilted CT imaging. The proposed method improves the image reconstruction by filtering the projection data along a proper direction which is determined by CT parameters and gantry-tilted angle. As a result, the proposed algorithm for gantry-tilted CT reconstruction can provide more scanning flexibilities in clinical CT scanning and is efficient in computation. The performance of the proposed algorithm is evaluated with turbell clock phantom and thorax phantom and compared with FDK algorithm and a popular 2D (two-dimensional approximate algorithm. The results show that the proposed algorithm can achieve better image quality for gantry-tilted CT image reconstruction.
Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Gravitational Search Algorithm
Directory of Open Access Journals (Sweden)
D. S. Sidhu
2015-01-01
Full Text Available In the recent years, the digital IIR filter design as a single objective optimization problem using evolutionary algorithms has gained much attention. In this paper, the digital IIR filter design is treated as a multiobjective problem by minimizing the magnitude response error, linear phase response error and optimal order simultaneously along with meeting the stability criterion. Hybrid gravitational search algorithm (HGSA has been applied to design the digital IIR filter. GSA technique is hybridized with binary successive approximation (BSA based evolutionary search method for exploring the search space locally. The relative performance of GSA and hybrid GSA has been evaluated by applying these techniques to standard mathematical test functions. The above proposed hybrid search techniques have been applied effectively to solve the multiparameter and multiobjective optimization problem of low-pass (LP, high-pass (HP, band-pass (BP, and band-stop (BS digital IIR filter design. The obtained results reveal that the proposed technique performs better than other algorithms applied by other researchers for the design of digital IIR filter with conflicting objectives.
Novel algorithm by low complexity filter on retinal vessel segmentation
Rostampour, Samad
2011-10-01
This article shows a new method to detect blood vessels in the retina by digital images. Retinal vessel segmentation is important for detection of side effect of diabetic disease, because diabetes can form new capillaries which are very brittle. The research has been done in two phases: preprocessing and processing. Preprocessing phase consists to apply a new filter that produces a suitable output. It shows vessels in dark color on white background and make a good difference between vessels and background. The complexity is very low and extra images are eliminated. The second phase is processing and used the method is called Bayesian. It is a built-in in supervision classification method. This method uses of mean and variance of intensity of pixels for calculate of probability. Finally Pixels of image are divided into two classes: vessels and background. Used images are related to the DRIVE database. After performing this operation, the calculation gives 95 percent of efficiency average. The method also was performed from an external sample DRIVE database which has retinopathy, and perfect result was obtained
Optimal design study of high order FIR digital filters based on neural network algorithm
Institute of Scientific and Technical Information of China (English)
王小华; 何怡刚
2004-01-01
An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved,and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass,bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely . The presented optimal design approach of high order FIR digital filter is significantly effective.
有效的图像滤波算法%Effective noise image filter algorithm
Institute of Scientific and Technical Information of China (English)
黄春艳; 张云鹏
2012-01-01
Owing to the characteristics of the gray relation analysis and the advantage of the alpha-trimmed mean filter, an efficient algorithm for noisy images removal based on the gray relational analysis and the alpha-trimmed mean filter is proposed. This algorithm uses the gray relation analysis to adjust the filter window's coefficients adap-tively, and it can improve the validity of the algorithms. Experimental results show that the proposed algorithm not only has better filtering effect for noisy image which corrupted by Gaussian noise or mixed noise, but also can preserve the integrity of edge and keep the details of the original image.%利用灰色关联度的特性和阿尔法均值滤波算法的优点,提出一种基于改进灰色关联度和阿尔法Alpha 均值滤波的噪声图像的自适应滤波算法.该算法采用灰色关联度自适应地确定滤波窗口的加权系数值,改善算法的滤波性能.实验结果表明算法对受到高斯噪声和混合噪声干扰的图像进行去噪能取得较好的滤波效果,同时还保护了原始图像的细节信息.
A parallel implementation of the dual-input Max-Tree algorithm for attribute filtering
Ouzounis, Georgios K.; Wilkinson, Michael H.F.
2007-01-01
This paper presents a concurrent implementation of a previously developed Dual-Input Max-Tree algorithm that implements anti-extensive attribute filters based on second-generation connectivity. The paralellization strategy has been recently introduced for ordinary Max-Trees and involves the concurre
Directory of Open Access Journals (Sweden)
Shaoxing Hu
2015-11-01
Full Text Available Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted “useful” data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.
Combination of Kalman Filtering Algorithm%一种组合式的Kalman滤波算法
Institute of Scientific and Technical Information of China (English)
余翔; 冯璐; 漆晶
2013-01-01
Because the noise impact and process signals in Kalman filtering can't be directly observed,a kind of combination of Kalman filtering algorithm is proposed.Firstly,the observation data is adaptively weighted fused.Secondly,the fusion results as a priori estimated value of the second step Kalman filtering is filtered.The adaptive algorithm combined with the Kalman algorithm improves the accuracy and precision.Finally,simulations confirme the effectiveness of the algorithm.%针对Kalman滤波算法在估计过程中存在噪声影响和过程信号无法直接观测等问题,提出一种组合式的Kalman滤波算法.首先对观测的数据进行自适应加权融合,然后将融合的结果作为第二级Kalman滤波的先验估计值,进行Kalman滤波.通过自适应算法与Kalman算法的组合算法进行数据融合,可以提高融合的准确度和精度.最后通过仿真证实算法的有效性.
A parallel implementation of the dual-input Max-Tree algorithm for attribute filtering
Ouzounis, Georgios K.; Wilkinson, Michael H.F.
2007-01-01
This paper presents a concurrent implementation of a previously developed Dual-Input Max-Tree algorithm that implements anti-extensive attribute filters based on second-generation connectivity. The paralellization strategy has been recently introduced for ordinary Max-Trees and involves the
Hu, Shaoxing; Xu, Shike; Wang, Duhu; Zhang, Aiwu
2015-11-11
Aiming at addressing the problem of high computational cost of the traditional Kalman filter in SINS/GPS, a practical optimization algorithm with offline-derivation and parallel processing methods based on the numerical characteristics of the system is presented in this paper. The algorithm exploits the sparseness and/or symmetry of matrices to simplify the computational procedure. Thus plenty of invalid operations can be avoided by offline derivation using a block matrix technique. For enhanced efficiency, a new parallel computational mechanism is established by subdividing and restructuring calculation processes after analyzing the extracted "useful" data. As a result, the algorithm saves about 90% of the CPU processing time and 66% of the memory usage needed in a classical Kalman filter. Meanwhile, the method as a numerical approach needs no precise-loss transformation/approximation of system modules and the accuracy suffers little in comparison with the filter before computational optimization. Furthermore, since no complicated matrix theories are needed, the algorithm can be easily transplanted into other modified filters as a secondary optimization method to achieve further efficiency.
Spectral measurement with a linear variable filter using a LMS algorithm
Emadi, A.; Grabarnik, S.; Wu, H.; De Graaf, R.F.; Wolffenbuttel, R.F.
2010-01-01
This paper presents spectral measurements using a linear variable optical filter. A LVOF has been developed for operation in the 530 nm–720 nm spectral band and has been fabricated in an IC-compatible process. The LVOF has been mounted on a CMOS camera. A Least Mean Square algorithm has been
Increasing the robustness of a preconditioned filtered-X LMS algorithm
Fraanje, P.R.; Verhaegen, M.; Doelman, N.J.
2004-01-01
This letter presents a robustification of the preconditioned Filtered-X LMS algorithm proposed by Elliott et al.. The method optimizes the average performance for probabilistic uncertainty in the secondary path and relaxes the SPR condition for global convergence. It also prevents large amplificatio
DEFF Research Database (Denmark)
Cappellin, C.; Pivnenko, Sergey; Jørgensen, E.
2013-01-01
This paper focuses on three important features of the 3D reconstruction algorithm of DIATOOL: the identification of array elements improper functioning and failure, the obtainable spatial resolution of the reconstructed fields and currents, and the filtering of undesired radiation and scattering...
Mejia, Yuri H.; Arguello, Henry
2016-05-01
Compressive sensing state-of-the-art proposes random Gaussian and Bernoulli as measurement matrices. Nev- ertheless, often the design of the measurement matrix is subject to physical constraints, and therefore it is frequently not possible that the matrix follows a Gaussian or Bernoulli distribution. Examples of these lim- itations are the structured and sparse matrices of the compressive X-Ray, and compressive spectral imaging systems. A standard algorithm for recovering sparse signals consists in minimizing an objective function that includes a quadratic error term combined with a sparsity-inducing regularization term. This problem can be solved using the iterative algorithms for solving linear inverse problems. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity. However, current algorithms are slow for getting a high quality image reconstruction because they do not exploit the structured and sparsity characteristics of the compressive measurement matrices. This paper proposes the development of a gradient-based algorithm for compressive sensing reconstruction by including a filtering step that yields improved quality using less iterations. This algorithm modifies the iterative solution such that it forces to converge to a filtered version of the residual AT y, where y is the measurement vector and A is the compressive measurement matrix. We show that the algorithm including the filtering step converges faster than the unfiltered version. We design various filters that are motivated by the structure of AT y. Extensive simulation results using various sparse and structured matrices highlight the relative performance gain over the existing iterative process.
Neural Network Algorithm for Designing FIR Filters Utilizing Frequency-Response Masking Technique
Institute of Scientific and Technical Information of China (English)
Xiao-Hua Wang; Yi-Gang He; Tian-Zan Li
2009-01-01
This paper presents a new joint optimization method for the design of sharp linear-phase finite-impulse response (FIR) digital filters which are synthesized by using basic and multistage frequency-response-masking (FRM) techniques. The method is based on a batch back-propagation neural network algorithm with a variable learning rate mode. We propose the following two-step optimization technique in order to reduce the complexity. At the first step, an initial FRM filter is designed by alternately optimizing the subfilters. At the second step, this solution is then used as a start-up solution to further optimization. The further optimization problem is highly nonlinear with respect to the coefficients of all the subfilters. Therefore, it is decomposed into several linear neural network optimization problems. Some examples from the literature are given, and the results show that the proposed algorithm can design better FRM filters than several existing methods.
Multidimensional Systolic Arrays of LMS AlgorithmAdaptive (FIR Digital Filters
Directory of Open Access Journals (Sweden)
Bakir A. R. Al-Hashemy
2009-01-01
Full Text Available A multidimensional systolic arrays realization of LMS algorithm by a method of mapping regular algorithm onto processor array, are designed. They are based on appropriately selected 1-D systolic array filter that depends on the inner product sum systolic implementation. Various arrays may be derived that exhibit a regular arrangement of the cells (processors and local interconnection pattern, which are important for VLSI implementation. It reduces latency time and increases the throughput rate in comparison to classical 1-D systolic arrays. The 3-D multilayered array consists of 2-D layers, which are connected with each other only by edges. Such arrays for LMS-based adaptive (FIR filter may be opposed the fundamental requirements of fast convergence rate in most adaptive filter applications.
Research on Improved Collaborative Filtering Recommendation Algorithm on MapReduce
Directory of Open Access Journals (Sweden)
Dong Jie
2016-01-01
Full Text Available Information overload is one of the most serious problems in big data environment, recommendation systems is a way to effectively mitigate the problem. In order to make use of rich user feedback and social networks information and to further improve the performance of the recommendation system ,This thesis makes a improvement on the user-based collaborative filtering algorithm by normalization method, Meanwhile the algorithm could be run on the MapReduce in the Hadoop platform. The experimental results show that the algorithm on Hadoop platform can effectively improve the accuracy of the data to recommend and computational efficiency, so as to improve the satisfaction of users.
EMMA: An Efficient Massive Mapping Algorithm Using Improved Approximate Mapping Filtering
Institute of Scientific and Technical Information of China (English)
Xin ZHANG; Zhi-Wei CAO; Zhi-Xin LIN; Qing-Kang WANG; Yi-Xue LI
2006-01-01
Efficient massive mapping algorithm (EMMA), an algorithm on efficiently mapping massive cDNAs onto genomic sequences, has recently been developed. The process of mapping massive cDNAs onto genomic sequences has been improved using more approximate mapping filtering based on an enhanced suffix array coupled with a pruned fast hash table, algorithms of block alignment extensions, and k-longest paths. When compared with the classical BLAT software in this field, the computing of EMMA ranges from two to forty-one times faster under similar prediction precisions.
Zielinski, B.; Patorski, K.
2008-12-01
The aim of this paper is to analyze the accuracy of 2D fringe pattern denoising performed by two chosen methods using quasi-1D two-arm spin filter and 2D Discrete Wavelet Transform (DWT) signal decomposition and thresholding. The ultimate aim of this comparison is to estimate which algorithm is better suited for high-accuracy interferometric measurements. In spite of the fact that both algorithms are designed to minimize possible fringe blur and distortion, the evaluation of errors introduced by each algorithm is essential for proper estimation of their performance.
Optimal fractional delay-IIR filter design using cuckoo search algorithm.
Kumar, Manjeet; Rawat, Tarun Kumar
2015-11-01
This paper applied a novel global meta-heuristic optimization algorithm, cuckoo search algorithm (CSA) to determine optimal coefficients of a fractional delay-infinite impulse response (FD-IIR) filter and trying to meet the ideal frequency response characteristics. Since fractional delay-IIR filter design is a multi-modal optimization problem, it cannot be computed efficiently using conventional gradient based optimization techniques. A weighted least square (WLS) based fitness function is used to improve the performance to a great extent. FD-IIR filters of different orders have been designed using the CSA. The simulation results of the proposed CSA based approach have been compared to those of well accepted evolutionary algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the CSA based FD-IIR filter is superior to those obtained by GA and PSO. The simulation and statistical results affirm that the proposed approach using CSA outperforms GA and PSO, not only in the convergence rate but also in optimal performance of the designed FD-IIR filter (i.e., smaller magnitude error, smaller phase error, higher percentage improvement in magnitude and phase error, fast convergence rate). The absolute magnitude and phase error obtained for the designed 5th order FD-IIR filter are as low as 0.0037 and 0.0046, respectively. The percentage improvement in magnitude error for CSA based 5th order FD-IIR design with respect to GA and PSO are 80.93% and 74.83% respectively, and phase error are 76.04% and 71.25%, respectively.
Mass Conservation and Positivity Preservation with Ensemble-type Kalman Filter Algorithms
Janjic, Tijana; McLaughlin, Dennis B.; Cohn, Stephen E.; Verlaan, Martin
2013-01-01
Maintaining conservative physical laws numerically has long been recognized as being important in the development of numerical weather prediction (NWP) models. In the broader context of data assimilation, concerted efforts to maintain conservation laws numerically and to understand the significance of doing so have begun only recently. In order to enforce physically based conservation laws of total mass and positivity in the ensemble Kalman filter, we incorporate constraints to ensure that the filter ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. We show that the analysis steps of ensemble transform Kalman filter (ETKF) algorithm and ensemble Kalman filter algorithm (EnKF) can conserve the mass integral, but do not preserve positivity. Further, if localization is applied or if negative values are simply set to zero, then the total mass is not conserved either. In order to ensure mass conservation, a projection matrix that corrects for localization effects is constructed. In order to maintain both mass conservation and positivity preservation through the analysis step, we construct a data assimilation algorithms based on quadratic programming and ensemble Kalman filtering. Mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate constraints. Some simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. The results show clear improvements in both analyses and forecasts, particularly in the presence of localized features. Behavior of the algorithm is also tested in presence of model error.
iDensity: an automatic Gabor filter-based algorithm for breast density assessment
Gamdonkar, Ziba; Tay, Kevin; Ryder, Will; Brennan, Patrick C.; Mello-Thoms, Claudia
2015-03-01
Abstract Although many semi-automated and automated algorithms for breast density assessment have been recently proposed, none of these have been widely accepted. In this study a novel automated algorithm, named iDensity, inspired by the human visual system is proposed for classifying mammograms into four breast density categories corresponding to the Breast Imaging Reporting and Data System (BI-RADS). For each BI-RADS category 80 cases were taken from the normal volumes of the Digital Database for Screening Mammography (DDSM). For each case only the left medio-lateral oblique was utilized. After image calibration using the provided tables of each scanner in the DDSM, the pectoral muscle and background were removed. Images were filtered by a median filter and down sampled. Images were then filtered by a filter bank consisting of Gabor filters in six orientations and 3 scales, as well as a Gaussian filter. Three gray level histogram-based features and three second order statistics features were extracted from each filtered image. Using the extracted features, mammograms were separated initially separated into two groups, low or high density, then in a second stage, the low density group was subdivided into BI-RADS I or II, and the high density group into BI-RADS III or IV. The algorithm achieved a sensitivity of 95% and specificity of 94% in the first stage, sensitivity of 89% and specificity of 95% when classifying BIRADS I and II cases, and a sensitivity of 88% and 91% specificity when classifying BI-RADS III and IV.
Fraanje, P.R.; Verhaegen, M.; Doelman, N.J.
2003-01-01
The Filtered-U LMS algorithm, proposed by Eriksson for active noise control applications, adapts the coefficients of an infinite-impulse response controller. Conditions for global convergence of the Filtered-U LMS algorithm were presented by Wang and Ren (Signal Processing, 73 (1999) 3) and Mosquera
A Review of the Performance of Artifact Filtering Algorithms for Cardiopulmonary Resuscitation
Directory of Open Access Journals (Sweden)
Yushun Gong
2013-01-01
Full Text Available Various filtering strategies have been adopted and investigated to suppress the cardiopulmonary resuscitation (CPR artifact. In this article, two types of artifact removal methods are reviewed: one is the method that removes CPR artifact using only ECG signals, and the other is the method with additional reference signals, such as acceleration, compression depth and transthoracic impedance. After filtering, the signal-to-noise ratio is improved from 0 dB to greater than 2.8 dB, the sensitivity is increased to > 90% as recommended by the American Heart Association, whereas the specificity was far from the recommended 95%, which is considered to be the major drawback of the available artifact removal methods. The overall performance of the adaptive filtering methods with additional reference signal outperforms the methods using only ECG signals. Further research should focus on the refinement of artifact filtering methods and the improvement of shock advice algorithms with the presence of CPR.
Optimality analysis of one-step OOSM filtering algorithms in target tracking
Institute of Scientific and Technical Information of China (English)
ZHOU WenHui; LI Lin; CHEN GuoHai; YU AnXi
2007-01-01
In centralized multisensor tracking systems, there are out-of-sequence measurements (OOSMs) frequently arising due to different time delays in communication links and varying pre-processing times at the sensor. Such OOSM arrival can induce the "negative-time measurement update" problem, which is quite common in real multisensor tracking systems. The A1 optimal update algorithm with OOSM is presented by Bar-Shalom for one-step case. However, this paper proves that the optimality of A1 algorithm is lost in direct discrete-time model (DDM) of the process noise, it holds true only in discretized continuous-time model (DCM). One better OOSM filtering algorithm for DDM case is presented. Also, another new optimal OOSM filtering algorithm, which is independent of the discrete time model of the process noise, is presented here. The performance of the two new algorithms is compared with that of A1 algorithm by Monte Carlo simulations. The effectiveness and correctness of the two proposed algorithms are validated by analysis and simulation results.
An Image Filter Based on Multiobjective Genetic Algorithm and Shearlet Transformation
Directory of Open Access Journals (Sweden)
Zhi-yong Fan
2013-01-01
Full Text Available Rician noise pollutes magnetic resonance imaging (MRI data, making data’s postprocessing difficult. In order to remove this noise and avoid loss of details as much as possible, we proposed a filter algorithm using both multiobjective genetic algorithm (MOGA and Shearlet transformation. Firstly, the multiscale wavelet decomposition is applied to the target image. Secondly, the MOGA target function is constructed by evaluation methods, such as signal-to-noise ratio (SNR and mean square error (MSE. Thirdly, MOGA is used with optimal coefficients of Shearlet wavelet threshold value in a different scale and a different orientation. Finally, the noise-free image could be obtained through inverse wavelet transform. At the end of the paper, experimental results show that this proposed algorithm eliminates Rician noise more effectively and yields better peak signal-to-noise ratio (PSNR gains compared with other traditional filters.
Collaborative filtering algorithm based on Forgetting Curve and Long Tail theory
Qi, Shen; Li, Shiwei; Zhou, Hao
2017-03-01
The traditional collaborative filtering algorithm only pays attention to the rating by users. In reality, however, user and item information is always changing with time flying. Therefore, recommendation systems need to take time-varying changes into consideration. The collaborative filtering algorithm which is based on Forgetting Curve and Long Tail theory (FCLT) is introduced for the above problems. The following two points are discussed depending on the problem: First, the user-item rating matrix can update in real time by forgetting curve; secondly, according to the Long Tail theory and item popularity, a further similarity calculation method is obtained. The experimental results demonstrated that the proposed algorithm can effectively improve the recommendation accuracy and alleviate the Long Tail effect.
Implementation and evaluation of two helical CT reconstruction algorithms in CIVA
Banjak, H.; Costin, M.; Vienne, C.; Kaftandjian, V.
2016-02-01
The large majority of industrial CT systems reconstruct the 3D volume by using an acquisition on a circular trajec-tory. However, when inspecting long objects which are highly anisotropic, this scanning geometry creates severe artifacts in the reconstruction. For this reason, the use of an advanced CT scanning method like helical data acquisition is an efficient way to address this aspect known as the long-object problem. Recently, several analytically exact and quasi-exact inversion formulas for helical cone-beam reconstruction have been proposed. Among them, we identified two algorithms of interest for our case. These algorithms are exact and of filtered back-projection structure. In this work we implemented the filtered-backprojection (FBP) and backprojection-filtration (BPF) algorithms of Zou and Pan (2004). For performance evaluation, we present a numerical compari-son of the two selected algorithms with the helical FDK algorithm using both complete (noiseless and noisy) and truncated data generated by CIVA (the simulation platform for non-destructive testing techniques developed at CEA).
Directory of Open Access Journals (Sweden)
E. L. Dmitrieva
2016-05-01
Full Text Available Basic peculiarities of nonlinear Kalman filtering algorithm applied to processing of interferometric signals are considered. Analytical estimates determining statistical characteristics of signal values prediction errors were obtained and analysis of errors histograms taking into account variations of different parameters of interferometric signal was carried out. Modeling of the signal prediction procedure with known fixed parameters and variable parameters of signal in the algorithm of nonlinear Kalman filtering was performed. Numerical estimates of prediction errors for interferometric signal values were obtained by formation and analysis of the errors histograms under the influence of additive noise and random variations of amplitude and frequency of interferometric signal. Nonlinear Kalman filter is shown to provide processing of signals with randomly variable parameters, however, it does not take into account directly the linearization error of harmonic function representing interferometric signal that is a filtering error source. The main drawback of the linear prediction consists in non-Gaussian statistics of prediction errors including cases of random deviations of signal amplitude and/or frequency. When implementing stochastic filtering of interferometric signals, it is reasonable to use prediction procedures based on local statistics of a signal and its parameters taken into account.
Directory of Open Access Journals (Sweden)
P.Nirmala
2014-08-01
Full Text Available In this paper, an optimal design of FIR filter is carried out using a “Dynamic Regional Harmony Search algorithm (DRHS with Opposition and Local Learning”. The Harmony Search (HS is a robust optimization algorithm which mimics the musician’s improvisation method and has been used by many researchers for solving and optimizing various real-world optimization problems and numerical solutions. For optimizing the functionality of the FIR filter, DRHS algorithm which is an enhanced variant of the HS algorithm is adopted to avoid pre-mature convergence and stagnation. BY adopting DRHS algorithm the low pass, high pass, band pass and band stop FIR filters are constructed and their performances are evaluated and compared with the other existing optimization techniques. A comparison of the DRHS with other optimization algorithms for constructing FIR filter clearly shows the DRHS finds the optimal solution and the convergence is clearly guaranteed.
Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order
Directory of Open Access Journals (Sweden)
J. G. Avalos
2017-01-01
Full Text Available Affine projection (AP algorithms are commonly used to implement active noise control (ANC systems because they provide fast convergence. However, their high computational complexity can restrict their use in certain practical applications. The Error Coded Affine Projection-Like (ECAP-L algorithm has been proposed to reduce the computational burden while maintaining the speed of AP, but no version of this algorithm has been derived for active noise control, for which the adaptive structures are very different from those of other configurations. In this paper, we introduce a version of the ECAP-L for single-channel and multichannel ANC systems. The proposed algorithm is implemented using the conventional filtered-x scheme, which incurs a lower computational cost than the modified filtered-x structure, especially for multichannel systems. Furthermore, we present an evolutionary method that dynamically decreases the projection order in order to reduce the dimensions of the matrix used in the algorithm’s computations. Experimental results demonstrate that the proposed algorithm yields a convergence speed and a final residual error similar to those of AP algorithms. Moreover, it achieves meaningful computational savings, leading to simpler hardware implementation of real-time ANC applications.
Modification of double vector control algorithm to filter out grid harmonics
DEFF Research Database (Denmark)
Awad, Hilmy; Blaabjerg, Frede
2005-01-01
filter (MAF) to detect the fundamental component of the measured voltages and currents (needed to control the SSC) while using a double vector control algorithm (DVC) to improve the transient performance of the SSC. This is made to accurately control the fundamental voltage component at the load...... terminals in the case of distorted grid voltage. Furthermore, a selective harmonic compensation strategy is applied to filter out the grid harmonics. The operation of the SSC under distorted utility conditions and voltage dips is discussed. The validity of the proposed controller is verified by experiments...
Study of data filtering algorithms for the KM3NeT neutrino telescope
Energy Technology Data Exchange (ETDEWEB)
Herold, B., E-mail: Bjoern.Herold@physik.uni-erlangen.d [Erlangen Centre for Astroparticle Physics, Erwin-Rommel-Str. 1, 91058 Erlangen (Germany); Seitz, T., E-mail: Thomas.Seitz@physik.uni-erlangen.d [Erlangen Centre for Astroparticle Physics, Erwin-Rommel-Str. 1, 91058 Erlangen (Germany); Shanidze, R., E-mail: shanidze@physik.uni-erlangen.d [Erlangen Centre for Astroparticle Physics, Erwin-Rommel-Str. 1, 91058 Erlangen (Germany)
2011-01-21
The photomultiplier signals above a defined threshold (hits) are the main data collected from the KM3NeT neutrino telescope. The neutrino and muon events will be reconstructed from these signals. However, in the deep sea the dominant source of hits are the decays of {sup 40}K isotope and marine fauna bioluminescence. The selection of neutrino and muon events requires the implementation of fast and efficient data filtering algorithms for the reduction of accidental background event rates. A possible data filtering scheme for the KM3NeT neutrino telescope is discussed in the paper.
A Filter-Based Uniform Algorithm for Optimizing Top-k Query in Distributed Networks
Institute of Scientific and Technical Information of China (English)
ZHAO Zhibin; YAO Lan; YANG Xiaochun; LI Binyang; YU Ge
2006-01-01
In this paper we propose a Filter-based Uniform Algorithm (FbUA) for optimizing top-k query in distributed networks, which has been a topic of much recent interest.The basic idea of FbUA is to set a filter at each node to prevent it from sending out the data with little chance to contribute to the top-k result.FbUA can gain exact answers to top-k query through two phrases of round-trip communications between query station and participant nodes.The experiment results show that FbUA reduces network bandwidth consumption dramatically.
3D head pose estimation and tracking using particle filtering and ICP algorithm
Ben Ghorbel, Mahdi
2010-01-01
This paper addresses the issue of 3D head pose estimation and tracking. Existing approaches generally need huge database, training procedure, manual initialization or use face feature extraction manually extracted. We propose a framework for estimating the 3D head pose in its fine level and tracking it continuously across multiple Degrees of Freedom (DOF) based on ICP and particle filtering. We propose to approach the problem, using 3D computational techniques, by aligning a face model to the 3D dense estimation computed by a stereo vision method, and propose a particle filter algorithm to refine and track the posteriori estimate of the position of the face. This work comes with two contributions: the first concerns the alignment part where we propose an extended ICP algorithm using an anisotropic scale transformation. The second contribution concerns the tracking part. We propose the use of the particle filtering algorithm and propose to constrain the search space using ICP algorithm in the propagation step. The results show that the system is able to fit and track the head properly, and keeps accurate the results on new individuals without a manual adaptation or training. © Springer-Verlag Berlin Heidelberg 2010.
Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm.
Cameron, Fraser; Wilson, Darrell M; Buckingham, Bruce A; Arzumanyan, Hasmik; Clinton, Paula; Chase, H Peter; Lum, John; Maahs, David M; Calhoun, Peter M; Bequette, B Wayne
2012-09-01
An insulin pump shutoff system can prevent nocturnal hypoglycemia and is a first step on the pathway toward a closed-loop artificial pancreas. In previous pump shutoff studies using a voting algorithm and a 1 min continuous glucose monitor (CGM), 80% of induced hypoglycemic events were prevented. The pump shutoff algorithm used in previous studies was revised to a single Kalman filter to reduce complexity, incorporate CGMs with different sample times, handle sensor signal dropouts, and enforce safety constraints on the allowable pump shutoff time. Retrospective testing of the new algorithm on previous clinical data sets indicated that, for the four cases where the previous algorithm failed (minimum reference glucose less than 60 mg/dl), the mean suspension start time was 30 min earlier than the previous algorithm. Inpatient studies of the new algorithm have been conducted on 16 subjects. The algorithm prevented hypoglycemia in 73% of subjects. Suspension-induced hyperglycemia is not assessed, because this study forced excessive basal insulin infusion rates. The new algorithm functioned well and is flexible enough to handle variable sensor sample times and sensor dropouts. It also provides a framework for handling sensor signal attenuations, which can be challenging, particularly when they occur overnight. © 2012 Diabetes Technology Society.
Directory of Open Access Journals (Sweden)
Jian Yi
2016-04-01
Full Text Available In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative filtering algorithm (SoMu. We first define the user attraction similarity by users’ historical rated behaviors using graph theory, and secondly, define the user interaction similarity by users’ social friendship which is based on the social relationship of being followed and following. Then, we combine the user attraction similarity and the user interaction similarity to obtain a multi-attribute comprehensive user similarity model. Finally, realize personalized recommendation according to the comprehensive similarity model. Experimental results on Douban and MovieLens show that the proposed algorithm successfully incorporates multiple attributes in social networks to recommendation algorithm, and improves the accuracy of recommender system with the improved comprehensive similarity computing model.
Duplication-remove algorithm of image based on EZW-based matrix bloom filter
Che, Yujing; Fei, Xiangdong; Hu, Bo
2011-10-01
Transmission efficiency is seriously hindered by a huge amount of data which is largely redundant during the image transmission on the network. To solver this problem, a new algorithm is put forward here. It firstly uses EZW coding algorithm to compress, code and transform data and then uses Matrix Bloom filter on account of the characters of EZW to remove the redundant data according to the strictly defined ranks. This new algorithm attains its goal of reducing the data being transmitted on the network and improving the transmission efficiency by making real-time judgment that whether the data should be transmitted again in order to cease redundant data transmission as early as possible. Finally, the effectiveness and practicability of this new algorithm has been demonstrated by the simulation experiments.
Convergence analysis of filtered-X LMS algorithm with secondary path modeling error
Institute of Scientific and Technical Information of China (English)
SUN Xu; CHEN Duanshi
2003-01-01
A more relaxed sufficient condition for the convergence of filtered-X LMS (FXLMS)algorithm is presented. It is pointed out that if some positive real condition for secondary pathtransfer function and its estimates is satisfied within all the frequency bands, FXLMS algorithmconverges whatever the reference signal is like. But if the above positive real condition is satisfiedonly within some frequency bands, the convergence of FXLMS algorithm is dependent on thedistribution of power spectral density of the reference signal, and the convergence step size isdetermined by the distribution of some specific correlation matrix eigenvalues.Applying the conclusion above to the Delayed LMS (DLMS) algorithm, it is shown thatDLMS algorithm with some error of time delay estimation converges in certain discrete fre-quency bands, and the width of which are determined only by the "time-delay estimation errorfrequency" which is equal to one fourth of the inverse of estimated error of the time delay.
The particle filter algorithm of SLAM%粒子滤波的SLAM算法
Institute of Scientific and Technical Information of China (English)
唐羽; 马小平
2011-01-01
In this paper,the mobile robot and simultaneous localization and mapping problem is discussed first in this paper,then discussed the mobile robot in indoor unknown environment of SLAM problem, based on the analysis of the particle filter algorithm, Put forward the particle filter of mobile robot synchronous simultaneous localization and mapping (SLAM) method, And gives the corresponding matlab of particle filter.The particle filter is a kind of more mature filtering technology in foreign countries, and is currently also has a lot of colleges are studying in the home.This paper introduces the principle of the particle filter and research progress of the particle filter, Looking to the future development of particle filter.%本文首先对移动机器人的同时定位与地图创建问题进行了阐述，继而讨论了移动机器人在室内未知环境下的SLAM问题，在分析粒子滤波算法的基础上，提出了以下方法：基于粒子滤波的移动机器人同时定位与地图创建方法，并给出了粒子滤波的相应的matlab实现。粒子滤波在国外已经是一种较成熟的滤波技术，目前在国内也有很多高校正在研究，本文对粒子滤波的原理和研究进展进行了详细的介绍同时对粒子滤波的未来发展进行了展望。
A Correlation Based Strategy for the Acceleration of Nonlocal Means Filtering Algorithm
Directory of Open Access Journals (Sweden)
Junfeng Zhang
2016-01-01
Full Text Available Although the nonlocal means (NLM algorithm takes a significant step forward in image filtering field, it suffers from a high computational complexity. To deal with this drawback, this paper proposes an acceleration strategy based on a correlation operation. Instead of per-pixel processing, this approach performs a simultaneous calculation of all the image pixels with the help of correlation operators. Complexity analysis and experimental results are reported and show the advantage of the proposed algorithm in terms of computation and time cost.
Czaplewski, Raymond L
2015-09-17
Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables. A National Forest Inventory (NFI) illustrates application within an official statistics program. Practical recommendations regarding remote sensing and statistical issues are offered. This algorithm has the potential to increase the value of synoptic sensor data for statistical monitoring of large geographic areas.
Islanding Detection for Microgrid Based on Frequency Tracking Using Extended Kalman Filter Algorithm
Directory of Open Access Journals (Sweden)
Bin Li
2014-01-01
Full Text Available Islanding detection is essential for secure and reliable operation of microgrids. Considering the relationship between the power generation and the load in microgrids, frequency may vary with time when islanding occurs. As a common approach, frequency measurement is widely used to detect islanding condition. In this paper, a novel frequency calculation algorithm based on extended Kalman filter was proposed to track dynamic frequency of the microgrid. Taylor series expansion was introduced to solve nonlinear state equations. In addition, a typical microgrid model was built using MATLAB/SIMULINK. Simulation results demonstrated that the proposed algorithm achieved great stability and strong robustness in of tracking dynamic frequency.
Improved Computing-Efficiency Least-Squares Algorithm with Application to All-Pass Filter Design
Directory of Open Access Journals (Sweden)
Lo-Chyuan Su
2013-01-01
Full Text Available All-pass filter design can be generally achieved by solving a system of linear equations. The associated matrices involved in the set of linear equations can be further formulated as a Toeplitz-plus-Hankel form such that a matrix inversion is avoided. Consequently, the optimal filter coefficients can be solved by using computationally efficient Levinson algorithms or Cholesky decomposition technique. In this paper, based on trigonometric identities and sampling the frequency band of interest uniformly, the authors proposed closed-form expressions to compute the elements of the Toeplitz-plus-Hankel matrix required in the least-squares design of IIR all-pass filters. Simulation results confirm that the proposed method achieves good performance as well as effectiveness.
Wang, Tianyang; Chu, Fulei; Han, Qinkai
2017-03-01
Identifying the differences between the spectra or envelope spectra of a faulty signal and a healthy baseline signal is an efficient planetary gearbox local fault detection strategy. However, causes other than local faults can also generate the characteristic frequency of a ring gear fault; this may further affect the detection of a local fault. To address this issue, a new filtering algorithm based on the meshing resonance phenomenon is proposed. In detail, the raw signal is first decomposed into different frequency bands and levels. Then, a new meshing index and an MRgram are constructed to determine which bands belong to the meshing resonance frequency band. Furthermore, an optimal filter band is selected from this MRgram. Finally, the ring gear fault can be detected according to the envelope spectrum of the band-pass filtering result.
New algorithm for robust H2/H∞ filtering with error variance assignment
Institute of Scientific and Technical Information of China (English)
刘立恒; 邓正隆; 王广雄
2004-01-01
We consider the robust H2/H∞ filtering problem for linear perturbed systems with steady-state error variance assignment. The generalized inverse technique of matrix is introduced, and a new algorithm is developed. After two Riccati equations are solved, the filter can be obtained directly, and the following three performance requirements are simultaneously satisfied: The filtering process is asymptotically stable; the steady-state variance of the estimation error of each state is not more than the individual prespecified upper bound; the transfer function from exogenous noise inputs to error state outputs meets the prespecified H∞ norm upper bound constraint. A numerical example is provided to demonstrate the flexibility of the proposed design approach.
Zero-crossing detection algorithm for arrays of optical spatial filtering velocimetry sensors
DEFF Research Database (Denmark)
Jakobsen, Michael Linde; Pedersen, Finn; Hanson, Steen Grüner
2008-01-01
This paper presents a zero-crossing detection algorithm for arrays of compact low-cost optical sensors based on spatial filtering for measuring fluctuations in angular velocity of rotating solid structures. The algorithm is applicable for signals with moderate signal-to-noise ratios, and delivers...... a "real-time" output (0-1 kHz). The sensors use optical spatial-filtering velocimetry on the dynamical speckles arising from scattering off a rotating solid object with a non-specular surface. The technology measures the instantaneous angular velocity of a target, without being biased by any linear...... factor is directly related to the thermal expansion and refractive-index coefficients of the optics (> 10(-5) K-1 for glass). By cascade-coupling an array of sensors, the ensemble-averaged angular velocity is measured in "real-time". This will reduce the influence of pseudo-vibrations arising from...
Olivares, Alberto; Górriz, J M; Ramírez, J; Olivares, G
2016-05-01
With the advent of miniaturized inertial sensors many systems have been developed within the last decade to study and analyze human motion and posture, specially in the medical field. Data measured by the sensors are usually processed by algorithms based on Kalman Filters in order to estimate the orientation of the body parts under study. These filters traditionally include fixed parameters, such as the process and observation noise variances, whose value has large influence in the overall performance. It has been demonstrated that the optimal value of these parameters differs considerably for different motion intensities. Therefore, in this work, we show that, by applying frequency analysis to determine motion intensity, and varying the formerly fixed parameters accordingly, the overall precision of orientation estimation algorithms can be improved, therefore providing physicians with reliable objective data they can use in their daily practice.
Optimization of Filter by using Support Vector Regression Machine with Cuckoo Search Algorithm
Directory of Open Access Journals (Sweden)
M. İlarslan
2014-09-01
Full Text Available Herein, a new methodology using a 3D Electromagnetic (EM simulator-based Support Vector Regression Machine (SVRM models of base elements is presented for band-pass filter (BPF design. SVRM models of elements, which are as fast as analytical equations and as accurate as a 3D EM simulator, are employed in a simple and efficient Cuckoo Search Algorithm (CSA to optimize an ultra-wideband (UWB microstrip BPF. CSA performance is verified by comparing it with other Meta-Heuristics such as Genetic Algorithm (GA and Particle Swarm Optimization (PSO. As an example of the proposed design methodology, an UWB BPF that operates between the frequencies of 3.1 GHz and 10.6 GHz is designed, fabricated and measured. The simulation and measurement results indicate in conclusion the superior performance of this optimization methodology in terms of improved filter response characteristics like return loss, insertion loss, harmonic suppression and group delay.
Zielinski, B.; Patorski, K.
2010-06-01
The aim of this paper is to analyze 2D fringe pattern denoising performed by two chosen methods based on quasi-1D two-arm spin filter and 2D discrete wavelet transform (DWT) signal decomposition and thresholding. The ultimate aim of this comparison is to estimate which algorithm is better suited for high-accuracy measurements by phase shifting interferometry (PSI) with the phase step evaluation using the lattice site approach. The spin filtering method proposed by Yu et al. (1994) was designed to minimize possible fringe blur and distortion. The 2D DWT also presents such features due to a lossless nature of the signal wavelet decomposition. To compare both methods, a special 2D histogram introduced by Gutman and Weber (1998) is used to evaluate intensity errors introduced by each of the presented algorithms.
Infomax Algorithm for Filtering Airwaves in the Field of Seabed Logging
Directory of Open Access Journals (Sweden)
Adeel Ansari
2014-04-01
Full Text Available This research focuses on applying Independent Component Analysis (ICA in the field of Seabed Logging (SBL. ICA is a statistical method for transforming an observed multidimensional or multivariate dataset into its constituent components (sources that are statistically as independent from each other as possible. ICA-type de-convolution algorithm, Infomax is suitable for mixed signals de-convolution, is proposed and considered convenient depending upon the nature of the source and noise model, in the application of seabed logging. Infomax is applied in the domain of marine Controlled Source Electro Magnetic (CSEM sensing method used for the detection of hydrocarbons based reservoirs in seabed logging application. The task is to identify the air waves and to filter them out. The infomax algorithm of ICA is considered for filtering the airwaves.
Lim, Wei Jer; Neoh, Siew Chin; Norizan, Mohd Natashah; Mohamad, Ili Salwani
2015-05-01
Optimization for complex circuit design often requires large amount of manpower and computational resources. In order to optimize circuit performance, it is critical not only for circuit designers to adjust the component value but also to fulfill objectives such as gain, cutoff frequency, ripple and etc. This paper proposes Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize a ninth order multiple feedback Chebyshev low pass filter. Multi-objective Pareto-Based optimization is involved whereby the research aims to obtain the best trade-off for minimizing the pass-band ripple, maximizing the output gain and achieving the targeted cut-off frequency. The developed NSGA-II algorithm is executed on the NGSPICE circuit simulator to assess the filter performance. Overall results show satisfactory in the achievements of the required design specifications.
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P.O.
2013-01-01
This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically wit...
Adaptive filter design based on the LMS algorithm for delay elimination in TCR/FC compensators.
Hooshmand, Rahmat Allah; Torabian Esfahani, Mahdi
2011-04-01
Thyristor controlled reactor with fixed capacitor (TCR/FC) compensators have the capability of compensating reactive power and improving power quality phenomena. Delay in the response of such compensators degrades their performance. In this paper, a new method based on adaptive filters (AF) is proposed in order to eliminate delay and increase the response of the TCR compensator. The algorithm designed for the adaptive filters is performed based on the least mean square (LMS) algorithm. In this design, instead of fixed capacitors, band-pass LC filters are used. To evaluate the filter, a TCR/FC compensator was used for nonlinear and time varying loads of electric arc furnaces (EAFs). These loads caused occurrence of power quality phenomena in the supplying system, such as voltage fluctuation and flicker, odd and even harmonics and unbalancing in voltage and current. The above design was implemented in a realistic system model of a steel complex. The simulation results show that applying the proposed control in the TCR/FC compensator efficiently eliminated delay in the response and improved the performance of the compensator in the power system.
[Research of adaptive notch filter based on QRD-LS algorithm for power line interference in ECG].
Wang, Shuyan; Dong, Jian; Guan, Xin
2008-10-01
In this paper, an adaptive notch filter based on QRD-LS algorithm for power line interference in ECG is researched. It can automatically eliminate the power line interference in order to improve the signal-to-interference ratio. Furthermore, QLD-LS algorithm, which is recursive least-squares minimization using systolic arrays, is employed to adjust the weight vector. Compared with the adaptive notch filter based on LMS (least mean square) algorithm, it has good robustness. Simulation examples confirm the results. QRD-LS adaptive notch filter has better performance in comparison with LMS method.
Application of the Trend Filtering Algorithm for Photometric Time Series Data
Gopalan, Giri; Plavchan, Peter; van Eyken, Julian; Ciardi, David; von Braun, Kaspar; Kane, Stephen R.
2016-08-01
Detecting transient light curves (e.g., transiting planets) requires high-precision data, and thus it is important to effectively filter systematic trends affecting ground-based wide-field surveys. We apply an implementation of the Trend Filtering Algorithm (TFA) to the 2MASS calibration catalog and select Palomar Transient Factory (PTF) photometric time series data. TFA is successful at reducing the overall dispersion of light curves, however, it may over-filter intrinsic variables and increase “instantaneous” dispersion when a template set is not judiciously chosen. In an attempt to rectify these issues we modify the original TFA from the literature by including measurement uncertainties in its computation, including ancillary data correlated with noise, and algorithmically selecting a template set using clustering algorithms as suggested by various authors. This approach may be particularly useful for appropriately accounting for variable photometric precision surveys and/or combined data sets. In summary, our contributions are to provide a MATLAB software implementation of TFA and a number of modifications tested on synthetics and real data, summarize the performance of TFA and various modifications on real ground-based data sets (2MASS and PTF), and assess the efficacy of TFA and modifications using synthetic light curve tests consisting of transiting and sinusoidal variables. While the transiting variables test indicates that these modifications confer no advantage to transit detection, the sinusoidal variables test indicates potential improvements in detection accuracy.
Near-lossless compression algorithm for Bayer pattern color filter arrays
Bazhyna, Andriy; Gotchev, Atanas; Egiazarian, Karen
2005-02-01
In this contribution, we propose a near-lossless compression algorithm for Color Filter Arrays (CFA) images. It allows higher compression ratio than any strictly lossless algorithm for the price of some small and controllable error. In our approach a structural transformation is applied first in order to pack the pixels of the same color in a structure appropriate for the subsequent compression algorithm. The transformed data is compressed by a modified version of the JPEG-LS algorithm. A nonlinear and adaptive error quantization function is embedded in the JPEG-LS algorithm after the fixed and context adaptive predictors. It is step-like and adapts to the base signal level in such a manner that higher error values are allowed for lighter parts with no visual quality loss. These higher error values are then suppressed by gamma correction applied during the image reconstruction stage. The algorithm can be adjusted for arbitrary pixel resolution, gamma value and allowable error range. The compression performance of the proposed algorithm has been tested for real CFA raw data. The results are presented in terms of compression ratio versus reconstruction error and the visual quality of the reconstructed images is demonstrated as well.
Ensemble-Type Kalman Filter Algorithm conserving mass, total energy and enstrophy
Zeng, Yuefei; Janjic, Tijana; Ruckstuhl, Yvonne; Verlaan, Martin
2017-04-01
In a recent study (Zeng and Janjic 2016), we explored the effect on conservation properties of data assimilation using perfect model experiments with a 2D shallow water model preserving important properties of the true nonlinear flow. It was found that during the assimilation with the ensemble Kalman filter algorithm, the total energy of the analysis ensemble mean converges towards the nature run value with time. However, the enstrophy, divergence and energy spectra were strongly affected by the data assimilation settings. We tested the effects on the prediction depending on the type of error in the initial condition and showed that the accumulated noise during assimilation and the error of analysis are good indicators of the quality of the prediction. Having in mind that the conservation of both the kinetic energy and enstrophy by momentum advection schemes in the case of non-divergent flow prevents a systematic and unrealistic energy cascade towards the high wave numbers, we constructed the ensemble data assimilation algorithm that conserves both energy and enstrophy. This is done by extending QPEns (Janjic et al. 2014) to allow for nonlinear constraints using, instead of quadratic programming, the sequential quadratic programming algorithm. Experiments with the 2D shallow water model show similar RMSEs of the algorithm without constraints and the algorithm with only the total energy constrained. The algorithm which constraints enstrophy as well as energy and enstrophy during data assimilation showed smaller RMSE to the one without the constraint on enstrophy. Similar behavior can be seen in the energy spectrum where algorithms which include the constraint on enstrophy are closer to the true spectrum, in particular for wavelengths between 200 km and 1000 km. The enstrophy constraint resulted in a reduction of noise during data assimilation. Finally, the algorithm, with both energy and enstrophy constraint showed the smallest error growth during the two weeks
Conservation of Mass and Preservation of Positivity with Ensemble-Type Kalman Filter Algorithms
Janjic, Tijana; Mclaughlin, Dennis; Cohn, Stephen E.; Verlaan, Martin
2014-01-01
This paper considers the incorporation of constraints to enforce physically based conservation laws in the ensemble Kalman filter. In particular, constraints are used to ensure that the ensemble members and the ensemble mean conserve mass and remain nonnegative through measurement updates. In certain situations filtering algorithms such as the ensemble Kalman filter (EnKF) and ensemble transform Kalman filter (ETKF) yield updated ensembles that conserve mass but are negative, even though the actual states must be nonnegative. In such situations if negative values are set to zero, or a log transform is introduced, the total mass will not be conserved. In this study, mass and positivity are both preserved by formulating the filter update as a set of quadratic programming problems that incorporate non-negativity constraints. Simple numerical experiments indicate that this approach can have a significant positive impact on the posterior ensemble distribution, giving results that are more physically plausible both for individual ensemble members and for the ensemble mean. In two examples, an update that includes a non-negativity constraint is able to properly describe the transport of a sharp feature (e.g., a triangle or cone). A number of implementation questions still need to be addressed, particularly the need to develop a computationally efficient quadratic programming update for large ensemble.
The mathematics of some tomography algorithms used at JET
Energy Technology Data Exchange (ETDEWEB)
Ingesson, L
2000-03-01
Mathematical details are given of various tomographic reconstruction algorithms that are in use at JET. These algorithms include constrained optimization (CO) with local basis functions, the Cormack method, methods with natural basis functions and the iterative projection-space reconstruction method. Topics discussed include: derivation of the matrix equation for constrained optimization, variable grid size, basis functions, line integrals, derivative matrices, smoothness matrices, analytical expression of the CO solution, sparse matrix storage, projection-space coordinates, the Cormack method in elliptical coordinates, interpolative generalized natural basis functions and some details of the implementation of the filtered backprojection method. (author)
Low-cost attitude determination system using an extended Kalman filter (EKF) algorithm
Esteves, Fernando M.; Nehmetallah, Georges; Abot, Jandro L.
2016-05-01
Attitude determination is one of the most important subsystems in spacecraft, satellite, or scientific balloon mission s, since it can be combined with actuators to provide rate stabilization and pointing accuracy for payloads. In this paper, a low-cost attitude determination system with a precision in the order of arc-seconds that uses low-cost commercial sensors is presented including a set of uncorrelated MEMS gyroscopes, two clinometers, and a magnetometer in a hierarchical manner. The faster and less precise sensors are updated by the slower, but more precise ones through an Extended Kalman Filter (EKF)-based data fusion algorithm. A revision of the EKF algorithm fundamentals and its implementation to the current application, are presented along with an analysis of sensors noise. Finally, the results from the data fusion algorithm implementation are discussed in detail.
An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm
Directory of Open Access Journals (Sweden)
Kai Hu
2015-01-01
Full Text Available Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR.
An optimal algorithm based on extended kalman filter and the data fusion for infrared touch overlay
Zhou, AiGuo; Cheng, ShuYi; Pan, Qiang Biao; Sun, Dong Yu
2016-01-01
Current infrared touch overlay has problems on the touch point recognition which bring some burrs on the touch trajectory. This paper uses the target tracking algorithm to improve the recognition and smoothness of infrared touch overlay. In order to deal with the nonlinear state estimate problem for touch point tracking, we use the extended Kalman filter in the target tracking algorithm. And we also use the data fusion algorithm to match the estimate value with the original target trajectory. The experimental results of the infrared touch overlay demonstrate that the proposed target tracking approach can improve the touch point recognition of the infrared touch overlay and achieve much smoother tracking trajectory than the existing tracking approach.
Novel Algorithm for Active Noise Control Systems Based on Frequency Selective Filters
Institute of Scientific and Technical Information of China (English)
Hong-liang ZHAO
2010-01-01
A novel algorithm for active noise control systems based on frequency selective filters (FSFANC)is presented in the paper.The FSFANC aims at the m lti-tonal noise attenuation problem.One FSFANC system copes with one of the tonal components,and several FSFANC systems can nun independently in parallel to cancel the selected multiple tones.The proposed algorithm adopts a simple structrue with only two coefficients that can be explained as the real and imaginary parts of the structure to modelthesecondary path,and estimates the secondary path by injecting sinusoidal identification signals.Theoretical analysis and laboratory experiments show that the proposed algorithm possesses some advantages,such as simpler stricture,less computational burden,greater stability,and fast canverging speed.
Directory of Open Access Journals (Sweden)
Chonghuan Xu
2013-01-01
Full Text Available With the rapid development of customer relationship management, more and more user recommendation technologies are used to enhance the customer satisfaction. Although there are many good recommendation algorithms, it is still a challenge to increase the accuracy and diversity of these algorithms to fulfill users’ preferences. In this paper, we construct a user recommendation model containing a new method to compute the similarities among users on bipartite networks. Different from other standard similarities, we consider the influence of each object node including popular degree, preference degree, and trust relationship. Substituting these new definitions of similarity for the standard cosine similarity, we propose a modified collaborative filtering algorithm based on multifactors (CF-M. Detailed experimental analysis on two benchmark datasets shows that the CF-M is of high accuracy and also generates more diversity.
SimpLiFiCPM: A Simple and Lightweight Filter-Based Algorithm for Circular Pattern Matching.
Azim, Md Aashikur Rahman; Iliopoulos, Costas S; Rahman, M Sohel; Samiruzzaman, M
2015-01-01
This paper deals with the circular pattern matching (CPM) problem, which appears as an interesting problem in many biological contexts. CPM consists in finding all occurrences of the rotations of a pattern of length m in a text of length n. In this paper, we present SimpLiFiCPM (pronounced "Simplify CPM"), a simple and lightweight filter-based algorithm to solve the problem. We compare our algorithm with the state-of-the-art algorithms and the results are found to be excellent. Much of the speed of our algorithm comes from the fact that our filters are effective but extremely simple and lightweight.
Peña, M.
2016-10-01
Achieving acceptable signal-to-noise ratio (SNR) can be difficult when working in sparsely populated waters and/or when species have low scattering such as fluid filled animals. The increasing use of higher frequencies and the study of deeper depths in fisheries acoustics, as well as the use of commercial vessels, is raising the need to employ good denoising algorithms. The use of a lower Sv threshold to remove noise or unwanted targets is not suitable in many cases and increases the relative background noise component in the echogram, demanding more effectiveness from denoising algorithms. The Adaptive Wiener Filter (AWF) denoising algorithm is presented in this study. The technique is based on the AWF commonly used in digital photography and video enhancement. The algorithm firstly increments the quality of the data with a variance-dependent smoothing, before estimating the noise level as the envelope of the Sv minima. The AWF denoising algorithm outperforms existing algorithms in the presence of gaussian, speckle and salt & pepper noise, although impulse noise needs to be previously removed. Cleaned echograms present homogenous echotraces with outlined edges.
Harmonic regression based multi-temporal cloud filtering algorithm for Landsat 8
Joshi, P.
2015-12-01
Landsat data archive though rich is seen to have missing dates and periods owing to the weather irregularities and inconsistent coverage. The satellite images are further subject to cloud cover effects resulting in erroneous analysis and observations of ground features. In earlier studies the change detection algorithm using statistical control charts on harmonic residuals of multi-temporal Landsat 5 data have been shown to detect few prominent remnant clouds [Brooks, Evan B., et al, 2014]. So, in this work we build on this harmonic regression approach to detect and filter clouds using a multi-temporal series of Landsat 8 images. Firstly, we compute the harmonic coefficients using the fitting models on annual training data. This time series of residuals is further subjected to Shewhart X-bar control charts which signal the deviations of cloud points from the fitted multi-temporal fourier curve. For the process with standard deviation σ we found the second and third order harmonic regression with a x-bar chart control limit [Lσ] ranging between [0.5σ HOT), and utilizing the seasonal physical properties of these parameters, we have designed a novel multi-temporal algorithm for filtering clouds from Landsat 8 images. The method is applied to Virginia and Alabama in Landsat8 UTM zones 17 and 16 respectively. Our algorithm efficiently filters all types of cloud cover with an overall accuracy greater than 90%. As a result of the multi-temporal operation and the ability to recreate the multi-temporal database of images using only the coefficients of the fourier regression, our algorithm is largely storage and time efficient. The results show a good potential for this multi-temporal approach for cloud detection as a timely and targeted solution for the Landsat 8 research community, catering to the need for innovative processing solutions in the infant stage of the satellite.
Genetic Algorithm-Based Design of the Active Damping for an LCL-Filter Three-Phase Active Rectifier
DEFF Research Database (Denmark)
Liserre, Marco; Aquila, Antonio Dell; Blaabjerg, Frede
2004-01-01
of this filter is easily done, for a wide range of sampling frequencies, with the use of genetic algorithms. This method is used only for the optimum choice of the parameters in the filter, and an on-line implementation is not needed. Thus the resulting active damping solution does not need new sensors...
Quaternion-Based Kalman Filter for AHRS Using an Adaptive-Step Gradient Descent Algorithm
Directory of Open Access Journals (Sweden)
Li Wang
2015-09-01
Full Text Available This paper presents a quaternion-based Kalman filter for real-time estimation of the orientation of a quadrotor. Quaternions are used to represent rotation relationship between navigation frame and body frame. Processing of a 3-axis accelerometer using Adaptive-Step Gradient Descent (ASGD produces a computed quaternion input to the Kalman filter. The step-size in GD is set in direct proportion to the physical orientation rate. Kalman filter combines 3-axis gyroscope and computed quaternion to determine pitch and roll angles. This combination overcomes linearization error of the measurement equations and reduces the calculation cost. 3-axis magnetometer is separated from ASGD to independently calculate yaw angle for Attitude Heading Reference System (AHRS. This AHRS algorithm is able to remove the magnetic distortion impact. Experiments are carried out in the small-size flight controller and the real world flying test shows the proposed AHRS algorithm is adequate for the real-time estimation of the orientation of a quadrotor.
A Novel Image Segmentation Algorithm Based on Neutrosophic Filtering and Level Set
Directory of Open Access Journals (Sweden)
Yanhui Guo
2016-03-01
Full Text Available Image segmentation is an important step in image processing and analysis, pattern recognition, and machine vision. A few of algorithms based on level set have been proposed for image segmentation in the last twenty years. However, these methods are time consuming, and sometime fail to extract the correct regions especially for noisy images. Recently, neutrosophic set (NS theory has been applied to image processing for noisy images with indeterminant information. In this paper, a novel image segmentation approach is proposed based on the filter in NS and level set theory. At first, the image is transformed into NS domain, which is described by three membership sets (T, I and F. Then, a filter is newly defined and employed to reduce the indeterminacy of the image. Finally, a level set algorithm is used in the image after filtering operation for image segmentation. Experiments have been conducted using different images. The results demonstrate that the proposed method can segment the images effectively and accurately. It is especially able to remove the noise effect and extract the correct regions on both the noise-free images and the images with different levels of noise.
Analysis of Naïve Bayes Algorithm for Email Spam Filtering across Multiple Datasets
Fitriah Rusland, Nurul; Wahid, Norfaradilla; Kasim, Shahreen; Hafit, Hanayanti
2017-08-01
E-mail spam continues to become a problem on the Internet. Spammed e-mail may contain many copies of the same message, commercial advertisement or other irrelevant posts like pornographic content. In previous research, different filtering techniques are used to detect these e-mails such as using Random Forest, Naïve Bayesian, Support Vector Machine (SVM) and Neutral Network. In this research, we test Naïve Bayes algorithm for e-mail spam filtering on two datasets and test its performance, i.e., Spam Data and SPAMBASE datasets [8]. The performance of the datasets is evaluated based on their accuracy, recall, precision and F-measure. Our research use WEKA tool for the evaluation of Naïve Bayes algorithm for e-mail spam filtering on both datasets. The result shows that the type of email and the number of instances of the dataset has an influence towards the performance of Naïve Bayes.
Ge, Shuang-Chao; Deng, Ming; Chen, Kai; Li, Bin; Li, Yuan
2016-12-01
Time-domain induced polarization (TDIP) measurement is seriously affected by power line interference and other field noise. Moreover, existing TDIP instruments generally output only the apparent chargeability, without providing complete secondary field information. To increase the robustness of TDIP method against interference and obtain more detailed secondary field information, an improved dataprocessing algorithm is proposed here. This method includes an efficient digital notch filter which can effectively eliminate all the main components of the power line interference. Hardware model of this filter was constructed and Vhsic Hardware Description Language code for it was generated using Digital Signal Processor Builder. In addition, a time-location method was proposed to extract secondary field information in case of unexpected data loss or failure of the synchronous technologies. Finally, the validity and accuracy of the method and the notch filter were verified by using the Cole-Cole model implemented by SIMULINK software. Moreover, indoor and field tests confirmed the application effect of the algorithm in the fieldwork.
Adaptive system noise covariance for performance enhancement of Kalman filter-based algorithms
Lee, Vika; Chan, Keith C. C.; Leung, Henry
1996-06-01
Several designs of Kalman filters and the interacting multiple models algorithm were used in real tracking tasks involving high dynamic targets. The data were obtained through the joint effort of the defense departments of Canada and the US. Their performance, measured in terms of positional deviation and the number of track losses, are rather unsatisfactory even though they perform particularly well when using simulated data. To identify the reasons behind, we compared and analyzed the differences between the model assumptions behind the design of these Kalman filters and the model required for accurate tracking of these targets. In this paper, we discussed our findings. Moreover, based on the characteristics of real tracking data, we present an alternative methodology for measuring the effectiveness of various Kalman filter based trackers in stressful environmental. It can also be used to explain the well known characteristics of Kalman filter. A lower bound for the deviation, obtained from this equation, shows that deviation could be too large to manage if noise bandwidth is as high as the real data instead of a pre-assumed magnitude. Instead of having to redesign many existing Kalman filters to suit for stressful environment, we developed a design-independent module that can be added to different types of Kalman filters based trackers to enhance their performance in the tracking high dynamic targets. The module is called adaptive systems noise covariance estimation. It is not only safe (i.e. almost no negative effect) but it can sometimes even double the performance of trackers in stressful environment.
Liu, Qianshun; Liu, Yan; Yu, Feihong
2013-08-01
As a kind of film device, band-pass filter is widely used in pattern recognition, infrared detection, optical fiber communication, etc. In this paper, an algorithm for automatic measurement of band-pass filter quality criterion is proposed based on the proven theory calculation of derivate spectral transmittance of filter formula. Firstly, wavelet transform to reduce spectrum data noises is used. Secondly, combining with the Gaussian curve fitting and least squares method, the algorithm fits spectrum curve and searches the peak. Finally, some parameters for judging band-pass filter quality are figure out. Based on the algorithm, a pipeline for band-pass filters automatic measurement system has been designed that can scan the filter array automatically and display spectral transmittance of each filter. At the same time, the system compares the measuring result with the user defined standards to determine if the filter is qualified or not. The qualified product will be market with green color, and the unqualified product will be marked with red color. With the experiments verification, the automatic measurement system basically realized comprehensive, accurate and rapid measurement of band-pass filter quality and achieved the expected results.
A robust SEM auto-focus algorithm using multiple band-pass filters
Harada, Minoru; Obara, Kenji; Nakamae, Koji
2017-01-01
An auto-focus algorithm using multiple band-pass filters for a scanning electron microscope (SEM) is proposed. To acquire sharp images of various kinds of defects by SEM defect observation in semiconductor manufacturing, the auto-focus process must be robust. A method for designing a band-pass filter for calculating the ‘focus measure’ (a key parameter of the auto-focus process) is proposed. To achieve an optimal specific frequency response for various images, multiple band-pass filters are introduced. As for the proposed method, two series of focus measures are calculated by using multiple band-pass filters independently, and it is selected according to reliability of the series of focus measures. The signal-to-noise ratio of an image for acceptable auto-focus precision is determined by simulation using pseudo images. In an experiment using the proposed method with real images, the success rate of auto focus is improved from 79.4% to 95.6%.
An Efficient Data Fingerprint Query Algorithm Based on Two-Leveled Bloom Filter
Directory of Open Access Journals (Sweden)
Bin Zhou
2013-04-01
Full Text Available The function of the comparing fingerprints algorithm was to judge whether a new partitioned data chunk was in a storage system a decade ago. At present, in the most de-duplication backup system the fingerprints of the big data chunks are huge and cannot be stored in the memory completely. The performance of the system is unavoidably retarded by data chunks accessing the storage system at the querying stage. Accordingly, a new query mechanism namely Two-stage Bloom Filter (TBF mechanism is proposed. Firstly, as a representation of the entirety for the first grade bloom filter, each bit of the second grade bloom filter in the TBF represents the chunks having the identical fingerprints reducing the rate of false positives. Secondly, a two-dimensional list is built corresponding to the two grade bloom filter for the absolute addresses of the data chunks with the identical fingerprints. Finally, a new hash function class with the strong global random characteristic is set up according to the data fingerprints’ random characteristics. To reduce the comparing data greatly, TBF decreases the number of accessing disks, improves the speed of detecting the redundant data chunks, and reduces the rate of false positives which helps the improvement of the overall performance of system.
A Fault-Tolerant Filtering Algorithm for SINS/DVL/MCP Integrated Navigation System
Directory of Open Access Journals (Sweden)
Xiaosu Xu
2015-01-01
Full Text Available The Kalman filter (KF, which recursively generates a relatively optimal estimate of underlying system state based upon a series of observed measurements, has been widely used in integrated navigation system. Due to its dependence on the accuracy of system model and reliability of observation data, the precision of KF will degrade or even diverge, when using inaccurate model or trustless data set. In this paper, a fault-tolerant adaptive Kalman filter (FTAKF algorithm for the integrated navigation system composed of a strapdown inertial navigation system (SINS, a Doppler velocity log (DVL, and a magnetic compass (MCP is proposed. The evolutionary artificial neural networks (EANN are used in self-learning and training of the intelligent data fusion algorithm. The proposed algorithm can significantly outperform the traditional KF in providing estimation continuously with higher accuracy and smoothing the KF outputs when observation data are inaccurate or unavailable for a short period. The experiments of the prototype verify the effectiveness of the proposed method.
Directory of Open Access Journals (Sweden)
Wentao Yu
2013-01-01
high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.
Research on the filtering algorithm in speed and position detection of maglev trains.
Dai, Chunhui; Long, Zhiqiang; Xie, Yunde; Xue, Song
2011-01-01
This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS) train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train's structure, the permanent magnet electrodynamic suspension (EDS) train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD) and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.
Research on the Filtering Algorithm in Speed and Position Detection of Maglev Trains
Directory of Open Access Journals (Sweden)
Chunhui Dai
2011-07-01
Full Text Available This paper introduces in brief the traction system of a permanent magnet electrodynamic suspension (EDS train. The synchronous traction mode based on long stators and track cable is described. A speed and position detection system is recommended. It is installed on board and is used as the feedback end. Restricted by the maglev train’s structure, the permanent magnet electrodynamic suspension (EDS train uses the non-contact method to detect its position. Because of the shake and the track joints, the position signal sent by the position sensor is always aberrant and noisy. To solve this problem, a linear discrete track-differentiator filtering algorithm is proposed. The filtering characters of the track-differentiator (TD and track-differentiator group are analyzed. The four series of TD are used in the signal processing unit. The result shows that the track-differentiator could have a good effect and make the traction system run normally.
Katyal, Vini
2012-01-01
This paper focuses on fruit defect detection and glare removal using morphological operations, Glare removal can be considered as an important preprocessing step as uneven lighting may introduce it in images, which hamper the results produced through segmentation by Gabor filters .The problem of glare in images is very pronounced sometimes due to the unusual reflectance from the camera sensor or stray light entering, this method counteracts this problem and makes the defect detection much more pronounced. Anisotropic diffusion is used for further smoothening of the images and removing the high energy regions in an image for better defect detection and makes the defects more retrievable. Our algorithm is robust and scalable the employability of a particular mask for glare removal has been checked and proved useful for counteracting.this problem, anisotropic diffusion further enhances the defects with its use further Optimal Gabor filter at various orientations is used for defect detection.
Optimal Filtering Algorithm-Based Multiuser Detector for Fast Fading CDMA Systems
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A multiuser detector was developed for fast fading code-division multiple-access systems by representing the channels as a system with the multiplicative noise (SMN) model and then using the known optimal filtering algorithm for the SMN for multiuser detection (MUD). This multiuser detector allows the channel response to be stochastic in one symbol duration, which can be regarded as an effective method of MUD for fast fading CDMA systems. Performance analyses show that the multiuser detector is theoretically valid for CDMA systems over fast fading channels. Simulations show that the multiuser detector performs better than the Kalman filter-based multiuser detector with a faster convergence rate and lower bit error rate.
Filter Bank Common Spatial Pattern algorithm on BCI Competition IV Datasets 2a and 2b
Directory of Open Access Journals (Sweden)
Kai Keng eAng
2012-03-01
Full Text Available The Common Spatial Pattern (CSP algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC, Pair-Wise (PW, and One-Versus-Rest (OVR approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR algorithm. The single-trial classification accuracies were presented using 10x10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b.
Ang, Kai Keng; Chin, Zheng Yang; Wang, Chuanchu; Guan, Cuntai; Zhang, Haihong
2012-01-01
The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
Kim, Ho-Wuk; Park, Hong-Sug; Lee, Sang-Kwon; Shin, Kihong
2011-01-01
This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Sharifzadeh, Sara
2013-01-01
In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over s...... the hierarchical clustering method (Fuzzy C-means algorithm). The experimental results show that the proposed pixon-based approach has a reduced computational load and a better accuracy compared to the other existing pixon-based image segmentation techniques.......In this paper, a new pixon-based method is presented for image segmentation. In the proposed algorithm, bilateral filtering is used as a kernel function to form a pixonal image. Using this filter reduces the noise and smoothes the image slightly. By using this pixon-based method, the image over...
Directory of Open Access Journals (Sweden)
Apoorva Aggarwal
2015-12-01
Full Text Available In this paper, an optimal design of linear phase digital finite impulse response (FIR highpass (HP filter using the L1-norm based real-coded genetic algorithm (RCGA is investigated. A novel fitness function based on L1 norm is adopted to enhance the design accuracy. Optimized filter coefficients are obtained by defining the filter objective function in L1 sense using RCGA. Simulation analysis unveils that the performance of the RCGA adopting this fitness function is better in terms of signal attenuation ability of the filter, flatter passband and the convergence rate. Observations are made on the percentage improvement of this algorithm over the gradient-based L1 optimization approach on various factors by a large amount. It is concluded that RCGA leads to the best solution under specified parameters for the FIR filter design on account of slight unnoticeable higher transition width.
Karimi, Davood; Ward, Rabab
2016-08-01
Forward- and back-projection operations are the main computational burden in iterative image reconstruction in computed tomography. In addition, their implementation has to be accurate to ensure stable convergence to a high-quality image. This paper reviews and compares some of the variations in the implementation of these operations in cone-beam computed tomography. We compare four algorithms for computing the system matrix, including a distance-driven algorithm, an algorithm based on cubic basis functions, another based on spherically symmetric basis functions, and a voxel-driven algorithm. The focus of our study is on understanding how the choice of the implementation of the system matrix will influence the performance of iterative image reconstruction algorithms, including such factors as the noise strength and spatial resolution in the reconstructed image. Our experiments with simulated and real cone-beam data reveal the significance of the speed-accuracy trade-off in the implementation of the system matrix. Our results suggest that fast convergence of iterative image reconstruction methods requires accurate implementation of forward- and back-projection operations, involving a direct estimation of the convolution of the footprint of the voxel basis function with the surface of the detectors. The required accuracy decreases by increasing the resolution of the projection measurements beyond the resolution of the reconstructed image. Moreover, reconstruction of low-contrast objects needs more accurate implementation of these operations. Our results also show that, compared with regularized reconstruction methods, the behavior of iterative reconstruction algorithms that do not use a proper regularization is influenced more significantly by the implementation of the forward- and back-projection operations.
Artificial Fish Swarm Algorithm-Based Particle Filter for Li-Ion Battery Life Prediction
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Ye Tian
2014-01-01
Full Text Available An intelligent online prognostic approach is proposed for predicting the remaining useful life (RUL of lithium-ion (Li-ion batteries based on artificial fish swarm algorithm (AFSA and particle filter (PF, which is an integrated approach combining model-based method with data-driven method. The parameters, used in the empirical model which is based on the capacity fade trends of Li-ion batteries, are identified dependent on the tracking ability of PF. AFSA-PF aims to improve the performance of the basic PF. By driving the prior particles to the domain with high likelihood, AFSA-PF allows global optimization, prevents particle degeneracy, thereby improving particle distribution and increasing prediction accuracy and algorithm convergence. Data provided by NASA are used to verify this approach and compare it with basic PF and regularized PF. AFSA-PF is shown to be more accurate and precise.
Analysis of Process Mining Model Using Frequentgroup Based Noise Filtering Algorithm
Directory of Open Access Journals (Sweden)
V. Priyadharshini
2014-02-01
Full Text Available Process mining is a process management system used to analyze business processes based on event logs. The knowledge is extracted from event logs by using knowledge retrieval techniques. The process mining algorithms are capable of automatically discover models to give details of all the events registered in some log traces provided as input. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets from a set of traces. The main objective of this paper is to propose new concept Frequentgroup based noise filtering algorithm. The experiment is done based on standard bench mark dataset HELIX and RALIC datasets. The performance of the proposed system is better than existing method. Keywords:
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, Ehsan; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm...
Xie, XianMing; Li, YingHui
2014-06-20
This paper presents an enhanced phase unwrapping algorithm by combining an unscented Kalman filter, an enhanced local phase gradient estimator based on an amended matrix pencil model, and a path-following strategy. This technology is able to accurately unwrap seriously noisy wrapped phase images by applying the unscented Kalman filter to simultaneously perform noise suppression and phase unwrapping along the path from the high-quality region to the low-quality region of the wrapped phase images. Results obtained with synthetic data and real data validate the effectiveness of the proposed method and show improved performance of this new algorithm with respect to some of the most used algorithms.
H-- Filtering Algorithms Case Study GPS-Based Satellite Orbit Determination
Kuang, Jinlu; Tan, Soonhie
In this paper the new Hfiltering algorithms for the design of navigation systems for autonomous LEO satellite is introduced. The nominal orbit (i.e., position and velocity) is computed by integrating the classical orbital differential equations of the LEO satellite by using the 7th-8th order Runge- Kutta algorithms. The perturbations due to the atmospheric drag force, the lunar-solar attraction and the solar radiation pressure are included together with the Earth gravity model (EGM-96). The spherical harmonic coefficients of the EGM-96 are considered up to 72 for the order and degree. By way of the MATLAB GPSoft software, the simulated pseudo ranges between the user LEO satellite and the visible GPS satellites are generated when given the appropriate angle of mask. The effects of the thermal noises, tropospheric refraction, ionospheric refraction, and multipath of the antenna are also compensated numerically in the simulated pseudo ranges. The dynamic Position-Velocity (PV) model is obtained by modeling the velocity as nearly constant being the white noise process. To further accommodate acceleration in the process model, the Position-Velocity-Acceleration (PVA) model is investigated by assuming the acceleration to be the Gaussian- Markov process. The state vector for the PV model becomes 8-dimensional (3-states for positions, 3-states for velocities, 1-state for range (clock) bias error, 1-state for range (clock) drift error). The state vector for the PV model becomes 11-dimensional with the addition of three more acceleration states. Three filtering approaches are used to smooth the orbit solution based upon the GPS pseudo range observables. The numerical simulation shows that the observed orbit root-mean-square errors of 60 meters by using the least squares adjustment method are improved to be less than 5 meters within 16 hours of tracking time by using the Hfiltering algorithms. The results are compared with the ones obtained by using the Extended Kalman
MRI Mammogram Image Segmentation using NCut method and Genetic Algorithm with partial filters
Directory of Open Access Journals (Sweden)
Pitchumani Angayarkanni
2011-01-01
Full Text Available Cancer is one of the most common leading deadly diseases which affect men and women around the world. Among the cancer diseases, breast cancer is especially a concern in women. It has become a major health problem in developed and developing countries over the past 50 years and the incidence has increased in recent years. Recent trends in digital image processing are CAD systems, which are computerized tools designed to assist radiologists. Most of these systems are used for automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of breast increases. In this paper , the proposed algorithm uses partial filters to enhance the images and the Ncut method is applied to segment the malignant and benign regions , further genetic algorithm is applied to identify the nipple position followed by bilateral subtraction of the left and the right breast image to cluster the cancerous and non cancerous regions. The system is trained using Back Propagation Neural Network algorithm. Computational efficiency and accuracy of the proposed system are evaluated based on the Frequency Receiver Operating Characteristic curve(FROC. The algorithm are tested on 161 pairs of digitized mammograms from MIAS database. The Receiver Operating Characteristic curve leads to 99.987% accuracy in detection of cancerous masses.
Simplified inverse filter tracking algorithm for estimating the mean trabecular bone spacing.
Huang, Kai; Ta, Dean; Wang, Weiqi; Le, L H
2008-07-01
Ultrasonic backscatter signals provide useful information relevant to bone tissue characterization. Trabecular bone microstructures have been considered as quasi-periodic tissues with a collection of regular and diffuse scatterers. This paper investigates the potential of a novel technique using a simplified inverse filter tracking (SIFT) algorithm to estimate mean trabecular bone spacing (MTBS) from ultrasonic backscatter signals. In contrast to other frequency-based methods, the SIFT algorithm is a time-based method and utilizes the amplitude and phase information of backscatter echoes, thus retaining the advantages of both the autocorrelation and the cepstral analysis techniques. The SIFT algorithm was applied to backscatter signals from simulations, phantoms, and bovine trabeculae in vitro. The estimated MTBS results were compared with those of the autoregressive (AR) cepstrum and quadratic transformation (QT) . The SIFT estimates are better than the AR cepstrum estimates and are comparable with the QT values. The study demonstrates that the SIFT algorithm has the potential to be a reliable and robust method for the estimation of MTBS in the presence of a small signal-to-noise ratio, a large spacing variation between regular scatterers, and a large scattering strength ratio of diffuse scatterers to regular ones.
SEGMENTATION OF CT SCAN LUMBAR SPINE IMAGE USING MEDIAN FILTER AND CANNY EDGE DETECTION ALGORITHM
Directory of Open Access Journals (Sweden)
E.Punarselvam
2013-09-01
Full Text Available The lumbar vertebrae are the largest segments of the movable part of the vertebral column, they are elected L1 to L5, starting at the top. The spinal column, more commonly called the backbone, is made up primarily of vertebrae discs, and the spinal cord. Acting as a communication conduit for the brain, signals are transmitted and received through the spinal cord. It is otherwise known as vertebralcolumn consists of 24 separate bony vertebrae together with 5 fused vertebrae, it is the unique interaction between the solid and fluid components that provides the disc strength and flexibility required to bear loading of the lumbar spine. In this work the Segmentation of Spine Image using Median Filter and Canny Edge Detection Algorithm between lumbar spine CT scan spine disc image. The result shows thatthe canny edge detection algorithm produced better result when compared other edge detection algorithm. Finding the correct boundary in a noisy image of spine disc is still a difficult one. To find outabsolute edges from noisy images, the comparative result can be verified and validated with the standard medical values. The result shows that the canny edge detection algorithm performs well and produced a solution very nearer to the optimal solution. This method is vigorous for all kinds of noisy images.
A fast image super-resolution algorithm using an adaptive Wiener filter.
Hardie, Russell
2007-12-01
A computationally simple super-resolution algorithm using a type of adaptive Wiener filter is proposed. The algorithm produces an improved resolution image from a sequence of low-resolution (LR) video frames with overlapping field of view. The algorithm uses subpixel registration to position each LR pixel value on a common spatial grid that is referenced to the average position of the input frames. The positions of the LR pixels are not quantized to a finite grid as with some previous techniques. The output high-resolution (HR) pixels are obtained using a weighted sum of LR pixels in a local moving window. Using a statistical model, the weights for each HR pixel are designed to minimize the mean squared error and they depend on the relative positions of the surrounding LR pixels. Thus, these weights adapt spatially and temporally to changing distributions of LR pixels due to varying motion. Both a global and spatially varying statistical model are considered here. Since the weights adapt with distribution of LR pixels, it is quite robust and will not become unstable when an unfavorable distribution of LR pixels is observed. For translational motion, the algorithm has a low computational complexity and may be readily suitable for real-time and/or near real-time processing applications. With other motion models, the computational complexity goes up significantly. However, regardless of the motion model, the algorithm lends itself to parallel implementation. The efficacy of the proposed algorithm is demonstrated here in a number of experimental results using simulated and real video sequences. A computational analysis is also presented.
Cervantes-Sanchez, Fernando; Hernandez-Aguirre, Arturo; Solorio-Meza, Sergio; Ornelas-Rodriguez, Manuel; Torres-Cisneros, Miguel
2016-01-01
This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA) in X-ray angiograms. Since the single-scale Gabor filters (SSG) are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az) under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az = 0.9502 over a training set of 40 images and Az = 0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms. PMID:27738422
Directory of Open Access Journals (Sweden)
Fernando Cervantes-Sanchez
2016-01-01
Full Text Available This paper presents a novel method for improving the training step of the single-scale Gabor filters by using the Boltzmann univariate marginal distribution algorithm (BUMDA in X-ray angiograms. Since the single-scale Gabor filters (SSG are governed by three parameters, the optimal selection of the SSG parameters is highly desirable in order to maximize the detection performance of coronary arteries while reducing the computational time. To obtain the best set of parameters for the SSG, the area (Az under the receiver operating characteristic curve is used as fitness function. Moreover, to classify vessel and nonvessel pixels from the Gabor filter response, the interclass variance thresholding method has been adopted. The experimental results using the proposed method obtained the highest detection rate with Az=0.9502 over a training set of 40 images and Az=0.9583 with a test set of 40 images. In addition, the experimental results of vessel segmentation provided an accuracy of 0.944 with the test set of angiograms.
Kostencka, J.; Kozacki, T.
2016-04-01
Filtered back propagation (FBPP) is a well-established reconstruction technique that is used in diffractive holographic tomography. The great advantage of the algorithm is the space-domain implementation, which enables avoiding the error-prone interpolation in the spectral domain that is an inherent part of the main counterpart of FBPP - the direct inversion tomographic reconstruction method. However, the fundamental flaw of FBPP is lack of generality, i.e. the method can be applied solely for the classical tomographic systems, where alternation of the measurement views is achieved by rotating a sample. At the same time, majority of the nowadays tomographic setups apply an alternative measurement concept, which is based on scanning of an illumination beam. The aim of this paper is to remove the mentioned limitation of the FBPP and enable its application in the systems utilizing scanning of illumination. This is achieved by introducing a new method of accounting for uneven cover of the sampled object frequencies, which applies normalization of the object spectrum with coherent transfer function of a considered tomographic system. The feasibility of the proposed, modified filtered back propagation algorithm is demonstrated with numerical simulations, which mimic tomographic measurement of a complex sample, i.e. the Shepp-Logan phantom.
Inter Channel Correlation based Demosaicking Algorithm for Enhanced Bayer Color Filter Array
Directory of Open Access Journals (Sweden)
K. John Peter
2014-04-01
Full Text Available Demosaicking is a process of obtaining a full color image by interpolating the missing colors of an image captured from a digital still and video cameras that use a single-sensor array. In this study a new Color Filter Array (CFA is proposed. Which is based on the actual weight of the Human Visual System. It is developed based on the sensitivity level of the human eye to red as 29.9%, green as 58.7% and blue as 11.4%. This study also provides an effective iterative demosaicing algorithm applying a weighted-edge interpolation to handle green pixels followed by a series of color difference interpolation to update red, blue and green pixels. Before applying demosaicking algorithm Gaussian filter is applied to remove noise of the sensor applied image and also enhance the image quality. Experimental results show that the proposed method performs much better than other latest demosaicing techniques in terms of image quality and PSNR value.
Directory of Open Access Journals (Sweden)
G.Mallikarjuna Rao
2014-09-01
Full Text Available In the present day real time applications of visual object tracking in surveillance, it has become extremely complex, time consuming and tricky to do the tracking when there are occlusions are present for small duration or for longer time and also when it is done in outdoor environments. In these conditions, the target to be tracked can be lost for few seconds and that should be tracked as soon as possible. As from the literature it is observed that particle filter can be able to track the target robustly in different kinds of background conditions, and it’s robust to partial occlusion. However, this tracking cannot recover from large proportion of occlusion and complete occlusion, to avoid this condition, we proposed two new algorithms (modified kalman and modified particle filter for fast tracking of objects in the presence of occlusions. We considered the complete occlusion of tracking object and the main objective is how fast the system is able to track the object after the occlusion is crossed. From the experimental results, it is observed that the proposed algorithms have shown good improvement in results compared to the traditional methods.
Optimal IIR filter design using Gravitational Search Algorithm with Wavelet Mutation
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S.K. Saha
2015-01-01
Full Text Available This paper presents a global heuristic search optimization technique, which is a hybridized version of the Gravitational Search Algorithm (GSA and Wavelet Mutation (WM strategy. Thus, the Gravitational Search Algorithm with Wavelet Mutation (GSAWM was adopted for the design of an 8th-order infinite impulse response (IIR filter. GSA is based on the interaction of masses situated in a small isolated world guided by the approximation of Newtonian’s laws of gravity and motion. Each mass is represented by four parameters, namely, position, active, passive and inertia mass. The position of the heaviest mass gives the near optimal solution. For better exploitation in multidimensional search spaces, the WM strategy is applied to randomly selected particles that enhance the capability of GSA for finding better near optimal solutions. An extensive simulation study of low-pass (LP, high-pass (HP, band-pass (BP and band-stop (BS IIR filters unleashes the potential of GSAWM in achieving better cut-off frequency sharpness, smaller pass band and stop band ripples, smaller transition width and higher stop band attenuation with assured stability.
Simulation Research on a SVPWM Control Algorithm for a Four-Leg Active Power Filter
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper the topology of a four-leg shunt active-power filter (APF) is given.The APF compensates harmonic and reactive power in a three-phase four-wire system.The scheme adopted for control of the four-leg active power filter, a 3-Dimensional Pulse Width Modulation (PWM) technique, is presented.The theoretical deduction of a space vector PWM (SVPWM) algorithm is given in this paper.The paper also analyzes the distribution of the voltage-space vector of the four-leg converter in αβγ coordinates and describes methods to determine the location of the voltage-space vector and to calculate duration time.Finally, the algorithm is implemented in simulation; the results show that the total harmonic distortion (THD) of the three phase-current waveforms is reduced.The neutral wire current, after compensation, is about 0 A showing that the topology of the four-leg shunt APF is feasible and the proposed scheme is effective.
Niederhauser, Thomas; Wyss-Balmer, Thomas; Haeberlin, Andreas; Marisa, Thanks; Wildhaber, Reto A; Goette, Josef; Jacomet, Marcel; Vogel, Rolf
2015-06-01
Long-term electrocardiogram (ECG) often suffers from relevant noise. Baseline wander in particular is pronounced in ECG recordings using dry or esophageal electrodes, which are dedicated for prolonged registration. While analog high-pass filters introduce phase distortions, reliable offline filtering of the baseline wander implies a computational burden that has to be put in relation to the increase in signal-to-baseline ratio (SBR). Here, we present a graphics processor unit (GPU)-based parallelization method to speed up offline baseline wander filter algorithms, namely the wavelet, finite, and infinite impulse response, moving mean, and moving median filter. Individual filter parameters were optimized with respect to the SBR increase based on ECGs from the Physionet database superimposed to autoregressive modeled, real baseline wander. A Monte-Carlo simulation showed that for low input SBR the moving median filter outperforms any other method but negatively affects ECG wave detection. In contrast, the infinite impulse response filter is preferred in case of high input SBR. However, the parallelized wavelet filter is processed 500 and four times faster than these two algorithms on the GPU, respectively, and offers superior baseline wander suppression in low SBR situations. Using a signal segment of 64 mega samples that is filtered as entire unit, wavelet filtering of a seven-day high-resolution ECG is computed within less than 3 s. Taking the high filtering speed into account, the GPU wavelet filter is the most efficient method to remove baseline wander present in long-term ECGs, with which computational burden can be strongly reduced.
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V. Sakthivel
2015-12-01
Full Text Available The design of low complexity sharp transition width Modified Discrete Fourier Transform (MDFT filter bank with perfect reconstruction (PR is proposed in this work. The current trends in technology require high data rates and speedy processing along with reduced power consumption, implementation complexity and chip area. Filters with sharp transition width are required for various applications in wireless communication. Frequency response masking (FRM technique is used to reduce the implementation complexity of sharp MDFT filter banks with PR. Further, to reduce the implementation complexity, the continuous coefficients of the filters in the MDFT filter banks are represented in discrete space using canonic signed digit (CSD. The multipliers in the filters are replaced by shifters and adders. The number of non-zero bits is reduced in the conversion process to minimize the number of adders and shifters required for the filter implementation. Hence the performances of the MDFT filter bank with PR may degrade. In this work, the performances of the MDFT filter banks with PR are improved using a hybrid Harmony-Gravitational search algorithm.
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Sicuranza Giovanni L
2007-01-01
Full Text Available The paper provides an analysis of the transient and the steady-state behavior of a filtered-x partial-error affine projection algorithm suitable for multichannel active noise control. The analysis relies on energy conservation arguments, it does not apply the independence theory nor does it impose any restriction to the signal distributions. The paper shows that the partial-error filtered-x affine projection algorithm in presence of stationary input signals converges to a cyclostationary process, that is, the mean value of the coefficient vector, the mean-square error and the mean-square deviation tend to periodic functions of the sample time.
Directory of Open Access Journals (Sweden)
N. F. Liu
2013-06-01
Full Text Available Land-surface albedo plays a critical role in the earth's radiant energy budget studies. Satellite remote sensing provides an effective approach to acquire regional and global albedo observations. Owing to cloud coverage, seasonal snow and sensor malfunctions, spatiotemporally continuous albedo datasets are often inaccessible. The Global LAnd Surface Satellite (GLASS project aims at providing a suite of key land surface parameter datasets with high temporal resolution and high accuracy for a global change study. The GLASS preliminary albedo datasets are global daily land-surface albedo generated by an angular bin algorithm (Qu et al., 2013. Like other products, the GLASS preliminary albedo datasets are affected by large areas of missing data; beside, sharp fluctuations exist in the time series of the GLASS preliminary albedo due to data noise and algorithm uncertainties. Based on the Bayesian theory, a statistics-based temporal filter (STF algorithm is proposed in this paper to fill data gaps, smooth albedo time series, and generate the GLASS final albedo product. The results of the STF algorithm are smooth and gapless albedo time series, with uncertainty estimations. The performance of the STF method was tested on one tile (H25V05 and three ground stations. Results show that the STF method has greatly improved the integrity and smoothness of the GLASS final albedo product. Seasonal trends in albedo are well depicted by the GLASS final albedo product. Compared with MODerate resolution Imaging Spectroradiometer (MODIS product, the GLASS final albedo product has a higher temporal resolution and more competence in capturing the surface albedo variations. It is recommended that the quality flag should be always checked before using the GLASS final albedo product.
Optimization of interference filters with genetic algorithms applied to silver-based heat mirrors.
Eisenhammer, T; Lazarov, M; Leutbecher, M; Schöffel, U; Sizmann, R
1993-11-01
In the optimization of multilayer stacks for various optical filtering purposes not only the thicknesses of the thin films are to be optimized, but also the sequence of materials. Materials with very different optical properties, such as metals and dielectrics, may be combined. A genetic algorithm is introduced to search for the optimal sequence of materials along with their optical thicknesses. This procedure is applied to a heat mirror in combination with a blackbody absorber for thermal solar energy applications at elevated temperatures (250 °C). The heat mirror is based on silver films with antireflective dielectric layers. Seven dielectrics have been considered. For a five-layer stack the sequence (TiO(2)/Ag/TiO(2)/Ag/Y(2)O(3)) is found to be optimal.
Directory of Open Access Journals (Sweden)
Deguang Wang
2011-02-01
Full Text Available Intrusion detection is a computer network system that collects information on several key points. and it gets these information from the security audit, monitoring, attack recognition and response aspects, check if there are some the behavior and signs against the network security policy. The classification of data acquisition is a key part of intrusion detection. In this article, we use the data cloud model to classify the invasion, effectively maintaining a continuous data on the qualitative ambiguity of the concept and evaluation phase of the invasion against the use of the coordination level filtering recommendation algorithm greatly improves the intrusion detection system in the face of massive data processing efficiency suspicious intrusion.
An inertia-free filter line-search algorithm for large-scale nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Chiang, Nai-Yuan; Zavala, Victor M.
2016-02-15
We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.
Different View on PQ Theory Used in the Control Algorithm of Active Power Filters
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Rastislav Pavlanin
2006-01-01
Full Text Available The improvement of power quality is a frequently discussed issue, which still requires a considerable research effort to be devoted to the study of the problem. The aim of this paper is to describe some problems related to the control of switching compensators, commonly known as active power filters. It also includes some shortcomings of pq theory regarded as three phase instantaneous power theory. The term “shortcomings” means that the pq theory does not provide a proper description of power properties. Moreover the control algorithm based on this theory only achieves satisfactory results for sinusoidal balanced voltage system. Nevertheless it can still be considered a helpful approach to the problem under study. The simulation results presented in this paper illustrate the weaknesses of the pq theory.
Battery State-of-Charge and Parameter Estimation Algorithm Based on Kalman Filter
DEFF Research Database (Denmark)
Dragicevic, Tomislav; Sucic, Stjepan; Guerrero, Josep M.
2013-01-01
Electrochemical battery is the most widely used energy storage technology, finding its application in various devices ranging from low power consumer electronics to utility back-up power. All types of batteries show highly non-linear behaviour in terms of dependence of internal parameters...... on operating conditions, momentary replenishment and a number of past charge/discharge cycles. A good indicator for the quality of overall customer service in any battery based application is the availability and reliability of these informations, as they point out important runtime variables...... such as the actual state of charge (SOC) and state of health (SOH). Therefore, a modern battery management systems (BMSs) should incorporate functions that accommodate real time tracking of these nonlinearities. For that purpose, Kalman filter based algorithms emerged as a convenient solution due to their ability...
协同过滤推荐算法专利综述%Patent Review of Collaborative Filtering Recommendation Algorithm
Institute of Scientific and Technical Information of China (English)
张博; 周瑞瑞; 鱼冰
2015-01-01
在信息化爆炸的时代,面对海量信息人们往往无法快速确定自己真正需求的信息,推荐系统及其相应的推荐算法应运而生,协同过滤推荐算法的出现标志着推荐系统的产生.协同过滤算法包含基于用户的协同过滤算法和基于物品的协同过滤算法,能够实现个性化推荐、处理复杂的非结构对象、新异兴趣的发现,且随着时间推移性能提高、自动化程度高.%In the era of information explosion, facing the vast amounts of information, people are often unable to quickly determine their real demand information, recommendation system and its corresponding recommendation algorithm have come into being, collaborative filtering recommendation algorithm marks the generation of recommendation system. Collaborative filtering algorithm contains collaborative filtering algorithm based on user and collaborative filtering algorithm based on item to achieve personalized recommendation, dealing with complex non-structure object, and novelty interest, and with the improvement of time performance, high degree of automation is obtained.
Xiang, Shiming; Zhang, Haijiang
2016-11-01
It is known full-waveform inversion (FWI) is generally ill-conditioned and various strategies including pre-conditioning and regularizing the inversion system have been proposed to obtain a reliable estimation of the velocity model. Here, we propose a new edge-guided strategy for FWI in frequency domain to efficiently and reliably estimate velocity models with structures of the size similar to the seismic wavelength. The edges of the velocity model at the current iteration are first detected by the Canny edge detection algorithm that is widely used in image processing. Then, the detected edges are used for guiding the calculation of FWI gradient as well as enforcing edge-preserving total variation (TV) regularization for next iteration of FWI. Bilateral filtering is further applied to remove noise but keep edges of the FWI gradient. The proposed edge-guided FWI in the frequency domain with edge-guided TV regularization and bilateral filtering is designed to preserve model edges that are recovered from previous iterations as well as from lower frequency waveforms when FWI is conducted from lower to higher frequencies. The new FWI method is validated using the complex Marmousi model that contains several steeply dipping fault zones and hundreds of horizons. Compared to FWI without edge guidance, our proposed edge-guided FWI recovers velocity model anomalies and edges much better. Unlike previous image-guided FWI or edge-guided TV regularization strategies, our method does not require migrating seismic data, thus is more efficient for real applications.
Adaptive Controller for Vehicle Active Suspension Generated Through LMS Filter Algorithms
Institute of Scientific and Technical Information of China (English)
SUN Jianmin; SHU Gequn
2006-01-01
The least means squares (LMS) adaptive filter algorithm was used in active suspension system.By adjusting the weight of adaptive filter, the minimum quadratic performance index was obtained.For two-degree-of-freedom vehicle suspension model, LMS adaptive controller was designed.The acceleration of the sprung mass,the dynamic tyre load between wheels and road,and the dynamic deflection between sprung mass and unsprung mass were determined as the evaluation targets of suspension performance.For LMS adaptive control suspension, compared with passive suspension, acceleration power spectral density of sprung mass acceleration under the road input model decreased 8-10 times in high frequency resonance band or low frequency resonance band.The simulation results show that LMS adaptive control is simple and remarkably effective.It further proves that the active control suspension system can improve both the riding comfort and handling safety in various operation conditions, and the method is fit for the active control of the suspension system.
Chen, Jing; Liu, Tundong; Jiang, Hao
2016-01-01
A Pareto-based multi-objective optimization approach is proposed to design multichannel FBG filters. Instead of defining a single optimal objective, the proposed method establishes the multi-objective model by taking two design objectives into account, which are minimizing the maximum index modulation and minimizing the mean dispersion error. To address this optimization problem, we develop a two-stage evolutionary computation approach integrating an elitist non-dominated sorting genetic algorithm (NSGA-II) and technique for order preference by similarity to ideal solution (TOPSIS). NSGA-II is utilized to search for the candidate solutions in terms of both objectives. The obtained results are provided as Pareto front. Subsequently, the best compromise solution is determined by the TOPSIS method from the Pareto front according to the decision maker's preference. The design results show that the proposed approach yields a remarkable reduction of the maximum index modulation and the performance of dispersion spectra of the designed filter can be optimized simultaneously.
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.
Institute of Scientific and Technical Information of China (English)
1998-01-01
Vibration suppression is one of the most important tasks for helicopter research. ACSR (Active Control of Structural Response) in time domain has turned out to be an effective technique to deal with this issue. In this paper, based on Least Square Principle and ACSR Principle, a multichannel delayed filtered-x FTF (Fast Transversal Filter) algorithm is developed for suppressing helicopter vibration. In order to keep the algorithm running in a stable and efficient state, DRR (Desired Response Reconstruction) technique is developed and a Constraint Stabilization Technique is firstly presented for the developed algorithm. Computer simulations are conducted on attenuating helicopter vibration and remarkable vibration reductions are achieved. The results demonstrate good properties of the obtained FTF algorithm in stability, robustness, convergence speed, tracking capability, etc.. They also show that time delay and DRR technique play important and effective roles in keeping ACSR system working efficiently.
A Novel Pixon-Based Image Segmentation Process Using Fuzzy Filtering and Fuzzy C-mean Algorithm
DEFF Research Database (Denmark)
Nadernejad, E; Barari, Amin
2011-01-01
for image segmentation. The key idea is to create a pixon model by combining fuzzy filtering as a kernel function and a fuzzy c-means clustering algorithm for image segmentation. Use of fuzzy filters reduces noise and slightly smoothes the image. Use of the proposed pixon model prevented image over......Image segmentation, which is an important stage of many image processing algorithms, is the process of partitioning an image into nonintersecting regions, such that each region is homogeneous and the union of no two adjacent regions is homogeneous. This paper presents a novel pixon-based algorithm......-segmentation and produced better experimental results than those obtained with other pixon-based algorithms....
Dong, Feng; Gunn, James E; Wechsler, Risa H
2007-01-01
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is ~85% complete and over 90% pure for clusters with masses above 1.0*10^{14} h^{-1} M_solar and redshifts up to z=0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensi...
Energy Technology Data Exchange (ETDEWEB)
Dong, Feng; Pierpaoli, Elena; Gunn, James E.; Wechsler, Risa H.
2007-10-29
We present a modified adaptive matched filter algorithm designed to identify clusters of galaxies in wide-field imaging surveys such as the Sloan Digital Sky Survey. The cluster-finding technique is fully adaptive to imaging surveys with spectroscopic coverage, multicolor photometric redshifts, no redshift information at all, and any combination of these within one survey. It works with high efficiency in multi-band imaging surveys where photometric redshifts can be estimated with well-understood error distributions. Tests of the algorithm on realistic mock SDSS catalogs suggest that the detected sample is {approx} 85% complete and over 90% pure for clusters with masses above 1.0 x 10{sup 14}h{sup -1} M and redshifts up to z = 0.45. The errors of estimated cluster redshifts from maximum likelihood method are shown to be small (typically less that 0.01) over the whole redshift range with photometric redshift errors typical of those found in the Sloan survey. Inside the spherical radius corresponding to a galaxy overdensity of {Delta} = 200, we find the derived cluster richness {Lambda}{sub 200} a roughly linear indicator of its virial mass M{sub 200}, which well recovers the relation between total luminosity and cluster mass of the input simulation.
Efficient Rectangular Maximal-Volume Algorithm for Rating Elicitation in Collaborative Filtering
Fonarev, Alexander
2017-02-07
Cold start problem in Collaborative Filtering can be solved by asking new users to rate a small seed set of representative items or by asking representative users to rate a new item. The question is how to build a seed set that can give enough preference information for making good recommendations. One of the most successful approaches, called Representative Based Matrix Factorization, is based on Maxvol algorithm. Unfortunately, this approach has one important limitation - a seed set of a particular size requires a rating matrix factorization of fixed rank that should coincide with that size. This is not necessarily optimal in the general case. In the current paper, we introduce a fast algorithm for an analytical generalization of this approach that we call Rectangular Maxvol. It allows the rank of factorization to be lower than the required size of the seed set. Moreover, the paper includes the theoretical analysis of the method\\'s error, the complexity analysis of the existing methods and the comparison to the state-of-the-art approaches.
Unscented Kalman Filter Algorithm for WiFi-PDR Integrated Indoor Positioning
Directory of Open Access Journals (Sweden)
CHEN GuoLiang
2015-12-01
Full Text Available Indoor positioning still faces lots of fundamental technical problems although it has been widely applied. A novel indoor positioning technology by using the smart phone with the assisting of the widely available and economically signals of WiFi is proposed. It also includes the principles and characteristics in indoor positioning. Firstly, improve the system's accuracy by fusing the WiFi fingerprinting positioning and PDR (ped estrian dead reckoning positioning with UKF (unscented Kalman filter. Secondly, improve the real-time performance by clustering the WiFi fingerprinting with k-means clustering algorithm. An investigation test was conducted at the indoor environment to learn about its performance on a HUAWEI P6-U06 smart phone. The result shows that compared to the pattern-matching system without clustering, an average reduction of 51% in the time cost can be obtained without degrading the positioning accuracy. When the state of personnel is walking, the average positioning error of WiFi is 7.76 m, the average positioning error of PDR is 4.57 m. After UKF fusing, the system's average positioning error is down to 1.24 m. It shows that the algorithm greatly improves the system's real-time and positioning accuracy.
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Barthélémy, S.; Ricci, S.; Pannekoucke, O.; Thual, O.; Malaterre, P. O.
2013-06-01
This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this
Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model
Directory of Open Access Journals (Sweden)
S. Barthélémy
2013-06-01
Full Text Available This study describes the emulation of an Ensemble Kalman Filter (EnKF algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length
Imaging source process of earthquakes from back-projection of high frequency seismograms
Pulido, N.
2007-12-01
the waveforms at the end. We band-pass filter the data between 1Hz and 30Hz, and calculate the waveforms envelopes using the root-mean-square of the original waveforms and their Hilbert transform. We calculate a grid "brigthness" by adding all the envelope amplitudes corresponding to every grid isochron time for all stations. The final result is a distribution of the brightness across the fault plane, which gives us an idea of the location of asperities within the fault plane. We obtained an image of the source process of recent Japanese crustal earthquakes, by using data of the K-NET and KiK-net strong motion networks operated by NIED, and applying the Isochrones Backprojection Method (IBM). Our method has the capability to quickly map asperities of large earthquakes, and is able to provide stable estimates of the fault rupture velocity. We investigate the resolution of our source models by exploring different data sets as well as performing synthetic tests. References Festa, G., and A. Zollo, Geophys. J. Int.,166, 745-756, 2006. Ishii, M., P. Shearer, H. Houston, and J. E. Vidale, Nature, 435, 933-936, 2005. Kao, H., and S.J. Shan, Geophys. J. Int., 157, 589-594, 2004. Pulido, N., S. Aoi, and H. Fujiwara, 2007. Rupture process of the 2007 Notohanto Earthquake by using an Isochrones Back-projection Method and K-NET and KiK-net data, (submitted). Spudich, P., and E. Cranswick, Bull. Seism. Soc. Am. 74, 2083-2114, 1984. shis.bosai.go.jp/staff/nelson/index_e.html
一种卡尔曼滤波自适应算法%An adaptive Algorithm on Kalman Filtering
Institute of Scientific and Technical Information of China (English)
黄波; 郑新星; 刘凤伟
2012-01-01
自适应滤波是指随着外部信号的变化,滤波器能够自我调节滤波参数,使得滤波器的某一性能指标达到最优。文章以卡尔曼滤波理论为基础,给出一种新的自适应卡尔曼滤波算法。%Adaptive-filtering means the filter could adjust filtration parameters by itself and make some performance index optimal when the external signals vary.This paper will give a new Kalman filter algorithm whose base is Kalman filter theory.
Desai, Naeem M.; Lionheart, William R. B.
2016-11-01
We give an explicit plane-by-plane filtered back-projection reconstruction algorithm for the transverse ray transform of symmetric second rank tensor fields on Euclidean three-space, using data from rotation about three orthogonal axes. We show that in the general case two-axis data is insufficient, but we give an explicit reconstruction procedure for the potential case with two-axis data. We describe a numerical implementation of the three-axis algorithm and give reconstruction results for simulated data.
Institute of Scientific and Technical Information of China (English)
LI; Zicheng; SUN; Yukun
2006-01-01
Considering the detection principle that "when load current is periodic current, the integral in a cycle for absolute value of load current subtracting fundamental active current is the least", harmonic current real-time detection methods for power active filter are proposed based on direct computation, simple iterative algorithm and optimal iterative algorithm. According to the direct computation method, the amplitude of the fundamental active current can be accurately calculated when load current is placed in stable state. The simple iterative algorithm and the optimal iterative algorithm provide an idea about judging the state of load current. On the basis of the direct computation method, the simple iterative algorithm, the optimal iterative algorithm and precise definition of the basic concepts such as the true amplitude of the fundamental active current when load current is placed in varying state, etc., the double linear construction idea is proposed in which the amplitude of the fundamental active current at the moment of the sample is accurately calculated by using the first linear construction and the condition which disposes the next sample is created by using the second linear construction. On the basis of the double linear construction idea, a harmonic current real-time detection method for power active filter is proposed based on the double linear construction algorithm. This method has the characteristics of small computing quantity, fine real-time performance, being capable of accurately calculating the amplitude of the fundamental active current and so on.
Maes, K.; Iliopoulos, A.; Weijtjens, W.; Devriendt, C.; Lombaert, G.
2016-08-01
Offshore wind turbines are exposed to continuous wind and wave excitation. The monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. At some critical locations below the water level, direct measurements of the strains are not feasible. Response estimation techniques can then be used to estimate the strains from a limited set of response measurements and a system model. This paper compares a Kalman filtering algorithm, a joint input-state estimation algorithm, and a modal expansion algorithm, for the estimation of dynamic strains in the tower of an offshore monopile wind turbine. The algorithms make use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. The strain signals obtained from the response estimation algorithms are compared to the actual measured strains in the tower.
Comparison of Deconvolution Filters for Photoacoustic Tomography.
Directory of Open Access Journals (Sweden)
Dominique Van de Sompel
Full Text Available In this work, we compare the merits of three temporal data deconvolution methods for use in the filtered backprojection algorithm for photoacoustic tomography (PAT. We evaluate the standard Fourier division technique, the Wiener deconvolution filter, and a Tikhonov L-2 norm regularized matrix inversion method. Our experiments were carried out on subjects of various appearances, namely a pencil lead, two man-made phantoms, an in vivo subcutaneous mouse tumor model, and a perfused and excised mouse brain. All subjects were scanned using an imaging system with a rotatable hemispherical bowl, into which 128 ultrasound transducer elements were embedded in a spiral pattern. We characterized the frequency response of each deconvolution method, compared the final image quality achieved by each deconvolution technique, and evaluated each method's robustness to noise. The frequency response was quantified by measuring the accuracy with which each filter recovered the ideal flat frequency spectrum of an experimentally measured impulse response. Image quality under the various scenarios was quantified by computing noise versus resolution curves for a point source phantom, as well as the full width at half maximum (FWHM and contrast-to-noise ratio (CNR of selected image features such as dots and linear structures in additional imaging subjects. It was found that the Tikhonov filter yielded the most accurate balance of lower and higher frequency content (as measured by comparing the spectra of deconvolved impulse response signals to the ideal flat frequency spectrum, achieved a competitive image resolution and contrast-to-noise ratio, and yielded the greatest robustness to noise. While the Wiener filter achieved a similar image resolution, it tended to underrepresent the lower frequency content of the deconvolved signals, and hence of the reconstructed images after backprojection. In addition, its robustness to noise was poorer than that of the Tikhonov
Theoretical framework for filtered back projection in tomosynthesis
Lauritsch, Guenter; Haerer, Wolfgang H.
1998-06-01
Tomosynthesis provides only incomplete 3D-data of the imaged object. Therefore it is important for reconstruction tasks to take all available information carefully into account. We are focusing on geometrical aspects of the scan process which can be incorporated into reconstruction algorithms by filtered backprojection methods. Our goal is a systematic approach to filter design. A unified theory of tomosynthesis is derived in the context of linear system theory, and a general four-step filter design concept is presented. Since the effects of filtering are understandable in this context, a methodical formulation of filter functions is possible in order to optimize image quality regarding the specific requirements of any application. By variation of filter parameters the slice thickness and the spatial resolution can easily be adjusted. The proposed general concept of filter design is exemplarily discussed for circular scanning but is valid for any specific scan geometry. The inherent limitations of tomosynthesis are pointed out and strategies for reducing the effects of incomplete sampling are developed. Results of a dental application show a striking improvement in image quality.
Institute of Scientific and Technical Information of China (English)
FENG Bo; MA Hong-Bin; FU Meng-Yin; WANG Shun-Ting
2013-01-01
Kalman filtering techniques have been widely used in many applications,however,standard Kalman filters for linear Gaussian systems usually cannot work well or even diverge in the presence of large model uncertainty.In practical applications,it is expensive to have large number of high-cost experiments or even impossible to obtain an exact system model.Motivated by our previous pioneering work on finite-model adaptive control,a framework of finite-model Kalman filtering is introduced in this paper.This framework presumes that large model uncertainty may be restricted by a finite set of known models which can be very different from each other.Moreover,the number of known models in the set can be flexibly chosen so that the uncertain model may always be approximated by one of the known models,in other words,the large model uncertainty is "covered" by the "convex hull" of the known models.Within the presented framework according to the idea of adaptive switching via the minimizing vector distance principle,a simple finite-model Kalman filter,MVDP-FMKF,is mathematically formulated and illustrated by extensive simulations.An experiment of MEMS gyroscope drift has verified the effectiveness of the proposed algorithm,indicating that the mechanism of finite-model Kalman filter is useful and efficient in practical applications of Kalman filters,especially in inertial navigation systems.
Latifoğlu, Fatma
2013-09-01
In this study a novel approach based on 2D FIR filters is presented for denoising digital images. In this approach the filter coefficients of 2D FIR filters were optimized using the Artificial Bee Colony (ABC) algorithm. To obtain the best filter design, the filter coefficients were tested with different numbers (3×3, 5×5, 7×7, 11×11) and connection types (cascade and parallel) during optimization. First, the speckle noise with variances of 1, 0.6, 0.8 and 0.2 respectively was added to the synthetic test image. Later, these noisy images were denoised with both the proposed approach and other well-known filter types such as Gaussian, mean and average filters. For image quality determination metrics such as mean square error (MSE), peak signal-to-noise ratio (PSNR) and signal-to-noise ratio (SNR) were used. Even in the case of noise having maximum variance (the most noisy), the proposed approach performed better than other filtering methods did on the noisy test images. In addition to test images, speckle noise with a variance of 1 was added to a fetal ultrasound image, and this noisy image was denoised with very high PSNR and SNR values. The performance of the proposed approach was also tested on several clinical ultrasound images such as those obtained from ovarian, abdomen and liver tissues. The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
S. El-Ouafi Bahlous
2013-01-01
Full Text Available The authors recently developed a damage identification method which combines ambient vibration measurements and a Statistical Modal Filtering approach to predict the location and degree of damage. The method was then validated experimentally via ambient vibration tests conducted on full-scale reinforced concrete laboratory specimens. The main purpose of this paper is to demonstrate the feasibility of the identification method for a real bridge. An important challenge in this case is to overcome the absence of vibration measurements for the structure in its undamaged state which corresponds ideally to the reference state of the structure. The damage identification method is, therefore, modified to adapt it to the present situation where the intact state was not subjected to measurements. An additional refinement of the method consists of using a genetic algorithm to improve the computational efficiency of the damage localization method. This is particularly suited for a real case study where the number of damage parameters becomes significant. The damage diagnosis predictions suggest that the diagnosed bridge is damaged in four elements among a total of 168 elements with degrees of damage varying from 6% to 18%.
An algorithm to filter out packing arrangements based on steric clashes.
Koudelka, Bohdan; Capkova, Pavla
2003-12-01
This document outlines the use of an algorithm to filter out impossible crystal-packing arrangements based on steric considerations. Within an exhaustive grid search frame, the space sample is reduced by analysis of spherical areas where atom pairs from different rigid units might clash. This technique finds areas in the state space where the global energy minimum might lie. The minimum can then be found by the usual methods of molecular modeling restricted to these particular areas. Only a tiny fraction of atom pair distances need to be tested; usually a single quantity on average per one state of model space! For example, a crystal of three rigid molecules, each containing 12 atoms, has 3x12x12=432 atom pairs just in one unit cell but our method needs to test on average 1 to 4 atom pairs per state. Using modern computers, about 10(12-15) models can be tested within several hours or days. For example, a crystal model with six rotational degrees of freedom (two rigid molecules in the unit cell) each with step 3 degrees can be tested in a few hours on a 1-GHz x86 processor-based machine. The method presented here has been implemented in the SUPRAMOL program.
Energy Technology Data Exchange (ETDEWEB)
Seo, Chang-Woo; Cha, Bo Kyung; Jeon, Sungchae; Huh, Young [Converged Medical Device Research Center, Advanced Medical Device Research Division, KERI, Gyeonggido 426-910 (Korea, Republic of)
2015-07-01
Recently, beam hardening reduction is required to produce high-quality reconstructions of X-ray cone-beam computed tomography (CBCT) system for medical applications. This paper introduces the iterative total variation (ITV) for filtered-backprojection suffering from the serious beam hardening problems. Feldkamp, Davis, and Kress (FDK) reconstruction algorithm for CBCT system is widely used reconstruction technique. FDK reconstruction algorithm could be realized by generating the weighted projection data, filtering the projection images, and back-projecting the filtered projection data into the volume. However, FDK algorithm suffers from the beam hardening artifacts by X-ray attenuation coefficients. Recently, total variation (TV) method for compressed sensing (CS) has been particularly useful in exploiting the prior knowledge of minimal variation in the X-ray attenuation characteristics across object or human body. But a practical implementation of this method still remains a challenge. The main problem is the iterative nature of solving the TV-based CS formulation, which generally requires multiple iterations of forward and backward projections of a large dataset in clinically or industrially feasible time frame. In this paper, we propose ITV method after FDK reconstruction for reducing the beam hardening artifacts. The beam hardening problems are reduced by the ITV method to promote sparsity inherent in the X-ray attenuation characteristics. (authors)
Wang, Bin; Dong, Lili; Zhao, Ming; Xu, Wenhai
2015-12-01
In order to realize accurate detection for small dim infrared maritime target, this paper proposes a target detection algorithm based on local peak detection and pipeline-filtering. This method firstly extracts some suspected targets through local peak detection and removes most of non-target peaks with self-adaptive threshold process. And then pipeline-filtering is used to eliminate residual interferences so that only real target can be retained. The experiment results prove that this method has high performance on target detection, and its missing alarm rate and false alarm rate can basically meet practical requirements.
Directory of Open Access Journals (Sweden)
P. Li
2017-06-01
Full Text Available Use of the Gaussian inverse Wishart probability hypothesis density (GIW-PHD filter has demonstrated promise as an approach to track an unknown number of extended targets. However, when targets of various sizes are spaced closely together and performing maneuvers, estimation errors will occur because measurement partitioning algorithms fail to provide the correct partitions. Specifically, the sub-partitioning algorithm fails to handle cases in which targets are of different sizes, while other partitioning approaches are sensitive to target maneuvers. This paper presents an improved partitioning algorithm for a GIW-PHD filter in order to solve the above problems. The sub-partitioning algorithm is improved by considering target extension information and by employing Mahalanobis distances to distinguish among measurement cells of different sizes. Thus, the improved approach is not sensitive to either differences in target sizes or target maneuvering. Simulation results show that the use of the proposed partitioning approach can improve the tracking performance of a GIW-PHD filter when target are spaced closely together.
An adaptive nonlocal filtering for low-dose CT in both image and projection domains
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Yingmei Wang
2015-04-01
Full Text Available An important problem in low-dose CT is the image quality degradation caused by photon starvation. There are a lot of algorithms in sinogram domain or image domain to solve this problem. In view of strong self-similarity contained in the special sinusoid-like strip data in the sinogram space, we propose a novel non-local filtering, whose average weights are related to both the image FBP (filtered backprojection reconstructed from restored sinogram data and the image directly FBP reconstructed from noisy sinogram data. In the process of sinogram restoration, we apply a non-local method with smoothness parameters adjusted adaptively to the variance of noisy sinogram data, which makes the method much effective for noise reduction in sinogram domain. Simulation experiments show that our proposed method by filtering in both image and projection domains has a better performance in noise reduction and details preservation in reconstructed images.
Sheng-Hui, Rong; Hui-Xin, Zhou; Han-Lin, Qin; Rui, Lai; Kun, Qian
2016-05-01
Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.
Pham, Mai Quyen; Chaux, Caroline; Pesquet, Jean-Christophe
2014-01-01
Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. Th...
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Šaponjić Đorđe
2009-01-01
Full Text Available By applying the well known dualism: mean count rate - mean time between successive pulses - the equivalence between an IIR digital filter and a preset count digital rate meter has been demonstrated. By using a bank of four second order IIR filters and an optimized automated algorithm for filter selection, a practical realization of a preset count rate meter giving good tradeoff between statistical fluctuations and speed of response, particularly at low count rates such as background monitoring, is presented. The presented solution is suitable for designing portable count rate meters. The designed prototype is capable of operating up to 3600 pulses per second with an accuracy of over 4% in steady-state and response times of 1 second for the rising edge and 2 seconds for the falling edge of the mean count rate step-change.
CSIR Research Space (South Africa)
Salmon, BP
2012-07-01
Full Text Available In this paper the Bias Variance Search Algorithm is proposed as an algorithm to optimize a candidate set of initial parameters for an Extended Kalman filter (EKF). The search algorithm operates on a Bias Variance Equilibrium Point criterion...
Directory of Open Access Journals (Sweden)
Xiang Gao
2012-05-01
Full Text Available In order to process target tracking approximation with unknown motion state models beforehand in a two-dimensional field of binary proximity sensors, the algorithms based on cost functions of particle filters and near-linear curve simple optimization are proposed in this paper. Through moving target across detecting intersecting fields of sensor nodes sequentially, cost functions are introduced to solve target tracking approximation and velocity estimation which is not similar to traditional particle filters that rely on probabilistic assumptions about the motion states. Then a near-linear curve geometric approach is used to simplify and easily describe target trajectories that are below a certain error measure. Because there maybe some sensor nodes invalid in practice, so a fault-tolerant detection is applied to avoid the nodes’ reporting fault and also improve accuracy of tracking at the same time. The validity of our algorithms is demonstrated through simulation results.
Fereydooni, H.; Mojeddifar, S.
2017-09-01
This study introduced a different procedure to implement matched filtering algorithm (MF) on the ASTER images to obtain the distribution map of alteration minerals in the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA). This region contains many areas with porphyry copper mineralization such as Meiduk, Abdar, Kader, Godekolvari, Iju, Serenu, Chahfiroozeh and Parkam. Also argillization, sericitization and propylitization are the most common types of hydrothermal alteration in the area. Matched filtering results were provided for alteration minerals with a matched filtering score, called MF image. To identify the pixels which contain only one material (endmember), an appropriate threshold value should be used to the MF image. The chosen threshold classifies a MF image into background and target pixels. This article argues that the current thresholding process (the choice of a threshold) shows misclassification for MF image. To address the issue, this paper introduced the directed matched filtering (DMF) algorithm in which a spectral signature-based filter (SSF) was used instead of the thresholding process. SSF is a user-defined rule package which contains numeral descriptions about the spectral reflectance of alteration minerals. On the other hand, the spectral bands are defined by an upper and lower limit in SSF filter for each alteration minerals. SSF was developed for chlorite, kaolinite, alunite, and muscovite minerals to map alteration zones. The validation proved that, at first: selecting a contiguous range of MF values could not identify desirable results, second: unexpectedly, considerable frequency of pure pixels was observed in the MF scores less than threshold value. Also, the comparison between DMF results and field studies showed an accuracy of 88.51%.
Detection and analysis of microseismic events using a Matched Filtering Algorithm (MFA)
Caffagni, Enrico; Eaton, David W.; Jones, Joshua P.; van der Baan, Mirko
2016-07-01
A new Matched Filtering Algorithm (MFA) is proposed for detecting and analysing microseismic events recorded by downhole monitoring of hydraulic fracturing. This method requires a set of well-located template (`parent') events, which are obtained using conventional microseismic processing and selected on the basis of high signal-to-noise (S/N) ratio and representative spatial distribution of the recorded microseismicity. Detection and extraction of `child' events are based on stacked, multichannel cross-correlation of the continuous waveform data, using the parent events as reference signals. The location of a child event relative to its parent is determined using an automated process, by rotation of the multicomponent waveforms into the ray-centred co-ordinates of the parent and maximizing the energy of the stacked amplitude envelope within a search volume around the parent's hypocentre. After correction for geometrical spreading and attenuation, the relative magnitude of the child event is obtained automatically using the ratio of stacked envelope peak with respect to its parent. Since only a small number of parent events require interactive analysis such as picking P- and S-wave arrivals, the MFA approach offers the potential for significant reduction in effort for downhole microseismic processing. Our algorithm also facilitates the analysis of single-phase child events, that is, microseismic events for which only one of the S- or P-wave arrivals is evident due to unfavourable S/N conditions. A real-data example using microseismic monitoring data from four stages of an open-hole slickwater hydraulic fracture treatment in western Canada demonstrates that a sparse set of parents (in this case, 4.6 per cent of the originally located events) yields a significant (more than fourfold increase) in the number of located events compared with the original catalogue. Moreover, analysis of the new MFA catalogue suggests that this approach leads to more robust interpretation
一种改进的均值滤波算法%A MODIFIED AVERAGE FILTERING ALGORITHM
Institute of Scientific and Technical Information of China (English)
朱士虎; 游春霞
2013-01-01
针对均值滤波在抑制噪声的过程中会损失图像的边缘等细节信息从而导致整幅图像模糊的问题，提出一种均值滤波改进算法。算法中局部窗口内中心像素灰度均值的计算既考虑了窗口内各像素与中心像素间的灰度值差异，又顾及了窗口内各像素与中心像素间的距离。实验结果表明，该算法能有效去除噪声，较好地保留图像边缘细节，相比传统均值滤波和自适应均值滤波算法有更好的去噪能力。%Details of image are broken by mean filter in image processing , and consequently the image turns out to be blurry .In light of this, we propose a modified average filtering algorithm .In the algorithm, the computation of gray averaging value of central pixel in local window considers both the gray value difference and the spatial distance between the central pixel and other neighbouring pixels in current local window.Experimental results show that the presented algorithm can effectively remove Gaussian noise as well as preserving the edge details of the image well .It is superior to tradition mean filter and adaptive averaging filter algorithms in de-noise capability .
协同过滤推荐算法综述%Survey of Collaborative Filtering Recommedation Algorithm
Institute of Scientific and Technical Information of China (English)
黄正
2012-01-01
Recommendation system is one of the most important technologies in E-commerce. Collaborative filtering is the most widely used and the most successful recommendation technology. This paper first introduces the basic concept and principle of collaborative filtering. And then, this paper summarizes the key problems and related solutions of the collaborative filtering recommendation algorithm. Finally, this paper introduces the collaborative filtering recommendation algorithm need to be further solved problems and possible development direction.%推荐系统是电子商务系统中最重要的技术之一,协同过滤推荐技术是目前应用最广泛和最成功的推荐技术.本文首先介绍了协同过滤的基本概念和原理,然后总结了协同过滤推荐算法中的关键问题和相关解决方案,最后介绍了协同过滤推荐算法需要进一步解决的问题和可能的发展方向.
Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok
2016-05-23
This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.
Directory of Open Access Journals (Sweden)
Muhammad Ilyas
2016-05-01
Full Text Available This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF and Unscented Kalman filter (UKF were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level.
Ilyas, Muhammad; Hong, Beomjin; Cho, Kuk; Baeg, Seung-Ho; Park, Sangdeok
2016-01-01
This paper provides algorithms to fuse relative and absolute microelectromechanical systems (MEMS) navigation sensors, suitable for micro planetary rovers, to provide a more accurate estimation of navigation information, specifically, attitude and position. Planetary rovers have extremely slow speed (~1 cm/s) and lack conventional navigation sensors/systems, hence the general methods of terrestrial navigation may not be applicable to these applications. While relative attitude and position can be tracked in a way similar to those for ground robots, absolute navigation information is hard to achieve on a remote celestial body, like Moon or Mars, in contrast to terrestrial applications. In this study, two absolute attitude estimation algorithms were developed and compared for accuracy and robustness. The estimated absolute attitude was fused with the relative attitude sensors in a framework of nonlinear filters. The nonlinear Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) were compared in pursuit of better accuracy and reliability in this nonlinear estimation problem, using only on-board low cost MEMS sensors. Experimental results confirmed the viability of the proposed algorithms and the sensor suite, for low cost and low weight micro planetary rovers. It is demonstrated that integrating the relative and absolute navigation MEMS sensors reduces the navigation errors to the desired level. PMID:27223293
Kelly, C. L.; Lawrence, J. F.
2014-12-01
During October 2012, 51 geophones and 6 broadband seismometers were deployed in an ~50x50m region surrounding a periodically erupting columnar geyser in the El Tatio Geyser Field, Chile. The dense array served as the seismic framework for a collaborative project to study the mechanics of complex hydrothermal systems. Contemporaneously, complementary geophysical measurements (including down-hole temperature and pressure, discharge rates, thermal imaging, water chemistry, and video) were also collected. Located on the western flanks of the Andes Mountains at an elevation of 4200m, El Tatio is the third largest geyser field in the world. Its non-pristine condition makes it an ideal location to perform minutely invasive geophysical studies. The El Jefe Geyser was chosen for its easily accessible conduit and extremely periodic eruption cycle (~120s). During approximately 2 weeks of continuous recording, we recorded ~2500 nighttime eruptions which lack cultural noise from tourism. With ample data, we aim to study how the source varies spatially and temporally during each phase of the geyser's eruption cycle. We are developing a new back-projection processing technique to improve source imaging for diffuse signals. Our method was previously applied to the Sierra Negra Volcano system, which also exhibits repeating harmonic and diffuse seismic sources. We back-project correlated seismic signals from the receivers back to their sources, assuming linear source to receiver paths and a known velocity model (obtained from ambient noise tomography). We apply polarization filters to isolate individual and concurrent geyser energy associated with P and S phases. We generate 4D, time-lapsed images of the geyser source field that illustrate how the source distribution changes through the eruption cycle. We compare images for pre-eruption, co-eruption, post-eruption and quiescent periods. We use our images to assess eruption mechanics in the system (i.e. top-down vs. bottom-up) and
A New Gaussian Noise Filtering Algorithm%一种新型高斯噪声滤波算法
Institute of Scientific and Technical Information of China (English)
王小兵; 孙久运
2011-01-01
In order to filter the Gaussian noise of digital image more effectively,a new filtering algorithm was proposed.Firstly,the image which contains Gaussian noise was decomposed two-dimensional wavelet,obtaining high frequency and low frequency wavelet coefficient.Secondly,retain the low-frequency wavelet coefficient unchanged,at the same time the high-frequency wavelet coefficient was conducted wiener filtering,the wavelet coefficients were reconstructed.Thirdly,the reconstruction image was implemented multi-scale wavelet decomposition,setting new threshold discriminant function so as to weaken unimportant decomposition coefficients.Finally,wavelet decomposition coefficients were reconstructed.By using this filtering algorithm,wiener filtering,wavelet threshold value method and average filtering on Gaussian noise denoising processing.The experiments show that PSNR value of the filtering algorithm is much higher than the other three methods.%为了更有效滤除数字图像中的高斯噪声,提出了一种新型滤波算法.该算法首先将含有高斯噪声的图像进行二维小波分解,得到高频和低频小波分解系数;然后保留低频小波系数不变,对高频小波系数通过维纳滤波器进行滤波,并进行小波系数重构;最后将重构图像进行多尺度小波分解,通过设定新的阈值和判别函数,弱化不重要的小波分解系数,并进行小波分解系数重构.分别采用该滤波算法、维纳滤波、小波阈值法以及均值滤波进行高斯噪声滤除处理,试验证明该滤波算法去噪后图像的PSNR值明显高于其他三种方法.
Institute of Scientific and Technical Information of China (English)
Han Wenhua; Que Peiwen
2006-01-01
With the widespread application and fast development of gas and oil pipeline network in China, the pipeline inspection technology has been used more extensively. The magnetic flux leakage (MFL) method has established itself as the most widely used in-line inspection technique for the evaluation of gas and oil pipelines. The MFL data obtained from seamless pipeline inspection is usually contaminated by the seamless pipe noise (SPN). SPN can in some cases completely mask MFL signals from certain type of defects,and therefore considerably reduces the detectability of the defect signals. In this paper, a new de-noising algorithm called wavelet domain adaptive filtering is proposed for removing the SPN contained in the MFL data. The new algorithm results from combining the wavelet transform with the adaptive filtering technique. Results from application of the proposed algorithm to the MFL data from field tests show that the proposed algorithm has good performance and considerably improves the detectability of the defect signals in the MFL data.
Optimal design and performance verification of a broadband waveguide filter using ANN-GA algorithm
Directory of Open Access Journals (Sweden)
Manidipa Nath
2013-09-01
Full Text Available In this work design and optimization of EBGstructure having multiple dielectric posts uniformly placed insidea rectangular waveguide is done to extract filter responses.Frequency response of BPF configuration using trained ANNmodel of multipost rectangular waveguide are studied andoptimized using GA. The geometrical and positional dimensionof post parameters are varied in accordance to the requirementof reflectance and transmittance of the filter.
Wang, Dun; Kawakatsu, Hitoshi; Zhuang, Jiancang; Mori, Jim; Maeda, Takuto; Tsuruoka, Hiroshi; Zhao, Xu
2017-06-01
Fast estimates of magnitude and source extent of large earthquakes are fundamental for disaster mitigation. However, resolving these estimates within 10-20 min after origin time remains challenging. Here we propose a robust algorithm to resolve magnitude and source length of large earthquakes using seismic data recorded by regional arrays and global stations. We estimate source length and source duration by backprojecting seismic array data. Then the source duration and the maximum amplitude of the teleseismic P wave displacement waveforms are used jointly to estimate magnitude. We apply this method to 74 shallow earthquakes that occurred within epicentral distances of 30-85° to Hi-net (2004-2014). The estimated magnitudes are similar to moment magnitudes estimated from W-phase inversions (U.S. Geological Survey), with standard deviations of 0.14-0.19 depending on the global station distributions. Application of this method to multiple regional seismic arrays could benefit tsunami warning systems and emergency response to large global earthquakes.
REALIZATION OF GPS／SST／SINS INTEGRATED NAVIGATION FILTER ALGORITHM FOR BALLISTIC MISSILE
Institute of Scientific and Technical Information of China (English)
KANGGuo-hua; LIUJian-ye; ZHUYan-hua; XIONGZhi
2005-01-01
Considering the domestic single navigation system of the ballistic missile, a new filter method is presented. The method integrates the information of the strapdown star tracker (SST) attitude, the position and the velocity of a high speed GPS with a strapdown inertial navigation system (SINS) information into one filter, thus improving the precision of the attitude, the velocity, and the position. Finally, the GPS/SST/SINS simulation platfornt is designed. Simulation results demonstrate that the filter is robust and reliable, and the precision rises to the comparative level abroad.
Offline Performance of the Filter Bank EEW Algorithm in the 2014 M6.0 South Napa Earthquake
Meier, M. A.; Heaton, T. H.; Clinton, J. F.
2014-12-01
Medium size events like the M6.0 South Napa earthquake are very challenging for EEW: the damage such events produce can be severe, but it is generally confined to relatively small zones around the epicenter and the shaking duration is short. This leaves a very short window for timely EEW alerts. Algorithms that wait for several stations to trigger before sending out EEW alerts are typically not fast enough for these kind of events because their blind zone (the zone where strong ground motions start before the warnings arrive) typically covers all or most of the area that experiences strong ground motions. At the same time, single station algorithms are often too unreliable to provide useful alerts. The filter bank EEW algorithm is a new algorithm that is designed to provide maximally accurate and precise earthquake parameter estimates with minimum data input, with the goal of producing reliable EEW alerts when only a very small number of stations have been reached by the p-wave. It combines the strengths of single station and network based algorithms in that it starts parameter estimates as soon as 0.5 seconds of data are available from the first station, but then perpetually incorporates additional data from the same or from any number of other stations. The algorithm analyzes the time dependent frequency content of real time waveforms with a filter bank. It then uses an extensive training data set to find earthquake records from the past that have had similar frequency content at a given time since the p-wave onset. The source parameters of the most similar events are used to parameterize a likelihood function for the source parameters of the ongoing event, which can then be maximized to find the most likely parameter estimates. Our preliminary results show that the filter bank EEW algorithm correctly estimated the magnitude of the South Napa earthquake to be ~M6 with only 1 second worth of data at the nearest station to the epicenter. This estimate is then
Model-based x-ray energy spectrum estimation algorithm from CT scanning data with spectrum filter
Li, Lei; Wang, Lin-Yuan; Yan, Bin
2016-10-01
With the development of technology, the traditional X-ray CT can't meet the modern medical and industry needs for component distinguish and identification. This is due to the inconsistency of X-ray imaging system and reconstruction algorithm. In the current CT systems, X-ray spectrum produced by X-ray source is continuous in energy range determined by tube voltage and energy filter, and the attenuation coefficient of object is varied with the X-ray energy. So the distribution of X-ray energy spectrum plays an important role for beam-hardening correction, dual energy CT image reconstruction or dose calculation. However, due to high ill-condition and ill-posed feature of system equations of transmission measurement data, statistical fluctuations of X ray quantum and noise pollution, it is very hard to get stable and accurate spectrum estimation using existing methods. In this paper, a model-based X-ray energy spectrum estimation method from CT scanning data with energy spectrum filter is proposed. First, transmission measurement data were accurately acquired by CT scan and measurement using phantoms with different energy spectrum filter. Second, a physical meaningful X-ray tube spectrum model was established with weighted gaussian functions and priori information such as continuity of bremsstrahlung and specificity of characteristic emission and estimation information of average attenuation coefficient. The parameter in model was optimized to get the best estimation result for filtered spectrum. Finally, the original energy spectrum was reconstructed from filtered spectrum estimation with filter priori information. Experimental results demonstrate that the stability and accuracy of X ray energy spectrum estimation using the proposed method are improved significantly.
Implementation of the FDK algorithm for cone-beam CT on the cell broadband engine architecture
Scherl, Holger; Koerner, Mario; Hofmann, Hannes; Eckert, Wieland; Kowarschik, Markus; Hornegger, Joachim
2007-03-01
In most of today's commercially available cone-beam CT scanners, the well known FDK method is used for solving the 3D reconstruction task. The computational complexity of this algorithm prohibits its use for many medical applications without hardware acceleration. The brand-new Cell Broadband Engine Architecture (CBEA) with its high level of parallelism is a cost-efficient processor for performing the FDK reconstruction according to the medical requirements. The programming scheme, however, is quite different to any standard personal computer hardware. In this paper, we present an innovative implementation of the most time-consuming parts of the FDK algorithm: filtering and back-projection. We also explain the required transformations to parallelize the algorithm for the CBEA. Our software framework allows to compute the filtering and back-projection in parallel, making it possible to do an on-the-fly-reconstruction. The achieved results demonstrate that a complete FDK reconstruction is computed with the CBEA in less than seven seconds for a standard clinical scenario. Given the fact that scan times are usually much higher, we conclude that reconstruction is finished right after the end of data acquisition. This enables us to present the reconstructed volume to the physician in real-time, immediately after the last projection image has been acquired by the scanning device.
National Research Council Canada - National Science Library
Zheng, Jian; Lu, Pei-Rong; Xiang, Dehui; Dai, Ya-Kang; Liu, Zhao-Bang; Kuai, Duo-Jie; Xue, Hui; Yang, Yue-Tao
2013-01-01
.... By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time...
National Research Council Canada - National Science Library
Zheng, Jian; Lu, Pei-Rong; Xiang, Dehui; Dai, Ya-Kang; Liu, Zhao-Bang; Kuai, Duo-Jie; Xue, Hui; Yang, Yue-Tao
2013-01-01
.... By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time...
Jian Zheng; Pei-Rong Lu; Dehui Xiang; Ya-Kang Dai; Zhao-Bang Liu; Duo-Jie Kuai; Hui Xue; Yue-Tao Yang
2013-01-01
We propose a new method to enhance and extract the retinal vessels. First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel. By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time. After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel str...
Directory of Open Access Journals (Sweden)
Abhijit Chandra
2012-10-01
Full Text Available In recent times, system designers are becoming very much apprehensive in reducing the structural complexity of digital systems with which they deal in practice. However, the uncontrolled minimization of any digital hardware always leads to significant deterioration of system performance making it incompatible for use in any practical system. As proper trade-off is inevitably essential between achievable performance and required hardware, researchers have sought a number of artificially intelligent optimization techniques to solve it out. Since such a technique generally involves variety of constructional alternatives, appropriate use of correct option demands justified attention. Numerous evolutionary computation techniques, being a branch of biologically inspired optimization process, are being increasingly used for a number of signal processing applications of late. This paper throws enough light to select the most suitable mutation strategy of Differential Evolution (DE algorithm for efficient design of multiplier-less low-pass finite duration impulse response (FIR filter. Computationally efficient mutation scheme has been identified by observing convergence behavior and error histogram plot for different alternatives. Performance of the designed filter has been compared in terms of its magnitude response and the requirement of various hardware blocks for four different lengths of the filter. Consequently the name of the most favorable mutation rule has been suggested upon analyzing all the factors. Finally the supremacy of our proposed design has been established by comparing its performance with that of other state-of-the-art multiplier-less low-pass FIR filters.
Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means
Chaudhury, Kunal N
2012-01-01
A direct implementation of the bilateral filter [1] requires O(\\sigma_s^2) operations per pixel, where \\sigma_s is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter was recently proposed in [2] that required O(1) operations per pixel with respect to \\sigma_s. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated in [3] that this idea could be extended to a larger class of filters, including the popular non-local means filter [4]. As already observed in [2], a flip side of this approach was that the run time depended on the width \\sigma_r of the range kernel. For an image with (local) intensity variations in the range [0,T], the run time scaled as O(T^2/\\sigma^2_r) with \\sigma_r. This made it difficult t...
Reconstruction-plane-dependent weighted FDK algorithm for cone beam volumetric CT
Tang, Xiangyang; Hsieh, Jiang
2005-04-01
The original FDK algorithm has been extensively employed in medical and industrial imaging applications. With an increased cone angle, cone beam (CB) artifacts in images reconstructed by the original FDK algorithm deteriorate, since the circular trajectory does not satisfy the so-called data sufficiency condition (DSC). A few "circular plus" trajectories have been proposed in the past to reduce CB artifacts by meeting the DSC. However, the circular trajectory has distinct advantages over other scanning trajectories in practical CT imaging, such as cardiac, vascular and perfusion applications. In addition to looking into the DSC, another insight into the CB artifacts of the original FDK algorithm is the inconsistency between conjugate rays that are 180° apart in view angle. The inconsistence between conjugate rays is pixel dependent, i.e., it varies dramatically over pixels within the image plane to be reconstructed. However, the original FDK algorithm treats all conjugate rays equally, resulting in CB artifacts that can be avoided if appropriate view weighting strategy is exercised. In this paper, a modified FDK algorithm is proposed, along with an experimental evaluation and verification, in which the helical body phantom and a humanoid head phantom scanned by a volumetric CT (64 x 0.625 mm) are utilized. Without extra trajectories supplemental to the circular trajectory, the modified FDK algorithm applies reconstruction-plane-dependent view weighting on projection data before 3D backprojection, which reduces the inconsistency between conjugate rays by suppressing the contribution of one of the conjugate rays with a larger cone angle. Both computer-simulated and real phantom studies show that, up to a moderate cone angle, the CB artifacts can be substantially suppressed by the modified FDK algorithm, while advantages of the original FDK algorithm, such as the filtered backprojection algorithm structure, 1D ramp filtering, and data manipulation efficiency, can be
Zeng, Bangze; Zhu, Youpan; Li, Zemin; Hu, Dechao; Luo, Lin; Zhao, Deli; Huang, Juan
2014-11-01
Duo to infrared image with low contrast, big noise and unclear visual effect, target is very difficult to observed and identified. This paper presents an improved infrared image detail enhancement algorithm based on adaptive histogram statistical stretching and gradient filtering (AHSS-GF). Based on the fact that the human eyes are very sensitive to the edges and lines, the author proposed to extract the details and textures by using the gradient filtering. New histogram could be acquired by calculating the sum of original histogram based on fixed window. With the minimum value for cut-off point, author carried on histogram statistical stretching. After the proper weights given to the details and background, the detail-enhanced results could be acquired finally. The results indicate image contrast could be improved and the details and textures could be enhanced effectively as well.
A Kalman filter-based short baseline RTK algorithm for single-frequency combination of GPS and BDS.
Zhao, Sihao; Cui, Xiaowei; Guan, Feng; Lu, Mingquan
2014-08-20
The emerging Global Navigation Satellite Systems (GNSS) including the BeiDou Navigation Satellite System (BDS) offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK) algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.
A Kalman Filter-Based Short Baseline RTK Algorithm for Single-Frequency Combination of GPS and BDS
Directory of Open Access Journals (Sweden)
Sihao Zhao
2014-08-01
Full Text Available The emerging Global Navigation Satellite Systems (GNSS including the BeiDou Navigation Satellite System (BDS offer more visible satellites for positioning users. To employ those new satellites in a real-time kinematic (RTK algorithm to enhance positioning precision and availability, a data processing model for the dual constellation of GPS and BDS is proposed and analyzed. A Kalman filter-based algorithm is developed to estimate the float ambiguities for short baseline scenarios. The entire work process of the high-precision algorithm based on the proposed model is deeply investigated in detail. The model is validated with real GPS and BDS data recorded from one zero and two short baseline experiments. Results show that the proposed algorithm can generate fixed baseline output with the same precision level as that of either a single GPS or BDS RTK algorithm. The significantly improved fixed rate and time to first fix of the proposed method demonstrates a better availability and effectiveness on processing multi-GNSSs.
基于Hadoop平台协同过滤推荐算法①%Hadoop-Based Collaborative Filtering Recommendation Algorithm
Institute of Scientific and Technical Information of China (English)
杨志文; 刘波
2013-01-01
In order to solve data sparsity and scalability of the Collaborative Filtering (CF) recommendation algorithm when the volume of the dataset is very large. After deeply analyzing the Hadoop distributed computing platform and the characteristic of Collaborative Filtering recommendation algorithm, the paper propose a optimization scheme on Hadoop platform. The experimental results show that it can effectively improve the execution efficiency of Collaborative Filtering recommendation algorithm in large data size, when it is realized by MapReduce with Hbase database on the Hadoop platform.And then, it contribute to build one recommendation system which is low cost, high-performance and dynamic scalability.% 针对协同过滤推荐算法在数据稀疏性及在大数据规模下系统可扩展性的两个问题，在分析研究 Hadoop分布式平台与协同过滤推荐算法后，提出了一种基于Hadoop平台实现协同过滤推荐算法的优化方案。实验证明，在Hadoop平台上通过MapReduce结合Hbase数据库实现算法，能够有效地提高协同过滤推荐算法在大数据规模下的执行效率，从而能够进一步地搭建低成本高性能、动态扩展的分布式推荐引擎。
Directory of Open Access Journals (Sweden)
T.S. Udhaya Suriya
2014-03-01
Full Text Available The MAC architecture is used in real time digital s ignal processing and multimedia information processing which requires high throughput. A novel method to estimate the transition activity at the nodes of a multiplier accumulator architecture based on modified booth algorithm implementing finite impulse response filter is prop osed in this paper. The input signals are described by a stationary Gaussian process and the transition activity per bit of a signal word is modeled according to the dual bit type (DBT model. This estimation is based on the mathematical formulation by multiplexing mechanism on the breakpoints of the DBT model.
Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering
Institute of Scientific and Technical Information of China (English)
Zhang Weipeng
2013-01-01
In order to preferably identify infrared image of refuge chamber,reduce image noises of refuge chamber and retain more image details,we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising.First,the wavelet transform is adopted to decompose the image of refuge chamber,of which low frequency component remains unchanged.Then,three high-frequency components are treated by bilateral filtering,and the image is reconstructed.The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image,while providing better visual effect.This is superior to using either bilateral filtering or wavelet transform alone.It is useful for perfecting emergency refuge system of coal.
A digital algorithm for spectral deconvolution with noise filtering and peak picking: NOFIPP-DECON
Edwards, T. R.; Settle, G. L.; Knight, R. D.
1975-01-01
Noise-filtering, peak-picking deconvolution software incorporates multiple convoluted convolute integers and multiparameter optimization pattern search. The two theories are described and three aspects of the software package are discussed in detail. Noise-filtering deconvolution was applied to a number of experimental cases ranging from noisy, nondispersive X-ray analyzer data to very noisy photoelectric polarimeter data. Comparisons were made with published infrared data, and a man-machine interactive language has evolved for assisting in very difficult cases. A modified version of the program is being used for routine preprocessing of mass spectral and gas chromatographic data.
Institute of Scientific and Technical Information of China (English)
WANG Ke; HUANG Zhi; ZHONG Zhihua
2014-01-01
Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the AdaBoost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.
数字全息技术中散斑噪声滤波算法比较%Comparison of algorithms for filtering speckle noise in digital holography
Institute of Scientific and Technical Information of China (English)
潘云; 潘卫清; 晁明举
2011-01-01
In the recording process of digital holographic measurement, the hologram is easily polluted by speckle noise, which may decrease the resolution of the hologram. In addition, the reconstructed effect is seriously affected by speckle noise in digital reconstruction. Thus it is important to study the filtering speckle algorithms for digital hologram. The median filtering algorithm, Lee filtering algorithm, Kuan filtering algorithm and SUSAN filtering algorithm were introduced to filter the speckle noise in hologram and reconstructed image. Then these algorithms were compared. The results showed that the SUSAN filtering algorithm was better in digital holographic technology. The speckle noises were suppressed significantly and the information of reconstructed images were well maintained.%在数字全息测量记录过程中,其所记录的全息图易受到散斑噪声的污染造成分辨率下降,同时也严重影响数字全息再现的效果,因此研究适用于数字全息技术中散斑滤波的算法具有重要的实用价值.介绍了中值滤波、Lee滤波、Kuan滤波和SUSAN滤波这四种常用的散斑滤波算法,并将它们运用于数字全息实验所记录图像和数字再现图像的散斑噪声滤波处理中,然后对这四种算法的处理结果进行评价.结果表明,在数字全息技术中使用SUSAN滤波算法进行处理,既明显抑制了散斑噪声,又有效保证了再现图像信息的完整性.
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.
Zhang, De-Jia
2009-07-01
With the fast development of Internet, many systems have emerged in e-commerce applications to support the product recommendation. Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, with the adding of user and item scales, user-item ratings are becoming extremely sparsity and recommender systems utilizing traditional collaborative filtering are facing serious challenges. To address the issue, this paper presents an approach to compute item genre similarity, through mapping each item with a corresponding descriptive genre, and computing similarity between genres as similarity, then make basic predictions according to those similarities to lower sparsity of the user-item ratings. After that, item-based collaborative filtering steps are taken to generate predictions. Compared with previous methods, the presented collaborative filtering employs the item genre similarity can alleviate the sparsity issue in the recommender systems, and can improve accuracy of recommendation.
Tomographic reconstructions using map algorithms - application to the SPIDR mission
Energy Technology Data Exchange (ETDEWEB)
Ghosh Roy, D.N.; Wilton, K.; Cook, T.A.; Chakrabarti, S.; Qi, J.; Gullberg, G.T.
2004-01-21
The spectral image of an astronomical scene is reconstructed from noisy tomographic projections using maximum a posteriori (MAP) and filtered backprojection (FBP) algorithms. Both maximum entropy (ME) and Gibbs prior are used in the MAP reconstructions. The scene, which is a uniform background with a localized emissive source superimposed on it, is reconstructed for a broad range of source counts. The algorithms are compared regarding their ability to detect the source in the background. Detectability is defined in terms of a contrast-to-noise ratio (CNR) which is a Monte Carlo ensemble average of spatially averaged CNRs for the individual reconstructions. Overall, MAP was found to yield improved CNR relative to FBP. Moreover, as a function of the total source counts, the CNR varies distinctly different for source and background regions. This may be important in separating a weak source from the background.
Directory of Open Access Journals (Sweden)
Wen-Chang Cheng
2012-12-01
Full Text Available In this paper we propose a robust lane detection and tracking method by combining particle filters with the particle swarm optimization method. This method mainly uses the particle filters to detect and track the local optimum of the lane model in the input image and then seeks the global optimal solution of the lane model by a particle swarm optimization method. The particle filter can effectively complete lane detection and tracking in complicated or variable lane environments. However, the result obtained is usually a local optimal system status rather than the global optimal system status. Thus, the particle swarm optimization method is used to further refine the global optimal system status in all system statuses. Since the particle swarm optimization method is a global optimization algorithm based on iterative computing, it can find the global optimal lane model by simulating the food finding way of fish school or insects under the mutual cooperation of all particles. In verification testing, the test environments included highways and ordinary roads as well as straight and curved lanes, uphill and downhill lanes, lane changes, etc. Our proposed method can complete the lane detection and tracking more accurately and effectively then existing options.
Cheng, Xuemin; Hao, Qun; Xie, Mengdi
2016-04-07
Video stabilization is an important technology for removing undesired motion in videos. This paper presents a comprehensive motion estimation method for electronic image stabilization techniques, integrating the speeded up robust features (SURF) algorithm, modified random sample consensus (RANSAC), and the Kalman filter, and also taking camera scaling and conventional camera translation and rotation into full consideration. Using SURF in sub-pixel space, feature points were located and then matched. The false matched points were removed by modified RANSAC. Global motion was estimated by using the feature points and modified cascading parameters, which reduced the accumulated errors in a series of frames and improved the peak signal to noise ratio (PSNR) by 8.2 dB. A specific Kalman filter model was established by considering the movement and scaling of scenes. Finally, video stabilization was achieved with filtered motion parameters using the modified adjacent frame compensation. The experimental results proved that the target images were stabilized even when the vibrating amplitudes of the video become increasingly large.
Jiang, Qingan; Wu, Wenqi; Jiang, Mingming; Li, Yun
2017-06-19
High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ) when using a Ring Laser Gyroscope (RLG)-based Inertial Measurement Unit (IMU) with gyro bias instability of 0.03°/h and random walk noise of 0.005 °h while control points of the track control network (CPIII) position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying.
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.
Directory of Open Access Journals (Sweden)
Xin Li
2016-06-01
Full Text Available The problem of heading drift error using only low cost Micro-Electro-Mechanical (MEMS Inertial-Measurement-Unit (IMU has not been well solved. In this paper, a heading estimation method with real-time compensation based on Kalman filter has been proposed, abbreviated as KHD. For the KHD method, a unified heading error model is established for various predictable errors in magnetic compass for pedestrian navigation, and an effective method for solving the model parameters is proposed in the indoor environment with regular structure. In addition, error model parameters are solved by Kalman filtering algorithm with building geometry information in order to achieve real-time heading compensation. The experimental results show that the KHD method can not only effectively correct the original heading information, but also effectively inhibit the accumulation effect of positioning errors. The performance observed in a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI building on the China University of Mining and Technology (CUMT campus confirms that apply KHD method to PDR(Pedestrian Dead Reckoning algorithm can reliably achieve meter-level positioning using a low cost MEMS IMU only.
Directory of Open Access Journals (Sweden)
Qingan Jiang
2017-06-01
Full Text Available High-accuracy railway track surveying is essential for railway construction and maintenance. The traditional approaches based on total station equipment are not efficient enough since high precision surveying frequently needs static measurements. This paper proposes a new filtering and smoothing algorithm based on the IMU/odometer and landmarks integration for the railway track surveying. In order to overcome the difficulty of estimating too many error parameters with too few landmark observations, a new model with completely observable error states is established by combining error terms of the system. Based on covariance analysis, the analytical relationship between the railway track surveying accuracy requirements and equivalent gyro drifts including bias instability and random walk noise are established. Experiment results show that the accuracy of the new filtering and smoothing algorithm for railway track surveying can reach 1 mm (1σ when using a Ring Laser Gyroscope (RLG-based Inertial Measurement Unit (IMU with gyro bias instability of 0.03°/h and random walk noise of 0.005 °h while control points of the track control network (CPIII position observations are provided by the optical total station in about every 60 m interval. The proposed approach can satisfy at the same time the demands of high accuracy and work efficiency for railway track surveying.
Meghoufel, Ali; Cloutier, Guy; Crevier-Denoix, Nathalie; de Guise, Jacques A
2011-03-01
The fiber bundle density (FBD) calculated from ultrasound B-scan images of the equine superficial digital flexor tendon (SDFT) can serve as an objective measurement to characterize the three metacarpal sites of normal SDFTs, and also to discriminate a healthy SDFT from an injured one. In this paper, we propose a shock filter algorithm for the thinning of hyper-echoic structures observed in B-scan images of the SDFT. This algorithm is further enhanced by applying closing morphological operations on filtered images to facilitate extraction and quantification of fiber bundle fascicles. The mean FBD values were calculated from a clinical B-scan image dataset of eight normal and five injured SDFTs. The FBD values measured at three different tendon sites in normal cases show a highest density on the proximal site (five cases out of eight) and a lowest value on the distal part (seven cases out of eight). The mean FBD values measured on the entire tendon from the whole B-scan image dataset show a significant difference between normal and injured SDFTs: 51 (±9) for the normal SDFTs and 39 (±7) for the injured ones (p = 0.004) . This difference likely indicates disruption of some fiber fascicle bundles where lesions occurred. To conclude, the potential of this imaging technique is shown to be efficient for anatomical structural SDFT characterizations, and opens the way to clinically identifying the integrity of SDFTs.
Labunets, Valeri G.; Labunets-Rundblad, Ekaterina V.; Astola, Jaakko T.
2001-12-01
Fast algorithms for a wide class of non-separable n-dimensional (nD) discrete unitary K-transforms (DKT) are introduced. They need less 1D DKTs than in the case of the classical radix-2 FFT-type approach. The method utilizes a decomposition of the nD K-transform into the product of a new nD discrete Radon transform and of a set of parallel/independ 1D K-transforms. If the nD K-transform has a separable kernel (e.g., the case of the discrete Fourier transform) our approach leads to decrease of multiplicative complexity by the factor of n comparing to the classical row/column separable approach. It is well known that an n-th order Volterra filter of one dimensional signal can be evaluated by an appropriate nD linear convolution. This work describes new superfast algorithm for Volterra filtering. New approach is based on the superfast discrete Radon and Nussbaumer polynomial transforms.
Institute of Scientific and Technical Information of China (English)
YU Zhi-jun; WEI Jian-ming; LIU Hai-tao
2009-01-01
Target tracking is one of the most important applications of wireless sensor networks. Optimized computation and energy dissipation are critical requirements to save the limited resource of sensor nodes. A new robust and energy-efficient collaborative target tracking framework is proposed in this article. After a target is detected, only one active cluster is responsible for the tracking task at each time step. The tracking algorithm is distributed by passing the sensing and computation operations from one cluster to another. An event-driven cluster reforming scheme is also proposed for balancing energy consumption among nodes. Observations from three cluster members are chosen and a new class of particle filter termed cost-reference particle filter (CRPF) is introduced to estimate the target motion at the cluster head. This CRPF method is quite robust for wireless sensor network tracking applications because it drops the strong assumptions of knowing the probability distributions of the system process and observation noises. In simulation experiments, the performance of the proposed collaborative target tracking algorithm is evaluated by the metrics of tracking precision and network energy consumption.
Wang, Dun; Takeuchi, Nozomu; Kawakatsu, Hitoshi; Mori, Jim
2017-04-01
With the recent establishment of regional dense seismic arrays (e.g., Hi-net in Japan, USArray in the North America), advanced digital data processing has enabled improvement of back-projection methods that have become popular and are widely used to track the rupture process of moderate to large earthquakes. Back-projection methods can be classified into two groups, one using time domain analyses, and the other frequency domain analyses. There are minor technique differences in both groups. Here we focus on the back-projection performed in the time domain using seismic waveforms recorded at teleseismic distances (30-90 degree). For the standard back-projection (Ishii et al., 2005), teleseismic P waves that are recorded on vertical components of a dense seismic array are analyzed. Since seismic arrays have limited resolutions and we make several assumptions (e.g., only direct P waves at the observed waveforms, and every trace has completely identical waveform), the final images from back-projections show the stacked amplitudes (or correlation coefficients) that are often smeared in both time and space domains. Although it might not be difficult to reveal overall source processes for a giant seismic source such as the 2004 Mw 9.0 Sumatra earthquake where the source extent is about 1400 km (Ishii et al., 2005; Krüger and Ohrnberger, 2005), there are more problems in imaging detailed processes of earthquakes with smaller source dimensions, such as a M 7.5 earthquake with a source extent of 100-150 km. For smaller earthquakes, it is more difficult to resolve space distributions of the radiated energies. We developed a new inversion method, Image Deconvolution Back-Projection (IDBP) to determine the sources of high frequency energy radiation by linear inversion of observed images from a back-projection approach. The observed back-projection image for multiple sources is considered as a convolution of the image of the true radiated energy and the array response for a
Xu, Shaoping; Hu, Lingyan; Yang, Xiaohui
2016-01-01
The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising algorithms (so-called nonblind denoising algorithms), which largely limits the applicability of these nonblind denoising algorithms in many applications. Moreover, these nonblind algorithms do not always achieve the best denoised images in visual quality even when fed with the actual noise level parameter. To address these shortcomings, in this paper we propose a new quality-aware features-based noise level estimator (NLE), which consists of quality-aware features extraction and optimal noise level parameter prediction. First, considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we utilize the marginal statistics of two local contrast operators, i.e., the gradient magnitude and the Laplacian of Gaussian (LOG), to extract quality-aware features. The proposed quality-aware features have very low computational complexity, making them well suited for time-constrained applications. Then we propose a learning-based framework where the noise level parameter is estimated based on the quality-aware features. Based on the proposed NLE, we develop a blind block matching and three-dimensional filtering (BBM3D) denoising algorithm which is capable of effectively removing additive white Gaussian noise, even coupled with impulse noise. The noise level parameter of the BBM3D algorithm is automatically tuned according to the quality-aware features, guaranteeing the best performance. As such, the classical block matching and three-dimensional algorithm can be transformed into a blind one in an unsupervised manner. Experimental results demonstrate that the
Anghel, Adela; Carrano, Charles; Komjathy, Attila; Astilean, Adina; Letia, Tiberiu
2009-01-01
Data collected from a GPS receiver located at low latitudes in the American sector are used to investigate the performance of the WinTEC algorithm [Anghel et al., 2008a, Kalman filter-based algorithm for near realtime monitoring of the ionosphere using dual frequency GPS data. GPS Solutions, accepted for publication; for different ionospheric modeling techniques: the single-shell linear, quadratic, and cubic approaches, and the multi-shell linear approach. Our results indicate that the quadratic and cubic approaches perform much better than the single-shell and multi-shell linear approaches in terms of post-fit residuals. The performance of the algorithm for the cubic approach is then further tested by comparing the vertical TEC predicted by WinTEC and USTEC [Spencer et al., 2004. Ionospheric data assimilation methods for geodetic applications. In: Proceedings of IEEE PLANS, Monterey, CA, 26-29 April, pp. 510-517] at five North American stations. In addition, since the GPS-derived total electron content (TEC) contains contributions from both ionospheric and plasmaspheric sections of the GPS ray paths, in an effort to improve the accuracy of the TEC retrievals, a new data assimilation module that uses background information from an empirical plasmaspheric model [Gallagher et al., 1988. An empirical model of the Earth's plasmasphere. Advances in Space Research 8, (8)15-(8)24] has been incorporated into the WinTEC algorithm. The new Kalman filter-based algorithm estimates both the ionospheric and plasmaspheric electron contents, the combined satellite and receiver biases, and the estimation error covariance matrix, in a single-site or network solution. To evaluate the effect of the plasmaspheric component on the estimated biases and total TEC and to assess the performance of the newly developed algorithm, we compare the WinTEC results, with and without the plasmaspheric term included, at three GPS receivers located at different latitudes in the American sector, during
Institute of Scientific and Technical Information of China (English)
许光辉; 胡光锐
2005-01-01
A new Kalman filtering algorithm based on estimation of spread spectrum signal before suppression of narrowband interference (NBI) in spread spectrum systems, using the dependence of autoregressive (AR) interference, is presented compared with performance of the ACM nonlinear filtering algorithm, simulation results show that the proposed algorithm has preferable performance, there is about 5 dB SNR improvement in average.
一种基于正切图像处理(TIP)模型的图像滤波算法%Image Filtering Algorithm Based on TIP Model
Institute of Scientific and Technical Information of China (English)
康牧; 李永亮
2012-01-01
Few image details will be lost when noise is filtered in traditional image filtering algorithm. This article put forward an image filtering algorithm based on tangent image process model. This algorithm filters image by using tangent function and arctangent function according to 3 × 3 neighborhood pixel value of the pixel to be detected. It is simple, and easy to be implemented. Details of image edge and corner can be preserved and enhanced when noise is restrained effectively. The experiment shows that this algorithm is obviously better than other image filtering algorithm.%传统的图像滤波算法在滤除噪声的同时会丢失一些图像的细节信息,使图像变得模糊,为此提出了一种基于正切图像处理模型的图像滤波算法,算法根据待检测像素周围3×3邻域的像素值,利用正切函数和反正切函数进行处理.算法简单、容易实现,能够在有效地抑制噪声的同时,增强和保留图像的边缘和角点等细节信息.通过实验比较可知,该算法明显优于其它图像滤波算法.
Tracking Infection Diffusion in Social Networks: Filtering Algorithms and Threshold Bounds
Krishnamurthy, Vikram; Pedersen, Tavis
2016-01-01
This paper deals with the statistical signal pro- cessing over graphs for tracking infection diffusion in social networks. Infection (or Information) diffusion is modeled using the Susceptible-Infected-Susceptible (SIS) model. Mean field approximation is employed to approximate the discrete valued infected degree distribution evolution by a deterministic ordinary differential equation for obtaining a generative model for the infection diffusion. The infected degree distribution is shown to follow polynomial dynamics and is estimated using an exact non- linear Bayesian filter. We compute posterior Cramer-Rao bounds to obtain the fundamental limits of the filter which depend on the structure of the network. Considering the time-varying nature of the real world networks, the relationship between the diffusion thresholds and the degree distribution is investigated using generative models for real world networks. In addition, we validate the efficacy of our method with the diffusion data from a real-world online s...
Adaptive Command-Filtered Backstepping Control for Linear Induction Motor via Projection Algorithm
Directory of Open Access Journals (Sweden)
Wenxu Yan
2016-01-01
Full Text Available A theoretical framework of the position control for linear induction motors (LIM has been proposed. First, indirect field-oriented control of LIM is described. Then, the backstepping approach is used to ensure the convergence and robustness of the proposed control scheme against the external time-varying disturbances via Lyapunov stability theory. At the same time, in order to solve the differential expansion and the control saturation problems in the traditional backstepping, command filter is designed in the control and compensating signals are presented to eliminate the influence of the errors caused by command filters. Next, unknown total mass of the mover, viscous friction, and load disturbances are estimated by the projection-based adaptive law which bounds the estimated function and simultaneously guarantees the robustness of the proposed controller against the parameter uncertainties. Finally, simulation results are given to illustrate the validity and potential of the designed control scheme.
A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise
Directory of Open Access Journals (Sweden)
Young-Seok Choi
2014-01-01
Full Text Available This paper presents a novel subband adaptive filter (SAF for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function, the proposed l0-norm sign SAF (l0-SSAF achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed l0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.
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Abhijit Chandra
2012-04-01
Full Text Available Reduction of computational complexity of digital hardware has drawn the special attention of researchers in recent past. Proper emphasis is needed in this regard towards the settlement of computationally efficient as well as functionally competent design of digital systems. In this communication, we have made one novel attempt for designing multiplier-free Finite duration Impulse Response (FIR digital filter using one robust evolutionary optimization technique, called Differential Evolution (DE. The search has been directed through two sequentially opposite paths which include quantization and optimization as fundamental operations. Besides performing a detailed comparative analysis between these two proposed approaches; the performance evaluation of the designed filter with other existing discrete coefficient FIR models has also been carried out. Finally, the optimum search method for realizing the required set of specifications has been suggested.
Zheng, Jian; Lu, Pei-Rong; Xiang, Dehui; Dai, Ya-Kang; Liu, Zhao-Bang; Kuai, Duo-Jie; Xue, Hui; Yang, Yue-Tao
2013-01-01
We propose a new method to enhance and extract the retinal vessels. First, we employ a multiscale Hessian-based filter to compute the maximum response of vessel likeness function for each pixel. By this step, blood vessels of different widths are significantly enhanced. Then, we adopt a nonlocal mean filter to suppress the noise of enhanced image and maintain the vessel information at the same time. After that, a radial gradient symmetry transformation is adopted to suppress the nonvessel structures. Finally, an accurate graph-cut segmentation step is performed using the result of previous symmetry transformation as an initial. We test the proposed approach on the publicly available databases: DRIVE. The experimental results show that our method is quite effective.
Katyal, Vini; Srivastava, Deepesh
2012-01-01
This paper focuses on fruit defect detection and glare removal using morphological operations, Glare removal can be considered as an important preprocessing step as uneven lighting may introduce it in images, which hamper the results produced through segmentation by Gabor filters .The problem of glare in images is very pronounced sometimes due to the unusual reflectance from the camera sensor or stray light entering, this method counteracts this problem and makes the defect detection much mor...
Energy Technology Data Exchange (ETDEWEB)
Park, Yeonok; Park, Chulkyu; Cho, Hyosung; Je, Uikyu; Hong, Daeki; Lee, Minsik; Cho, Heemoon; Choi, Sungil; Koo, Yangseo [Yonsei University, Wonju (Korea, Republic of)
2014-09-15
Digital breast tomosynthesis (DBT) is considered in clinics as a standard three-dimensional imaging modality, allowing the earlier detection of cancer. It typically acquires only 10-30 projections over a limited angle range of 15 - 60 .deg. with a stationary detector and typically uses a computationally-efficient filtered-backprojection (FBP) algorithm for image reconstruction. However, a common FBP algorithm yields poor image quality resulting from the loss of average image value and the presence of severe image artifacts due to the elimination of the dc component of the image by the ramp filter and to the incomplete data, respectively. As an alternative, iterative reconstruction methods are often used in DBT to overcome these difficulties, even though they are still computationally expensive. In this study, as a compromise, we considered a projection-angle dependent filtering method in which one-dimensional geometry-adapted filter kernels are computed with the aid of a conjugate-gradient method and are incorporated into the standard FBP framework. We implemented the proposed algorithm and performed systematic simulation works to investigate the imaging characteristics. Our results indicate that the proposed method is superior to a conventional FBP method for DBT imaging and has a comparable computational cost, while preserving good image homogeneity and edge sharpening with no serious image artifacts.
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.
Zheng, Ziyi; Sun, Mingshan; Pavkovich, John; Star-Lack, Josh
2011-03-01
A challenge in using on-board cone beam computed tomography (CBCT) to image lung tumor motion prior to radiation therapy treatment is acquiring and reconstructing high quality 4D images in a sufficiently short time for practical use. For the 1 minute rotation times typical of Linacs, severe view aliasing artifacts, including streaks, are created if a conventional phase-correlated FDK reconstruction is performed. The McKinnon-Bates (MKB) algorithm provides an efficient means of reducing streaks from static tissue but can suffer from low SNR and other artifacts due to data truncation and noise. We have added truncation correction and bilateral nonlinear filtering to the MKB algorithm to reduce streaking and improve image quality. The modified MKB algorithm was implemented on a graphical processing unit (GPU) to maximize efficiency. Results show that a nearly 4x improvement in SNR is obtained compared to the conventional FDK phase-correlated reconstruction and that high quality 4D images with 0.4 second temporal resolution and 1 mm3 isotropic spatial resolution can be reconstructed in less than 20 seconds after data acquisition completes.
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.
Directory of Open Access Journals (Sweden)
Gilson Alexandre Pinto
2005-06-01
Full Text Available This work presented the results of the implementation of an off-line smoothing algorithm in the monitoring system, for the partial hydrolysis of cheese whey proteins using enzymes, which used penalized least squares. Different algorithms for on-line signals filtering used by the control were also compared: artificial neural networks, moving average and smoothing algorithm.A hidrólise parcial de proteínas do soro de queijo, realizada por enzimas imobilizadas em suporte inerte, pode alterar ou evidenciar propriedades funcionais dos polipeptídeos produzidos, aumentando assim suas aplicações. O controle do pH do reator de proteólise é de fundamental importância para modular a distribuição de pesos moleculares dos peptídeos formados. Os sinais de pH e temperatura utilizados pelo algoritmo de controle e inferência de estado podem estar sujeitos a ruído considerável, tornando importante sua filtragem. Apresentam-se aqui resultados da implementação, no sistema de monitoramento do processo, de algoritmo suavizador, que utiliza mínimos quadrados com penalização para o pós-tratamento dos dados. Compara-se ainda o desempenho de diferentes algoritmos na filtragem em tempo real dos sinais utilizados pelo sistema de controle, a saber: redes neurais artificiais, média móvel e o sobredito suavizador.
Directory of Open Access Journals (Sweden)
Jian Wang
2015-11-01
Full Text Available In this paper, a scheme is presented for fusing a foot-mounted Inertial Measurement Unit (IMU and a floor map to provide ubiquitous positioning in a number of settings, such as in a supermarket as a shopping guide, in a fire emergency service for navigation, or with a hospital patient to be tracked. First, several Zero-Velocity Detection (ZDET algorithms are compared and discussed when used in the static detection of a pedestrian. By introducing information on the Zero Velocity of the pedestrian, fused with a magnetometer measurement, an improved Pedestrian Dead Reckoning (PDR model is developed to constrain the accumulating errors associated with the PDR positioning. Second, a Correlation Matching Algorithm based on map projection (CMAP is presented, and a zone division of a floor map is demonstrated for fusion of the PDR algorithm. Finally, in order to use the dynamic characteristics of a pedestrian’s trajectory, the Adaptive Unscented Kalman Filter (A-UKF is applied to tightly integrate the IMU, magnetometers and floor map for ubiquitous positioning. The results of a field experiment performed on the fourth floor of the School of Environmental Science and Spatial Informatics (SESSI building on the China University of Mining and Technology (CUMT campus confirm that the proposed scheme can reliably achieve meter-level positioning.
A Practical Reconstruction Algorithm in 2D Industrial CT%一种实用的二维工业CT重建算法
Institute of Scientific and Technical Information of China (English)
李慧; 田捷; 张兆田
2005-01-01
Computed tomography plays an important role in industrial non-destructive testing, medical applications, astronomy and many other fields to look inside the scanned object and to analysis its inner structures. A non-destructive testing software have been developed to efficiently detect inner flaws of space industrial components. As the core of our software, reconstruction algorithms including preprocess of raw data, re-arrange algorithm and filtered back-projection algorithms have been described in detail in this article. With real raw data from CASC of China, experimental results verified the applied reconstruction algorithm in our software. Furthermore, forward algorithms simulating generation of fan-beam raw data are also presented in this article.
Kudo, Hiroyuki; Nemoto, Takuya; Takaki, Keita
2016-01-01
This paper concerns iterative reconstruction for low-dose and few-view CT by minimizing a data-fidelity term regularized with the Total Variation (TV) penalty. We propose a very fast iterative algorithm to solve this problem. The algorithm derivation is outlined as follows. First, the original minimization problem is reformulated into the saddle point (primal-dual) problem by using the Lagrangian duality, to which we apply the first-order primal-dual iterative methods. Second, we precondition the iteration formula using the ramp flter of Filtered Backprojection (FBP) reconstruction algorithm in such a way that the problem solution is not altered. The resulting algorithm resembles the structure of so-called iterative FBP algorithm, and it converges to the exact minimizer of cost function very fast.
Values in the filter bubble Ethics of Personalization Algorithms in Cloud Computing
Bozdag, V.E.; Timmermans, J.F.C.
2001-01-01
Cloud services such as Facebook and Google search started to use personalization algorithms in order to deal with growing amount of data online. This is often done in order to reduce the “information overload”. User’s interaction with the system is recorded in a single identity, and the information
Galvan, Jose Ramon; Saxena, Abhinav; Goebel, Kai Frank
2012-01-01
This article discusses several aspects of uncertainty representation and management for model-based prognostics methodologies based on our experience with Kalman Filters when applied to prognostics for electronics components. In particular, it explores the implications of modeling remaining useful life prediction as a stochastic process, and how it relates to uncertainty representation, management and the role of prognostics in decision-making. A distinction between the interpretations of estimated remaining useful life probability density function is explained and a cautionary argument is provided against mixing interpretations for two while considering prognostics in making critical decisions.
RECURSIVE FILTERING RADON-AMBIGUITY TRANSFORM ALGORITHM FOR DETECTING MULTI-LFM SIGNALS
Institute of Scientific and Technical Information of China (English)
Li Yingxiang; Xiao Xianci
2003-01-01
In multi-LFM signal condition, Radon-Ambiguity Transform (RAT) of the strongLFM component has strong suppression effect on that of the weak LFM component. A methodnamed as Recursive Filtering RAT (RFRAT) Mgorithm is proposed for solving this problem. Byfully using of the Maximum Likelihood (ML) estimation value of the frequency modulation rategot by RAT. RFRAT can detect the noisy multi-LFM signals out step by step. The merit of thisnew method is validated by an illustrative example in low Signal-to-Noise-Ratio (SNR) condition.
基于BB-BC的粒子滤波算法研究%Particle Filter Based on BB-BC Algorithm
Institute of Scientific and Technical Information of China (English)
刘钊; 冯新喜; 鹿传国; 孔云波
2012-01-01
Particle filter is the major method of solving the issue of the nonlinear and non-Gaussian system. To overcome the impact of particle degeneration to the performance of particle filter, an intelligent particle filter algorithm based on the Big Bang-Big Crunch (BB-BC) optimization algorithm is proposed. By applying it into resampling, the particle degeneration problem can be resolved by iterative mechanism. The simulation result indicates that compared with standard particle filter, the BB-BC particle filter algorithm is simpler and has better filter effects.%粒子滤波是目前解决非线性、非高斯系统问题的主流方法,为克服粒子退化对粒子滤波性能的影响,提出了一种基于大爆炸-大坍塌(BB-BC)优化算法的智能化粒子滤波算法.将大爆炸-大坍塌优化算法应用于重采样,以迭代机制设计解决粒子退化问题.仿真结果表明,该算法与标准粒子滤波算法相比计算简单,滤波效果优于标准粒子滤波算法.
Design of jitter compensation algorithm for robot vision based on optical flow and Kalman filter.
Wang, B R; Jin, Y L; Shao, D L; Xu, Y
2014-01-01
Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.
A FAULT TOLERANT FPGA BASED IMAGE ENHANCEMENT FILTER USING SELF HEALING ALGORITHM
Directory of Open Access Journals (Sweden)
K.SRI RAMA KRISHNA,
2010-09-01
Full Text Available An original approach to automatic design of image filters is presented in this paper. The proposed solution employs Field Programmable Gate Array reconfigurable hardware at simplified functional level and produces high quality image when image features are corrupted by different types of noise. In addition, parallel architectures can be used to ease the enormous computational load due to different operations conducted on image data sets. Self healing circuit is the one which can compete against traditional designs in terms of quality and implementation cost in Xilinx’s chips. During the first phase, schemes for testing the configured processing elements of a reconfigurable circuit evolved for image enhancement application is presented. In the second phase, the internal Processing Elements in evolved circuit found faulty, they are restructured such that the sparse processing elements replace the faulty processing elements both functionally and structurally. Simulation results show that the evolved circuit is inherently testable and can restructure itself by avoiding the faulty ProcessingElements and make use of sparse ones. In third phase implantation of FPGA based image enhancement filter using Virtex-IV application board.
Design of Jitter Compensation Algorithm for Robot Vision Based on Optical Flow and Kalman Filter
Directory of Open Access Journals (Sweden)
B. R. Wang
2014-01-01
Full Text Available Image jitters occur in the video of the autonomous robot moving on bricks road, which will reduce robot operation precision based on vision. In order to compensate the image jitters, the affine transformation kinematics were established for obtaining the six image motion parameters. The feature point pair detecting method was designed based on Eigen-value of the feature windows gradient matrix, and the motion parameters equation was solved using the least square method and the matching point pairs got based on the optical flow. The condition number of coefficient matrix was proposed to quantificationally analyse the effect of matching errors on parameters solving errors. Kalman filter was adopted to smooth image motion parameters. Computing cases show that more point pairs are beneficial for getting more precise motion parameters. The integrated jitters compensation software was developed with feature points detecting in subwindow. And practical experiments were conducted on two mobile robots. Results show that the compensation costing time is less than frame sample time and Kalman filter is valid for robot vision jitters compensation.
Collaborative filtering algorithm with stepwise prediction%分步预测的协同过滤算法
Institute of Scientific and Technical Information of China (English)
肖明波; 郑鑫炜
2015-01-01
The collaborative filtering recommendation algorithm has the problem of data sparseness.In order to solve this problem,this paper put forward a new algorithm with stepwise prediction.It firstly preprocessed the scoring matrix:rearranged the location of the matrix elements to concentrate the values to the left upper corner and filled part of user’s missing data when it scored too less projects.Then it extracted a subsystem with high data density from scoring matrix and filled the missing va-lues by trust-based collaborative filtering algorithm.Finally it achieved stepwise prediction by constantly adding new user or new project.The experimental results on MovieLens demonstrate that the new algorithm can effectively alleviate the data sparseness problem and improve the accuracy.%针对数据稀疏性问题，对协同过滤推荐算法作了改进，提出分步预测的算法。算法先对评分矩阵作预处理，重新排列矩阵元素的位置，使评分数据集中到矩阵左上角，并对评分数过少的用户进行部分填充；然后再提取一个数据密度较高的子系统，用基于信任的算法填充其缺失值；最后通过不断向子系统里添加新用户、新项目的方法实现分步预测的目的。通过在 MovieLens 数据集上的实验结果表明，新算法可以有效地缓解数据稀疏性问题，提高系统的推荐精度。
Fallahi, Kia; Raoufi, Reza; Khoshbin, Hossein
2008-07-01
In recent years chaotic secure communication and chaos synchronization have received ever increasing attention. In this paper a chaotic communication method using extended Kalman filter is presented. The chaotic synchronization is implemented by EKF design in the presence of channel additive noise and processing noise. Encoding chaotic communication is used to achieve a satisfactory, typical secure communication scheme. In the proposed system, a multi-shift cipher algorithm is also used to enhance the security and the key cipher is chosen as one of the chaos states. The key estimate is employed to recover the primary data. To illustrate the effectiveness of the proposed scheme, a numerical example based on Chen dynamical system is presented and the results are compared to two other chaotic systems.
Shin, Yun-ho; Jang, Dong-doo; Moon, Seok-jun; Jung, Hyung-Jo; Moon, Yeong-jong; Song, Chang-kyu
2011-04-01
Recently, vibration requirements are getting stricter as precise equipments need more improved vibration environment to realize their powerful performance. Though the passive pneumatic vibration isolation tables are frequently used to satisfy the rigorous vibration requirements, the specific vibration problem, especially continuous sinusoidal or periodic vibration induced by a rotor system of other precise equipment, a thermo-hygrostat or a ventilation system, is still left. In this research, the application procedure of Filtered-X LMS algorithm to pneumatic vibration isolation table with piezo-stack actuators is proposed to enhance the isolation performance for the continuous sinusoidal or periodic vibration. In addition, the experimental results to show the isolation performance of proposed system are also presented together with the isolation performance of passive pneumatic isolation table.
Ayuk, R; Giovannini, H; Jost, A; Mudry, E; Girard, J; Mangeat, T; Sandeau, N; Heintzmann, R; Wicker, K; Belkebir, K; Sentenac, A
2013-11-15
Structured illumination microscopy (SIM) is a powerful technique for obtaining super-resolved fluorescence maps of samples, but it is very sensitive to aberrations or misalignments affecting the excitation patterns. Here, we present a reconstruction algorithm that is able to process SIM data even if the illuminations are strongly distorted. The approach is an extension of the recent blind-SIM technique, which reconstructs simultaneously the sample and the excitation patterns without a priori information on the latter. Our algorithm was checked on synthetic and experimental data using distorted and nondistorted illuminations. The reconstructions were similar to that obtained by up-to-date SIM methods when the illuminations were periodic and remained artifact-free when the illuminations were strongly distorted.
Rolling ball algorithm as a multitask filter for terrain conductivity measurements
Rashed, Mohamed
2016-09-01
Portable frequency domain electromagnetic devices, commonly known as terrain conductivity meters, have become increasingly popular in recent years, especially in locating underground utilities. Data collected using these devices, however, usually suffer from major problems such as complexity and interference of apparent conductivity anomalies, near edge local spikes, and fading of conductivity contrast between a utility and the surrounding soil. This study presents the experience of adopting the rolling ball algorithm, originally designed to remove background from medical images, to treat these major problems in terrain conductivity measurements. Applying the proposed procedure to data collected using different terrain conductivity meters at different locations and conditions proves the capability of the rolling ball algorithm to treat these data both efficiently and quickly.
Simulation and Performance Analysis of Adaptive Filtering Algorithms in Noise Cancellation
Ferdouse, Lilatul; Nipa, Tamanna Haque; Jaigirdar, Fariha Tasmin
2011-01-01
Noise problems in signals have gained huge attention due to the need of noise-free output signal in numerous communication systems. The principal of adaptive noise cancellation is to acquire an estimation of the unwanted interfering signal and subtract it from the corrupted signal. Noise cancellation operation is controlled adaptively with the target of achieving improved signal to noise ratio. This paper concentrates upon the analysis of adaptive noise canceller using Recursive Least Square (RLS), Fast Transversal Recursive Least Square (FTRLS) and Gradient Adaptive Lattice (GAL) algorithms. The performance analysis of the algorithms is done based on convergence behavior, convergence time, correlation coefficients and signal to noise ratio. After comparing all the simulated results we observed that GAL performs the best in noise cancellation in terms of Correlation Coefficient, SNR and Convergence Time. RLS, FTRLS and GAL were never evaluated and compared before on their performance in noise cancellation in ...
2017-01-05
in terms of DC gain and minimum phase. They carried out performance evaluation with the vowel /a/ synthesized by a physical model of voice production ...synthesizer provides a realistic simulation of the voice production process, and thus an adequate test bed for revealing the temporal and spectral performance...characteristics of each algorithm. Included in the synthetic data are continuous running speech utterances and sustained vowels , which are produced
Devaprakash, Daniel; Weir, Gillian J; Dunne, James J; Alderson, Jacqueline A; Donnelly, Cyril J
2016-12-01
There is a large and growing body of surface electromyography (sEMG) research using laboratory-specific signal processing procedures (i.e., digital filter type and amplitude normalisation protocols) and data analyses methods (i.e., co-contraction algorithms) to acquire practically meaningful information from these data. As a result, the ability to compare sEMG results between studies is, and continues to be challenging. The aim of this study was to determine if digital filter type, amplitude normalisation method, and co-contraction algorithm could influence the practical or clinical interpretation of processed sEMG data. Sixteen elite female athletes were recruited. During data collection, sEMG data was recorded from nine lower limb muscles while completing a series of calibration and clinical movement assessment trials (running and sidestepping). Three analyses were conducted: (1) signal processing with two different digital filter types (Butterworth or critically damped), (2) three amplitude normalisation methods, and (3) three co-contraction ratio algorithms. Results showed the choice of digital filter did not influence the clinical interpretation of sEMG; however, choice of amplitude normalisation method and co-contraction algorithm did influence the clinical interpretation of the running and sidestepping task. Care is recommended when choosing amplitude normalisation method and co-contraction algorithms if researchers/clinicians are interested in comparing sEMG data between studies.
Fully three-dimensional defocus-gradient corrected backprojection in cryoelectron microscopy
DEFF Research Database (Denmark)
Kazantsev, Ivan G; Klukowska, J.; Herman, Gabor T.;
2010-01-01
Recognizing that the microscope depth of field is a significant resolution-limiting factor in 3D cryoelectron microscopy, Jensen and Kornberg proposed a concept they called defocus-gradient corrected backprojection (DGCBP) and illustrated by computer simulations that DGCBP can effectively eliminate...
Directory of Open Access Journals (Sweden)
T. O. Ting
2014-01-01
Full Text Available In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC of a battery system. Subsequently, Kalman filter (KF is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS, is a very small value. From this work, it is found that different sets of Q and R values (KF’s parameters can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system. This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.
Ting, T O; Man, Ka Lok; Lim, Eng Gee; Leach, Mark
2014-01-01
In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of Q and R values (KF's parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune Q and R parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area.
SYNTHESIS OF NOVEL ALL-DIELECTRIC GRATING FILTERS USING GENETIC ALGORITHMS
Zuffada, Cinzia; Cwik, Tom; Ditchman, Christopher
1997-01-01
We are concerned with the design of inhomogeneous, all dielectric (lossless) periodic structures which act as filters. Dielectric filters made as stacks of inhomogeneous gratings and layers of materials are being used in optical technology, but are not common at microwave frequencies. The problem is then finding the periodic cell's geometric configuration and permittivity values which correspond to a specified reflectivity/transmittivity response as a function of frequency/illumination angle. This type of design can be thought of as an inverse-source problem, since it entails finding a distribution of sources which produce fields (or quantities derived from them) of given characteristics. Electromagnetic sources (electric and magnetic current densities) in a volume are related to the outside fields by a well known linear integral equation. Additionally, the sources are related to the fields inside the volume by a constitutive equation, involving the material properties. Then, the relationship linking the fields outside the source region to those inside is non-linear, in terms of material properties such as permittivity, permeability and conductivity. The solution of the non-linear inverse problem is cast here as a combination of two linear steps, by explicitly introducing the electromagnetic sources in the computational volume as a set of unknowns in addition to the material unknowns. This allows to solve for material parameters and related electric fields in the source volume which are consistent with Maxwell's equations. Solutions are obtained iteratively by decoupling the two steps. First, we invert for the permittivity only in the minimization of a cost function and second, given the materials, we find the corresponding electric fields through direct solution of the integral equation in the source volume. The sources thus computed are used to generate the far fields and the synthesized triter response. The cost function is obtained by calculating the deviation
Application of digital tomosynthesis (DTS) of optimal deblurring filters for dental X-ray imaging
Energy Technology Data Exchange (ETDEWEB)
Oh, J. E.; Cho, H. S.; Kim, D. S.; Choi, S. I.; Je, U. K. [Yonsei University, Wonju (Korea, Republic of)
2012-04-15
Digital tomosynthesis (DTS) is a limited-angle tomographic technique that provides some of the tomographic benefits of computed tomography (CT) but at reduced dose and cost. Thus, the potential for application of DTS to dental X-ray imaging seems promising. As a continuation of our dental radiography R and D, we developed an effective DTS reconstruction algorithm and implemented it in conjunction with a commercial dental CT system for potential use in dental implant placement. The reconstruction algorithm employed a backprojection filtering (BPF) method based upon optimal deblurring filters to suppress effectively both the blur artifacts originating from the out-focus planes and the high-frequency noise. To verify the usefulness of the reconstruction algorithm, we performed systematic simulation works and evaluated the image characteristics. We also performed experimental works in which DTS images of enhanced anatomical resolution were successfully obtained by using the algorithm and were promising to our ongoing applications to dental X-ray imaging. In this paper, our approach to the development of the DTS reconstruction algorithm and the results are described in detail.
A Filtering Algorithm for Removing Image Mixed Noise%一种去除图像混合噪声的滤波算法
Institute of Scientific and Technical Information of China (English)
吴德刚; 赵利平
2012-01-01
Classical median filtering and mean filtering are often used to filter out impulse noise and Gaussian noise, but if both impulse noise and Gaussian noise exist in the image, effect of the two filtering algorithms turn out to be dissatisfactory. In order to filter out two different kinds of noise simultaneously, a new filtering algorithm for mixed noise is proposed. According to the characteristics of impulse noise and the local energy information of pixel, firstly, the impulse noise is separated, and to be removed by median filtering algorithm. Then the image containing Gaussian noise is denoised by mean filtering algorithm. Test results show that the proposed algorithm can filter out mixed noise effectively and preserve image details very well; it provides an effective way for removing mixed noise in images.%经典的中值滤波和均值滤波常常被分别用来滤除脉冲噪声和高斯噪声,但是当图像同时存在脉冲噪声和高斯噪声时,这两种滤波算法都不能达到最好的滤波效果.为了能同时滤除两种不同性质的噪声,提出了一种新的混合噪声滤波算法.该算法首先根据脉冲噪声的特点和像素的局部能量信息,分离出脉冲噪声并采用中值滤波算法加以去除,然后对含有高斯噪声的图像采用均值滤波算法进行去噪.试验结果表明,该算法在有效滤除混合噪声的同时,能很好地保护图像的细节,从而为去除图像中的混合噪声提供了一种有效的途径.
Feng, Kaiqiang; Li, Jie; Zhang, Xiaoming; Shen, Chong; Bi, Yu; Zheng, Tao; Liu, Jun
2017-09-19
In order to reduce the computational complexity, and improve the pitch/roll estimation accuracy of the low-cost attitude heading reference system (AHRS) under conditions of magnetic-distortion, a novel linear Kalman filter, suitable for nonlinear attitude estimation, is proposed in this paper. The new algorithm is the combination of two-step geometrically-intuitive correction (TGIC) and the Kalman filter. In the proposed algorithm, the sequential two-step geometrically-intuitive correction scheme is used to make the current estimation of pitch/roll immune to magnetic distortion. Meanwhile, the TGIC produces a computed quaternion input for the Kalman filter, which avoids the linearization error of measurement equations and reduces the computational complexity. Several experiments have been carried out to validate the performance of the filter design. The results demonstrate that the mean time consumption and the root mean square error (RMSE) of pitch/roll estimation under magnetic disturbances are reduced by 45.9% and 33.8%, respectively, when compared with a standard filter. In addition, the proposed filter is applicable for attitude estimation under various dynamic conditions.
机载火控雷达TWS滤波算法仿真研究%Emulation Research on TWS Filter Algorithm for Airborne Fire-control Radar
Institute of Scientific and Technical Information of China (English)
吴慈伶
2001-01-01
A kind of filter tracking algorithm joint with inertia navigation system parameters (JINS) is proposed in this paper. This algorithm overcomes the affection on filter tracking performance while the airplane is maneuvering. Compared with the classical α—β filter algorithm in Monte Carlo emulation trial on computer, this algorithm is much better than the classical α—β filter in tracking performance as the airplane maneuvers.%提出了一种结合惯性导航系统参数的跟踪滤波算法(JINS)。该算法克服了由于载机本身机动对滤波器跟踪性能所造成的影响。通过计算机进行Monte Carlo仿真试验，并与经典的α—β滤波算法进行了比较。结果表明：该算法在载机机动时仍然能够对目标保持较高精度的跟踪。
A Wavelet Phase Filtering Algorithm for Image Noise Reduction%图像噪声去除的小波相位滤波算法
Institute of Scientific and Technical Information of China (English)
赵瑞珍; 徐龙; 宋国乡
2001-01-01
Most of the wavelet denoising methods available are based on magnitudes. However,for the images with low SNR.the edges of the image m the wavelet domain are hidden in the noise. A wavelet phase filtering algorithm is presented in this paper, which is insensitive to the magnitude of image.
Institute of Scientific and Technical Information of China (English)
王竹婷
2016-01-01
协同过滤算法是目前应用于电子商务个性化推荐系统中的一种最成功的推荐算法。为缓解因数据稀疏性问题导致的算法推荐质量下降，将关联规则分析引入协同过滤算法中，预测部分未评分项目的评分值，再运用传统的基于用户的协同过滤算法实施推荐。实验结果表明：与传统的协同过滤算法相比，采用关联规则预测评分可以一定程度提高算法推荐质量。%Collaborative filtering algorithm is one of the most successful recommendation algorithms ap-plied to the personalized recommendation system of E-commerce.In order to alleviate the problem of the algorithm recommendation quality decline that caused by the data sparse,the association rule anal-ysis is introduced into the collaborative filtering algorithm,which predicts the item ratings of the non rating items,and then uses the traditional user_based collaborative filtering algorithm to implement the recommendation.The experimental results show that compared with the traditional collaborative filte-ring algorithm,the algorithm uses association rules to predict the item ratings can improve the recom-mended quality.
Directory of Open Access Journals (Sweden)
Yap Hoon
2017-02-01
Full Text Available In this paper, a refined reference current generation algorithm based on instantaneous power (pq theory is proposed, for operation of an indirect current controlled (ICC three-level neutral-point diode clamped (NPC inverter-based shunt active power filter (SAPF under non-sinusoidal source voltage conditions. SAPF is recognized as one of the most effective solutions to current harmonics due to its flexibility in dealing with various power system conditions. As for its controller, pq theory has widely been applied to generate the desired reference current due to its simple implementation features. However, the conventional dependency on self-tuning filter (STF in generating reference current has significantly limited mitigation performance of SAPF. Besides, the conventional STF-based pq theory algorithm is still considered to possess needless features which increase computational complexity. Furthermore, the conventional algorithm is mostly designed to suit operation of direct current controlled (DCC SAPF which is incapable of handling switching ripples problems, thereby leading to inefficient mitigation performance. Therefore, three main improvements are performed which include replacement of STF with mathematical-based fundamental real power identifier, removal of redundant features, and generation of sinusoidal reference current. To validate effectiveness and feasibility of the proposed algorithm, simulation work in MATLAB-Simulink and laboratory test utilizing a TMS320F28335 digital signal processor (DSP are performed. Both simulation and experimental findings demonstrate superiority of the proposed algorithm over the conventional algorithm.
Institute of Scientific and Technical Information of China (English)
陈婧; 张苏
2014-01-01
According to fingerprint characteristics and the characteristics of the fingerprint singular points, the methods of multi-scale filtering and complex filtering are used to analyze fingerprint singularity feature extraction algorithm in order to improve the effi-ciency of automatic fingerprint identification.%根据指纹特征及指纹奇异点的特点，利用多尺度滤波及复数滤波方法，分析改进了指纹奇异特征提取算法，提高了自动指纹识别的效率。
Comparative Study on Some Nonlinear Filtering Algorithms%几种非线性滤波算法的比较研究
Institute of Scientific and Technical Information of China (English)
王庆欣; 史连艳
2011-01-01
针对组合导航等非线性系统,扩展卡尔曼滤波算法(EKF)在初值不准确时存在滤波发散的现象,故提出U-卡尔曼滤波(UKF);粒子滤波算法(PF)适合于强非线性、非高斯噪声系统,但同时存在退化现象,故提出2种改进算法.前人的工作多集中在单一算法的研究,而在此是将上述各种算法应用到同一典型非线性系统,通过应用Matlab进行仿真实验得出具体滤渡效果数据,综合对比分析了各算法的优缺点,得出一些有用的结论,为组合导航系统中非线性滤波算法的选择提供了参考.%For the nonlinear systems such as integrated navigation systems, since the extended Kalman filtering ( EKF) has a dispersing phenomenon when the initial state value is inaccurate, the unscented Kalman filiering ( UKF) is proposed,and although particle filtering (PF) is suitable for any nonlinear non-Gaussian systems, it has a degeneracy phenomenon, then two kinds of improved filtering algorithms are put forward. Scientific researchers focused on single filtering before. The filtering algorithms mentioned above are adopted in a same typical model of nonlinear system in this paper. The detailed data of the filtering algorithms were obtained by emulational experiments with Matlab. Some useful conclusios were acquired after the contrast and analysis of their advantages and disadvantages. A reference is offered in choosing a suitable nonlinearfiltering algorithm for integrated navigation systems.
Institute of Scientific and Technical Information of China (English)
俞琰; 邱广华
2012-01-01
Aiming at data sparsity and malicious behavior in traditional collaborative filtering algorithm, this paper pres- ents a new algorithm of collaborative filtering based on social network. Depending on social network information, the algo- rithm integrates user＇ s trust and preference in order to find the nearest neighbors of the target user, which the algorithm uses to compute weight of neighbors and to form item recommendation. Experimental results show that the algorithm can alleviate the sparsity and malicious behaviors problems and achieve a better prediction accuracy than traditional collaborative filtering algorithms.%针对传统协同过滤推荐算法的数据稀疏性及恶意行为等问题，提出一种新的基于社会网络的协同过滤推荐算法。该算法借助社会网络信息，结合用户信任和用户兴趣，寻找目标用户最近邻居，并以此作为权重，形成项目推荐，以提高推荐的准确度。实验表明，相对于传统的协同过滤算法，该算法可有效缓解稀疏性及恶意行为带来的问题，显著提高推荐系统的推荐质量。
Du, Tien Duc; Ngo-Duc, Thanh; Kieu, Chanh
2017-07-01
This study presents an approach to assimilate tropical cyclone (TC) real-time reports and the University of Wisconsin-Cooperative Institute for Meteorological Satellite Studies (CIMSS) Atmospheric Motion Vectors (AMV) data into the Weather Research and Forecasting (WRF) model for TC forecast applications. Unlike current methods in which TC real-time reports are used to either generate a bogus vortex or spin up a model initial vortex, the proposed approach ingests the TC real-time reports through blending a dynamically consistent synthetic vortex structure with the CIMSS-AMV data. The blended dataset is then assimilated into the WRF initial condition, using the local ensemble transform Kalman filter (LETKF) algorithm. Retrospective experiments for a number of TC cases in the northwestern Pacific basin during 2013-2014 demonstrate that this approach could effectively increase both the TC circulation and enhance the large-scale environment that the TCs are embedded in. Further evaluation of track and intensity forecast errors shows that track forecasts benefit more from improvement in the large-scale flow at 4-5-day lead times, whereas the intensity improvement is minimal. While the difference between the track and intensity improvement could be due to a specific model configuration, this result appears to be consistent with the recent reports of insignificant impacts of inner core data assimilation in operational TC models at the long range of 4-5 days. The new approach will be most beneficial for future regional TC models that are directly initialized from very high-resolution global models whose storm initial locations are sufficiently accurate at the initial analysis that there is no need to carry out any artificial vortex removal or filtering steps.
Christe, Andreas; Brönnimann, Alain; Vock, Peter
2014-02-01
A precise detection of volume change allows for better estimating the biological behavior of the lung nodules. Postprocessing tools with automated detection, segmentation, and volumetric analysis of lung nodules may expedite radiological processes and give additional confidence to the radiologists. To compare two different postprocessing software algorithms (LMS Lung, Median Technologies; LungCARE®, Siemens) in CT volumetric measurement and to analyze the effect of soft (B30) and hard reconstruction filter (B70) on automated volume measurement. Between January 2010 and April 2010, 45 patients with a total of 113 pulmonary nodules were included. The CT exam was performed on a 64-row multidetector CT scanner (Somatom Sensation, Siemens, Erlangen, Germany) with the following parameters: collimation, 24x1.2 mm; pitch, 1.15; voltage, 120 kVp; reference tube current-time, 100 mAs. Automated volumetric measurement of each lung nodule was performed with the two different postprocessing algorithms based on two reconstruction filters (B30 and B70). The average relative volume measurement difference (VME%) and the limits of agreement between two methods were used for comparison. At soft reconstruction filters the LMS system produced mean nodule volumes that were 34.1% (P filters (B30) was significantly larger than with hard filters (B70); 11.2% for LMS and 1.6% for LungCARE®, respectively (both with P filters, 13.6% for soft and 3.8% for hard filters, respectively (P 0.05). There is a substantial inter-software (LMS/LungCARE®) as well as intra-software variability (B30/B70) in lung nodule volume measurement; therefore, it is mandatory to use the same equipment with the same reconstruction filter for the follow-up of lung nodule volume.
基于三边滤波的Retinex图像去雾算法%Retinex image defogging algorithm based on trilateral filtering
Institute of Scientific and Technical Information of China (English)
曹永妹; 张尤赛
2013-01-01
针对传统Retinex算法采用高斯滤波估计图像的照射分量易产生边缘模糊，不能有效去除脉冲噪声且处理后的图像颜色易失真等问题，提出一种基于三边滤波的Retinex图像去雾算法。该算法利用三边滤波器估计图像的照射分量，三边滤波器继承了双边滤波器既可以有效降低图像加性高斯噪声又可以保持图像边缘细节的特性，同时又解决了双边滤波器与高斯滤波器不能有效滤除脉冲噪声，易产生伪边缘等问题。为验证该算法的有效性，采用5种不同的客观评价参数对处理后的图像进行评价。实验证明，该算法能有效地改善雾天图像的退化现象，提高图像的清晰度。%A Retinex image defogging algorithm based on trilateral filtering is proposed in this paper to avoid edge fuzzi-ness,impulse noise and color distortion in traditional Retinex algorithm. In the new algorithm,the trilateral filter is adopted to estimate the illumination component on image. The trilateral filter is utilized to replace Gaussian filter and bilateral filter,both of which can not effectively filter the pulse noise but are easy to produce false edge. Trilateral filter can preserve the image’s edges while it suppresses the additive white Gaussian noise as the bilateral filter does. Five different objective evaluation parame-ters are used to evaluate the disposed images to prove the effectiveness of the algorithm proposed in this paper. Experiment re-sults show that this algorithm can effectively improve the degradation of foggy images and enhance their definition.
Directory of Open Access Journals (Sweden)
Jayaraj V
2010-01-01
Full Text Available A new switching-based median filtering scheme for restoration of images that are highly corrupted by salt and pepper noise is proposed. An algorithm based on the scheme is developed. The new scheme introduces the concept of substitution of noisy pixels by linear prediction prior to estimation. A novel simplified linear predictor is developed for this purpose. The objective of the scheme and algorithm is the removal of high-density salt and pepper noise in images. The new algorithm shows significantly better image quality with good PSNR, reduced MSE, good edge preservation, and reduced streaking. The good performance is achieved with reduced computational complexity. A comparison of the performance is made with several existing algorithms in terms of visual and quantitative results. The performance of the proposed scheme and algorithm is demonstrated.
Filter back—projection technique applied to Abel inversion
Institute of Scientific and Technical Information of China (English)
JiangShano－En; LiuZhong－Li; 等
1997-01-01
The inverse Abel transform is applicable to optically thin plasma with cylindrical symmetry,which is often encountered in plasma physics and inertial(or magnetic)confinemant fusion.The filter back-projection technique is modified,and then a new method of inverse Abel transform is presented.
Improved Particle Filter Algorithm and Application Simulation%改进粒子滤波算法及其应用仿真
Institute of Scientific and Technical Information of China (English)
张军; 所玉君; 董小丰; 张玉朋
2013-01-01
In view of the low precision of particle filter algorithm and particle degradation in target tracking, a GH-RPF algorithm is put forward. Based on particle filter, Gauss-Hermite filter is applied to generate the importance density function, and meanwhile canonical transformation is adopted to re-sampling in order to improve the diversity of particles. If the algorithm is applied to nonlinear and non-Gaussian target tracking, it can be seen from the simulation result that the filtering accuracy is higher and tracking performance is better compared to the standard particle filter algorithm as well as EKPF.%针对目标跟踪中粒子滤波算法的估计精度不高、粒子退化问题，文中提出了一种 GH-RPF 算法。在粒子滤波的基础上，应用高斯-厄米特滤波来产生重要密度函数，同时对重采样采用正则变换以改善采样粒子的多样性。将该算法应用于非线性、非高斯的目标跟踪中，仿真结果表明，与标准粒子滤波及 EKPF 相比，该算法的滤波精度更高，具有更高的跟踪性能。
模糊自适应混合退火粒子滤波算法%THE ALGORITHM OF FUZZY ADAPTIVE HYBRID ANNEALED PARTICLE FILTER
Institute of Scientific and Technical Information of China (English)
蒋东明
2013-01-01
A new particle filter algorithm is proposed based on the hybrid annealed particle filter (HAPF) for on-line estimation of non-Gaussian nonlinear systems and inherent degeneracy problem of the particle filter.In the filtering algorithm,according to the relation between the statistical properties of state noise and measurement noise of the system,we introduce an adjustment factor,then an annealed coefficient is produced by fuzzy inference system.The state parameters separation and the annealed coefficient are used to produce important probability density function.Using the algorithm,we get better annealed coefficient on the basis of keeping the advantages of HAPF.Simulation experiments show that the performance of the proposed filtering algorithm outperforms the HAPF.%针对非线性、非高斯系统状态的在线估计问题,及粒子滤波本身固有的退化问题,在已提出的混合退火粒子滤波算法的基础上提出一种新的粒子滤波算法.在滤波算法中,根据系统的状态噪声统计特性和量测噪声统计特性的关系引入调整因子,再由模糊推理系统产生退火系数.用状态参数分解和退火系数来产生重要性概率密度函数.在保留原算法优点的基础上取得了更佳的退火系数.仿真实验表明该粒子滤波器的性能优于混合退火粒子滤波算法.
Torteeka, Peerapong; Gao, Peng-Qi; Shen, Ming; Guo, Xiao-Zhang; Yang, Da-Tao; Yu, Huan-Huan; Zhou, Wei-Ping; Zhao, You
2017-02-01
Although tracking with a passive optical telescope is a powerful technique for space debris observation, it is limited by its sensitivity to dynamic background noise. Traditionally, in the field of astronomy, static background subtraction based on a median image technique has been used to extract moving space objects prior to the tracking operation, as this is computationally efficient. The main disadvantage of this technique is that it is not robust to variable illumination conditions. In this article, we propose an approach for tracking small and dim space debris in the context of a dynamic background via one of the optical telescopes that is part of the space surveillance network project, named the Asia-Pacific ground-based Optical Space Observation System or APOSOS. The approach combines a fuzzy running Gaussian average for robust moving-object extraction with dim-target tracking using a particle-filter-based track-before-detect method. The performance of the proposed algorithm is experimentally evaluated, and the results show that the scheme achieves a satisfactory level of accuracy for space debris tracking.
Chen, Guoliang; Meng, Xiaolin; Wang, Yunjia; Zhang, Yanzhe; Tian, Peng; Yang, Huachao
2015-09-23
Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone's acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.
Lary, David J.; Mussa, Yussuf
2004-01-01
In this study a new extended Kalman filter (EKF) learning algorithm for feed-forward neural networks (FFN) is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH4 volume mixing ratio (v.m.r.). The neural network was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.
Directory of Open Access Journals (Sweden)
D. J. Lary
2004-06-01
Full Text Available In this study a new extended Kalman filter (EKF learning algorithm for feed-forward neural networks (FFN is used. With the EKF approach, the training of the FFN can be seen as state estimation for a non-linear stationary process. The EKF method gives excellent convergence performances provided that there is enough computer core memory and that the machine precision is high. Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH_{4}-N_{2}O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and CH_{4} volume mixing ratio (v.m.r.. The neural network was able to reproduce the CH_{4}-N_{2}O correlation with a correlation coefficient between simulated and training values of 0.9997. The neural network Fortran code used is available for download.
Varouchakis, Emmanouil
2017-04-01
Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic data driven approach of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable model (ARX model). The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA). KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale, the second a larger time delay in terms of the groundwater level input and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. We apply KFAA to time series of groundwater level values from Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to successfully predict the mean annual and biannual groundwater level for recent years (2007-2010).
Indian Academy of Sciences (India)
N Shantha Kumar; T Jann
2004-04-01
Due to costs, size and mass, commercially available inertial navigation systems are not suitable for small, autonomous ﬂying vehicles like ALEX and other UAVs. In contrast, by using modern MEMS (or of similar class) sensors, hardware costs, size and mass can be reduced substantially. However, low-cost sensors often suffer from inaccuracy and are inﬂuenced greatly by temperature variation. In this work, such inaccuracies and dependence on temperature variations have been studied, modelled and compensated in order to reach an adequate quality of measurements for the estimation of attitudes. This has been done applying a Kalman Filter-based sensor fusion algorithm that combines sensor models, error parameters and estimation scheme. Attitude estimation from low-cost sensors is ﬁrst realized in a Matlab/Simulink platform and then implemented on hardware by programming the micro controller and validated. The accuracies of the estimated roll and pitch attitudes are well within the stipulated accuracy level of ±5° for the ALEX. However, the estimation of heading, which is mainly derived from the magnetometer readings, seems to be inﬂuenced greatly by the variation in local magnetic ﬁeld.
Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization
Directory of Open Access Journals (Sweden)
Guoliang Chen
2015-09-01
Full Text Available Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.
Research on Recommendation Algorithm Based on Collaborative Filtering%基于协同过滤的推荐算法研究
Institute of Scientific and Technical Information of China (English)
王兴国
2016-01-01
With the advent of WEB2.0 technology in the Internet age, information overload problem should be solved through collaborative filtering recommendation system algorithm. This paper analyzes the based collaborative filtering recommendation algorithm research signiifcance and based on collaborative ifltering recommendation algorithm research present situation, according to the recommender system and collaborative ifltering algorithm characteristics, respectively on the fusion of information about the social network of collaborative ifltering algorithm, fusion based on users and items based on collaborative ifltering algorithm for simple analysis.%随着互联网时代WEB2.0技术的到来，信息过载问题要通过协同过滤推荐系统算法有效地解决。文章分析了基于协同过滤的推荐算法研究的意义和基于协同过滤的推荐算法研究的现状，根据推荐系统和协同过滤算法的特点，分别对融合社交网络信息的协同过滤算法、融合基于用户和基于项目协同过滤算法进行简单的研究和分析。
基于用户兴趣分类的协同过滤推荐算法%Collaborative Filtering Algorithm Based on Interest-Class
Institute of Scientific and Technical Information of China (English)
陶俊; 张宁
2011-01-01
在现代信息网络中,个性化的推荐系统已经成为用户和应用软件交互的关键部分.推荐算法是个性化推荐系统的核心,其中,协同过滤算法是至今应用最为成功的推荐算法之一.但传统的协同过滤算法没有考虑用户兴趣的多样性,对用户兴趣度量不准确,难以适用于用户多兴趣的推荐系统,提出了适应用户兴趣多样性的协同过滤算法并利用改进的模糊聚类算法搜索最近邻.最后采用实际的日志数据进行算法实验,实验结果表明该算法较其他推荐算法具有较优的执行效率和推荐精度.%In the modem information network, the personalized recommendation system has become a key part of users in software application. Recommendation algorithms are the core of personalized recommendation systems. Among them, the collaborative filtering is one of the most successful recommendation algorithm in application. However, the traditional collaborative filtering algorithm does not consider user's multiple interest and measure user's interest imprecisely, and can't be applied to recommendation system with kinds of interests. In this paper, a new method of collaborative filtering algorithm based on users' interest category is proposed using improved fuzzy clustering algorithm to search the nearest neighbors. Finally, the algorithm experiment is given with actual log-data. Results show that the proposed algorithm outperforms the other recommendation ones in efficiency and recommending accuracy.
Backprojection of GNSS total-electron content signals for recent large earthquakes
Mikesell, T. D.; Rolland, L.; Haney, M. M.; Larmat, C. S.; Lee, R.
2015-12-01
It is well known that earthquakes and tsunamis couple energy into the dynamically fluid atmosphere. This energy can propagate up to the ionosphere where we can observe perturbations in the total-electron content (TEC) signals measured by global navigation space systems (GNSS). Recent emphasis has been placed on using these new observables to characterize earthquake and tsunami hazards from space, as well as for planetary exploration. Backprojection is an array-based imaging technique used in seismology to characterize the seismic source location, including complex energy release patterns from large earthquakes. Here we present TEC backprojection results from 3 recent earthquakes - 1) 2009 Samoa triggered doublet (Mw 8.1), 2) 2011 Van dip-slip event (Mw 7.1) and 3) 2012 Haida Gwaii strike-slip underthrust event (Mw 7.8). Each of these events presents new obstacles to overcome if backprojection is to be used routinely to monitor hazards from space. We will discuss these obstacles in detail and present approaches to overcome them. For instance, one problem arises from the fact that the observation point is non-stationary in time because the satellites are moving. Another problem stems from the relative geometry of the geomagnetic field and the incoming acoustic wave at the ionosphere. Finally, we present array-based methods to reduce artifacts in the backprojection images, e.g. array deconvolution, and we show that under favorable circumstances, this approach can be used to characterize motion at the Earth surface from space with high temporal and spatial resolution.
社会化标签语义相似度的协同过滤算法%Collaborative Filtering Algorithm Based on Social Tags Semantic Similarity
Institute of Scientific and Technical Information of China (English)
谌颃
2016-01-01
In order to solve the traditional collaborative filtering algorithm can not accurately understand the user′s pref-erences,affect the recommendation accuracy and recommendation effect,a collaborative filtering algorithm based on social tags semantic similarity is proposed.Based on the semantic similarity of tags,the semantic information of project re-sources and related tags is included,and the prediction performance of the recommendation system is significantly im-proved.Research results show that:compared with the algorithm based on the user rating,the proposed algorithm can solve the problem of word similarity and sentence similarity computation,and the recommendation accuracy and recom-mendation effect,as well as the performance of the proposed algorithm is significantly improved compared with the previ-ous collaborative filtering algorithm.%为解决传统的协同过滤算法不能准确理解用户的喜好，影响推荐准确率和推荐效果，提出基于社会化标签语义相似度的协同过滤算法。算法以标签语义相似度为基础，将项目资源和相关标签的语义信息纳入，显著提高了推荐系统的预测性能。研究结果表明：与以具体评分数据为基础的算法相比，该算法较好地解决了词相似度和句子相似度计算问题，推荐准确度和性能较以往的协同过滤算法有明显提高，改善了推荐效果。
Filtering of the Radon transform to enhance linear signal features via wavelet pyramid decomposition
Meckley, John R.
1995-09-01
wavelet coefficient energy. The detection algorithm provides an estimate of the line offset, orientation, and length that is then used to index the appropriate filter shape. Additional wavelet pyramid decomposition is performed in areas of high energy to refine the line position estimate. After filtering, the new Radon transform is generated by inverting the wavelet pyramid. The Radon transform is then inverted by filtered backprojection to produce the final 2D signal estimate with the enhanced linear features. The wavelet-based method is compared to both the Fourier and the nonlinear filtering with examples of sparse and dense shapes in imaging, acoustics and medical tomography with test images of noisy concentric lines, a real spectrogram of a blow fish (a very nonstationary spectrum), and the Shepp Logan Computer Tomography phantom image. Both qualitative and derived quantitative measures demonstrate the improvement of wavelet-based filtering. Additional research is suggested based on these results. Open questions include what level(s) to use for detection and filtering because multiple-level representations exist. The lower levels are smoother at reduced spatial resolution, while the higher levels provide better response to edges. Several examples are discussed based on analytical and phenomenological arguments.
Li, Yinsheng; Niu, Kai; Li, Ke; Schafer, Sebastian; Royalty, Kevin; Strother, Charles; Chen, Guang-Hong
2016-03-01
In this work, a newly developed reconstruction algorithm, Synchronized MultiArtifact Reduction with Tomographic RECONstruction (SMART-RECON), was applied to C-arm cone beam CT perfusion (CBCTP) imaging. This algorithm contains a special rank regularizer, designed to reduce limited-view artifacts associated with super- short scan reconstructions. As a result, high temporal sampling and temporal resolution image reconstructions were achieved using an interventional C-arm x-ray system. The algorithm was evaluated in terms of the fidelity of the dynamic contrast update curves and the accuracy of perfusion parameters through numerical simulation studies. Results shows that, not only were the dynamic curves accurately recovered (relative root mean square error ∈ [3%, 5%] compared with [13%, 22%] for FBP), but also the noise in the final perfusion maps was dramatically reduced. Compared with filtered backprojection, SMART-RECON generated CBCTP maps with much improved capability in differentiating lesions with perfusion deficits from the surrounding healthy brain tissues.
Schumann, A.; Priegnitz, M.; Schoene, S.; Enghardt, W.; Rohling, H.; Fiedler, F.
2016-10-01
Range verification and dose monitoring in proton therapy is considered as highly desirable. Different methods have been developed worldwide, like particle therapy positron emission tomography (PT-PET) and prompt gamma imaging (PGI). In general, these methods allow for a verification of the proton range. However, quantification of the dose from these measurements remains challenging. For the first time, we present an approach for estimating the dose from prompt γ-ray emission profiles. It combines a filtering procedure based on Gaussian-powerlaw convolution with an evolutionary algorithm. By means of convolving depth dose profiles with an appropriate filter kernel, prompt γ-ray depth profiles are obtained. In order to reverse this step, the evolutionary algorithm is applied. The feasibility of this approach is demonstrated for a spread-out Bragg-peak in a water target.
Research on SINS/GPS Integrated Navigation Kalman Filter Algorithm%SINS/GPS组合导航卡尔曼滤波算法研究
Institute of Scientific and Technical Information of China (English)
牛强军; 张潮; 苏登辉
2015-01-01
Aiming at that single use of any kind of navigation equipment will not meet requirement of airborne fire control system and flight system, deduce a new Kalman filter algorithm of SINS/GPS integrated navigation. Introduce albinism processing of colored noise into Kalman filter, design a dynamic vehicle integrated navigation test system, put forwards the steps of Kalman filter algorithm based on colored noise albinism, use dynamic vehicle SINS/GPS integrated navigation system test data analysis to validate correctness and rationality of algorithm. The analyse results show that, Kalman filter which based on colored noise albinism make up for the deficiency of traditional Kalman filter, and improve the accuracy of navigation result.%针对单独使用某一种的导航设备都无法满足机载火控系统和飞行系统要求的问题,推导出 SINS/GPS 组合导航中的一种新的卡尔曼滤波算法.将有色噪声的白化处理引入到卡尔曼滤波器,设计了一套动态车载组合导航试验系统,给出了基于有色噪声白化的卡尔曼滤波器算法的具体步骤,以动态车载 SINS/GPS 组合导航系统试验的数据分析验证了此算法的正确性和合理性.分析结果表明:基于有色噪声白化的卡尔曼滤波器可以很好地解决有色噪声的影响,弥补了传统卡尔曼滤波器的不足,提高了导航结果的精确度.
Martinek, Radek; Kahankova, Radana; Nazeran, Homer; Konecny, Jaromir; Jezewski, Janusz; Janku, Petr; Bilik, Petr; Zidek, Jan; Nedoma, Jan; Fajkus, Marcel
2017-05-19
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size μ and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.
Institute of Scientific and Technical Information of China (English)
郭兰杰; 梁吉业; 赵兴旺
2016-01-01
To solve the problems of high data sparsity and limited recommendation precision of collaborative filtering recommendation algorithms, a collaborative filtering algorithm incorporating social network information is proposed under the framework of item_based collaborative filtering recommendation. In item similarity calculation period and user rating prediction period, social network information is utilized to fill missing values in rating matrix selectively and thus the existing rating information is utilized as much as possible. Finally, experiment is conducted on Epinions dataset. Results show that the proposed algorithm alleviates the data sparsity problem and outperforms other collaborative filtering algorithms on rating error and precision.%在推荐系统中，协同过滤推荐算法往往面临数据集的高度稀疏性和推荐精度有限的问题。为了解决上述问题，在基于物品的协同过滤推荐框架下，分别在物品相似度的计算和用户对物品的评分预测阶段，利用社交网络中朋友关系信息选择性地填充评分矩阵中的缺失值，最大化利用评分矩阵中的已有信息，提出融合社交网络信息的协同过滤推荐算法。最后，在Epinions数据集上的实验表明，文中算法在一定程度上缓解数据稀疏性问题，同时在评分误差和分类准确率两个指标上优于其它协同过滤算法。
一种新的混合噪音滤波器加速算法%A New Acceleration Algorithm for Mixed Noise Filter
Institute of Scientific and Technical Information of China (English)
王世秀; 罗晓军; 李兵
2012-01-01
Mixed noise filter MNF is one of the best algorithms at present.However,it uses the non-local algorithm,so there is greater calculation burden.For this shortage,a mixed noise filter acceleration algorithm (FMNF) base on mean value and variance similarity is proposed.The main idea of the algorithm is pre-classifying to neighboring pixels,the neighboring pixels of ratio of the mean value and variance within a given threshold value range (close to 1) is divided into a class,as a similar pixels.The similar pixels take part in filtering calculation and dissimilar pixels are ignored.Therefore,it reduces the number of pixels involved in "impulse controlled weighted norm" calculation and improves the filtering speed.The simulation results show that visual effect and PSNR of denoising figure of FMNF and MNF are quite to various kinds of noise (pure Gaussian noise,pure impulse noise and its mixed noise) denoising,and the filtering speed all can be increased by more than 15%.Therefore,the FMNF algorithm is more practical than MNF algorithm.%混合噪音滤波器MNF是目前滤波效果最好的算法之一,然而,由于它采用非局部算法思想,所以存在较大的计算负担,针对该不足,提出一种基于均值和方差相似性的加速算法(FMNF).该算法之关键思想是对邻域像素预分类,把两像素的均值比和方差比均在给定阈值范围内(接近于1)的邻域像素分为一类,视为相似像素,相似的像素参与滤波计算,不相似的像素被忽略,因此,减少了参与“脉冲过滤范数”计算的像素数,提高了滤波速度.仿真实验结果表明,FMNF对各种噪音类型(纯高斯噪音、纯脉冲噪音以及它们的混合噪音)去噪的视觉效果和PSNR均与MNF相当,且滤波速度均可提高15％以上.因而,FMNF算法比MNF更具有实用性.
一种新的Vague集的协同过滤推荐算法%Anew collaborative filtering recommendation algorithms based on Vague sets
Institute of Scientific and Technical Information of China (English)
王伟; 彭进业; 刘盛辉
2012-01-01
Aimed at the collaborative filtering recommendation system, a new collaborative filtering recommendation algorithm based on Vague sets (RSCFRA-VS) is presented. Based on the analysis of the limitations of existing collaborative filtering recommendation algorithmbasedonVaguesets,RSCFRA-VSalgorithmisintroducedindetails,andtheexperimentalresultsandanalysisofthis algorithm are given at the same time. The experimental results show that this method is effective and practical, which not only improves the accuracy of commodity recommendation but also provides a new idea of the application of Vague sets in the collaboration filtering recommendation system.% 针对协同过滤推荐系统，结合Vague集方法提出了一种新的协同过滤推荐RSCFRA-VS算法。在分析了已有的基于Vague集的协同过滤推荐算法及其不足之后，详细介绍了RSCFRA-VS算法，并给出了该算法的试验结果与分析。试验结果表明，该RSCFRA-VS方法是有效和实用的，能够提高商品推荐的准确度，也为将Vague集应用于推荐系统提供了一种思路。
Directory of Open Access Journals (Sweden)
Nor Farahaida Abdul Rahman
2016-09-01
Full Text Available An adaptive hybrid fuzzy-proportional plus crisp-integral current control algorithm (CCA for regulating supply current and enhancing the operation of a shunt active power filter (SAPF is presented. It introduces a unique integration of fuzzy-proportional (Fuzzy-P and crisp-integral (Crisp-I current controllers. The Fuzzy-P current controller is developed to perform gain tuning procedure and proportional control action. This controller inherits the simplest configuration; it is constructed using a single-input single-output fuzzy rule configuration. Thus, an execution of few fuzzy rules is sufficient for the controller’s operation. Furthermore, the fuzzy rule is developed using the relationship of currents only. Hence, it simplifies the controller development. Meanwhile, the Crisp-I current controller is developed to perform integral control action using a controllable gain value; to improve the steady-state control mechanism. The gain value is modified and controlled using the Fuzzy-P current controller’s output variable. Therefore, the gain value will continuously be adjusted at every sample period (or throughout the SAPF operation. The effectiveness of the proposed CCA in regulating supply current is validated in both simulation and experimental work. All results have proven that the SAPF using the proposed CCA is capable to regulate supply current during steady-state and dynamic-state operations. At the same time, the SAPF is able to enhance its operation in compensating harmonic currents and reactive power. Furthermore, the implementation of the proposed CCA has resulted more stable dc-link voltage waveform.
Directory of Open Access Journals (Sweden)
Lin Zhang
2016-10-01
Full Text Available To measure the pushing distance of a hydraulic-powered roof support, and reduce the cost from a non-reusable displacement sensor embedded in pushing a hydraulic cylinder, an inertial sensor is used to measure the pushing distance, and a Kalman filter is applied to process the inertial data. To obtain better estimation performance, an improved fruit fly optimization algorithm (IFOA is proposed to tune the parameters of the Kalman filter, processing noise covariance Q and observation noise covariance R. The key procedures of the proposed method, including state-space model, fitness function, and Kalman filter implementation, are presented. Finally, an artificial signal is utilized to verify the feasibility of the proposed method, and the tuning results of other algorithms, particle swarm optimization (PSO, genetic algorithm (GA, basic FOA, and 3D-FOA are compared. The proposed method is also applied in the pushing distance estimation scenario. The simulation and application results prove the effectiveness and superiority of the proposed method.
The influence of image reconstruction algorithms on linear thorax EIT image analysis of ventilation.
Zhao, Zhanqi; Frerichs, Inéz; Pulletz, Sven; Müller-Lisse, Ullrich; Möller, Knut
2014-06-01
Analysis methods of electrical impedance tomography (EIT) images based on different reconstruction algorithms were examined. EIT measurements were performed on eight mechanically ventilated patients with acute respiratory distress syndrome. A maneuver with step increase of airway pressure was performed. EIT raw data were reconstructed offline with (1) filtered back-projection (BP); (2) the Dräger algorithm based on linearized Newton-Raphson (DR); (3) the GREIT (Graz consensus reconstruction algorithm for EIT) reconstruction algorithm with a circular forward model (GR(C)) and (4) GREIT with individual thorax geometry (GR(T)). Individual thorax contours were automatically determined from the routine computed tomography images. Five indices were calculated on the resulting EIT images respectively: (a) the ratio between tidal and deep inflation impedance changes; (b) tidal impedance changes in the right and left lungs; (c) center of gravity; (d) the global inhomogeneity index and (e) ventilation delay at mid-dorsal regions. No significant differences were found in all examined indices among the four reconstruction algorithms (p > 0.2, Kruskal-Wallis test). The examined algorithms used for EIT image reconstruction do not influence the selected indices derived from the EIT image analysis. Indices that validated for images with one reconstruction algorithm are also valid for other reconstruction algorithms.
Allner, S.; Koehler, T.; Fehringer, A.; Birnbacher, L.; Willner, M.; Pfeiffer, F.; Noël, P. B.
2016-05-01
The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.
Allner, S; Koehler, T; Fehringer, A; Birnbacher, L; Willner, M; Pfeiffer, F; Noël, P B
2016-05-21
The purpose of this work is to develop an image-based de-noising algorithm that exploits complementary information and noise statistics from multi-modal images, as they emerge in x-ray tomography techniques, for instance grating-based phase-contrast CT and spectral CT. Among the noise reduction methods, image-based de-noising is one popular approach and the so-called bilateral filter is a well known algorithm for edge-preserving filtering. We developed a generalization of the bilateral filter for the case where the imaging system provides two or more perfectly aligned images. The proposed generalization is statistically motivated and takes the full second order noise statistics of these images into account. In particular, it includes a noise correlation between the images and spatial noise correlation within the same image. The novel generalized three-dimensional bilateral filter is applied to the attenuation and phase images created with filtered backprojection reconstructions from grating-based phase-contrast tomography. In comparison to established bilateral filters, we obtain improved noise reduction and at the same time a better preservation of edges in the images on the examples of a simulated soft-tissue phantom, a human cerebellum and a human artery sample. The applied full noise covariance is determined via cross-correlation of the image noise. The filter results yield an improved feature recovery based on enhanced noise suppression and edge preservation as shown here on the example of attenuation and phase images captured with grating-based phase-contrast computed tomography. This is supported by quantitative image analysis. Without being bound to phase-contrast imaging, this generalized filter is applicable to any kind of noise-afflicted image data with or without noise correlation. Therefore, it can be utilized in various imaging applications and fields.
基于网络安全的网页过滤模型及其关键算法%Webpage filter algorithm model and its key algorithms based on network security
Institute of Scientific and Technical Information of China (English)
季秀兰; 熊拥军
2011-01-01
As the World Wide Web continues to grow at an exponential rate, the Webpage Information Filtering used for identify the illegitimate text includes ill information, and then delete them. Result from the ever-increasing of the ill information in webpage, in the future it is a new field in the research of information filtering. Based on the traditional way of keywords, the webpage grasped was per-treated and then the key word dictionary was set up with weights by applying the concepts of the same category words in Chinese corpus, from an angle of lexical relevance, the relevance filtering algorithm based on same category words weight was put forward. Finally, an algorithm evaluation from two angles consideration was carried out. The filter algorithm is more effective and copes with the strategy to the anti-keyword filtering of eroticism website.%识别存在于大量的WEB网页中的不良信息的非法文本,并将其有效屏蔽,是未来信息过滤研究的新领域.在传统方法的基础上,在对抓取到的网页进行预处理后,设置加权的关键字词典；应用汉语语料库里同类词的概念,从词汇关联的角度出发,最终提出了基于同类词权重均值的关联过滤算法.最后,从两个角度进行算法评估,该过滤算法更为高效,并且能够很好的应对不良网站的反关键字过滤策略.
一种改进的Goldstein InSAR干涉图滤波算法%A Modified Goldstein Algorithm for InSAR Interferogram Filtering
Institute of Scientific and Technical Information of China (English)
于晓歆; 杨红磊; 彭军还
2011-01-01
提出了一种抑制InSAR干涉图噪声并保持干涉图条纹细节的算法,该算法改进了Goldstein滤波的参数α,将干涉图的相位标准偏差函数模型作为参数。相位标准偏差是相位噪声的体现,以干涉图的相位噪声强弱来决定滤波的强弱,噪声强的局部区域强滤波,噪声弱的局部区域弱滤波。实验结果表明,此方法改善了滤波效果,增强了滤波的局部自适应性和条纹细节的保真性。%An algorithm for filtering InSAR phase noise and preserving details of interferometric fringes is proposed,which improves Goldstein filtering parameter α and considers function of standard deviation of phase to be parameter.The standard deviation of phase shows phase noise effectively.According to degree of interferogram phase noise,the method can determine degree of filter strength.The modification can make the local area with strong noise strongly filtered,while those with week noise weekly filtered.Experimental results with both the simulated data sets and the real one show that the new method can improve filtering effect and enhance the local adaptability of the filter and fidelity details of interferometric fringes.
Analytic reconstruction algorithms for triple-source CT with horizontal data truncation
Energy Technology Data Exchange (ETDEWEB)
Chen, Ming [School of Mathematics and System Science, Shandong University of Science and Technology, Qingdao, Shandong 265590, China and Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854 (United States); Yu, Hengyong, E-mail: hengyong-yu@ieee.org [Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854 (United States)
2015-10-15
Purpose: This paper explores a triple-source imaging method with horizontal data truncation to enlarge the field of view (FOV) for big objects. Methods: The study is conducted by using theoretical analysis, mathematical deduction, and numerical simulations. The proposed algorithms are implemented in c + + and MATLAB. While the basic platform is constructed in MATLAB, the computationally intensive segments are coded in c + +, which are linked via a MEX interface. Results: A triple-source circular scanning configuration with horizontal data truncation is developed, where three pairs of x-ray sources and detectors are unevenly distributed on the same circle to cover the whole imaging object. For this triple-source configuration, a fan-beam filtered backprojection-type algorithm is derived for truncated full-scan projections without data rebinning. The algorithm is also extended for horizontally truncated half-scan projections and cone-beam projections in a Feldkamp-type framework. Using their method, the FOV is enlarged twofold to threefold to scan bigger objects with high speed and quality. The numerical simulation results confirm the correctness and effectiveness of the developed algorithms. Conclusions: The triple-source scanning configuration with horizontal data truncation cannot only keep most of the advantages of a traditional multisource system but also cover a larger FOV for big imaging objects. In addition, because the filtering is shift-invariant, the proposed algorithms are very fast and easily parallelized on graphic processing units.
Design and Optimization of FIR Filtering Algorithm Based on CUDA Platform%基于CUDA平台的FIR滤波算法的设计与优化
Institute of Scientific and Technical Information of China (English)
郭海凤; 李莉
2014-01-01
针对目前基于普通DSP的FIR算法速度低、扩展性差的缺点,提出并实现基于CUDA平台实现的FIR滤波算法。由于在CUDA中程序可以直接操作数据而无需借助于图形系统的API,使开发者能够在GPU 强大计算能力的基础上建立起一种效率更高的密集数据计算解决方案。该算法将CUDA用于FIR滤波器输入输出关系计算,采用矩阵乘法的并行运算技术,在GPU上建立并行滤波模型,并对算法进行了优化。实验结果表明,在Tesla C1060平台上,和传统的基于DSP的FIR滤波算法计算速度相比,基于CUDA平台计算FIR滤波算法时,其加速比可接近30,解决了传统基于DSP计算FIR滤波算法速度较慢、扩展性差的问题。%It is well known that FIR algorithm based on normal DSP has low computing speed and extensive capabilities. In order to over-come these,present a new FIR filter algorithm based on CUDA platform. Since in CUDA program can directly manipulate data without graphics API of the system,enables developers on the basis of the powerful GPU computing power to set up a efficient dense data compu-ting solutions. The algorithm adopts CUDA for FIR filter calculation of input and output relationship,using the parallel computing tech-nology of matrix multiplication,on the GPU the parallel filtering model is established,and the algorithm is optimized. Experiment on Tes-la C1060 shows that,compared with traditional FIR filter algorithm's speed based on DSP,it can accelerate its computation speed up to 30 times,solving conventional FIR filter's defect based on DSP of low speed and bad extending capabilities.
Institute of Scientific and Technical Information of China (English)
龙君; 曾三云
2014-01-01
先将非线性互补问题（NCP ）转化为与其等价且有可行解的辅助问题，再将引入了信赖域方法思想的SQP方法与Filter技术相结合，提出一种求解NCP问题的信赖域-SQP-filter算法，并讨论了解的存在性和算法的全局收敛性。数值结果表明我们的算法是有效并收敛的。%This paper constructs an auxiliary problem with feasible solution , which is equivalent to the nonlinear complementarity problem . Through combining the trust region -SQP method and filter technology , a trust region -SQP-filter algorithm for solving NCP is proposed . Finally , we discuss the global convergence of the algorithm and the existence of solution for NCP . The numerical results show that our algorithm is effective and convergent .
Comparation of several CUDA accelerated Gaussian filtering algorithms%几种CUDA加速高斯滤波算法的比较
Institute of Scientific and Technical Information of China (English)
刘进锋
2013-01-01
There are some image filtering algorithms based on CUDA, but some of them are not clearly described, and no one to compare the performance of these algorithms, which brings difficulties for understanding and using these algorithms. This paper discusses five different Gaussian image filters based on CUDA, they are naive method, separable share memory method, separa-ble texture memory method, FFT convolution filtering and recursive Gaussian filter. Core ideas are emphasized, time complexi-ties are compared, and performances are analyzed through experiments.%目前已有几种CUDA加速的图像高斯滤波算法，但这些算法有的描述不清楚，也没有人对它们的性能进行详尽的比较，这给理解及应用带来了困难。描述了几种CUDA加速的图像高斯滤波算法，包括直观的实现方式、使用共享内存的分离滤波器方法、使用纹理内存的分离滤波器方法、基于CUFFT的卷积滤波以及递归高斯滤波器。强调了这些算法的核心思想，比较了它们的时间复杂度，通过实验对它们的性能进行了分析。
Improved Kalman Filtering Algorithm in Non-Uniformity Correction%改进的卡尔曼滤波非均匀性校正算法
Institute of Scientific and Technical Information of China (English)
李晶; 朱斌; 郭立新; 龙波; 王小珂
2012-01-01
针对基于卡尔曼滤波(Kalman filtering,KF)的红外焦平面非均匀性校正算法的计算量和存储量较大,不利于实时性校正的缺点,提出一种改进的卡尔曼滤波非均匀性校正算法.该算法通过线性递归滤波器修正了观测方程,用每一帧块图像的统计均值来代替卡尔曼滤波校正算法中的观测矩阵,使增益矩阵得到简化.Matlab仿真实验结果证明:该算法的校正效果与传统的卡尔曼滤波校正算法相当,但大大减少了计算量和存储空间.%Aiming at the big computational complexity and memory requirement of infrared focal plane non-uniformity correction algorithm based on Kalman filtering, it cannot realize real-time correction shortage, an improved Kalman filtering non-uniformity correction algorithm is given. The proposed algorithm modified the observation model by applying linear recursion filter that the observation was instead of the mean value of every block image, then the matrix will be simplified. The simulation of Matlab proves that the computational complexity and memory requirements are significantly reduced, while the correction result is similar.
红外图像的自适应混合双边滤波算法%Adaptive hybrid bilateral filtering algorithm for infrared image
Institute of Scientific and Technical Information of China (English)
余博; 郭雷; 赵天云; 钱晓亮
2012-01-01
针对红外图像中的混合噪声,提出了一种自适应混合双边滤波算法.首先对双边滤波原理进行了分析,提出不能滤除强高斯噪声和脉冲噪声是由于双边滤波引入灰度域权值而带来的固有不足,因此根据双边滤波算法的特点设置了一种像素间的相似度,并以该相似度为基础将双边滤波不能滤除的强噪声点进行了标记,仅对红外图像中标记出的强噪声点进行中值滤波以减少图像模糊,对普通噪声点采用灰度方差自适应双边滤波以保留更多边缘特征.自适应混合双边滤波能够有效滤除红外图像中的高斯噪声、脉冲噪声以及由其组成的混合噪声,同时在滤波过程中并不降低双边滤波保留红外图像边缘特征的性能.仿真实验结果表明,与传统双边滤波、改进的双边滤波以及各项异性扩散-中值滤波算法相比,该算法无论是滤除红外图像的混合噪声还是保留边缘特征都较为优越.%In view of the mixed noise in the infrared image, the adaptive hybrid bilateral filtering was proposed. The principle of bilateral filter was analyzed. It was put forward that the shortage of bilateral filtering which couldn' t filter out strong Gauss noise and impulse noise was inherent. Therefore, the average similarity was defined for bilateral filtering. On the basis of the average similarity, noise points were marked with strong noise points which couldn't be filtered out by bilateral filtering and ordinary noise points which could be filtered out by bilateral filtering. To reduce image fuzzy, only the strong noise points were filtered by median filtering. To keep more edge character, ordinary noise points were filtered by adaptive bilateral filtering. Simulation results show that the hybrid bilateral filtering keeps more edge character, and filters out Gaussian noise and the mixed noise which consist of Gaussian noise and impulse noise effectively. The algorithm is superior to
基于用户实时反馈的协同过滤算法%Collaborative filtering algorithm based on real-time user feedback
Institute of Scientific and Technical Information of China (English)
傅鹤岗; 李冉
2011-01-01
Traditional memory-based collaborative filtering algorithm has the problem of bad scalability, while the modelbased collaborative filtering algorithm, due to lagged updating hysterics, has the problem of bad recommendation. To solve the above problems, a collaborative filtering algorithm based on real-time users' feedback was proposed, which achieved that recommender system can finish the real-time updating of the model data when a new rating was submitted by active user.Hence, recommender system can reflect the changing of user interest accurately. The experimental results indicate that the algorithm can improve the recommendation accuracy efficiently and reduce the recommendation time significantly.%传统的基于内存的协同过滤算法存在可扩展性不足的问题,而基于模型的协同过滤算法由于模型数据的滞后,造成推荐质量不高.针对以上情况,提出一种基于用户实时反馈的协同过滤算法,该算法在用户提交项目评分之后能实现对推荐模型数据的实时更新,从而更精确地反映用户的兴趣变化.实验结果表明,该算法能够有效地提高推荐精确度并且大幅地缩短了推荐时间.
一种自适应窗口滤波算法研究%Research on a novel adaptive window for image filter algorithm
Institute of Scientific and Technical Information of China (English)
赵立兴; 唐英干; 王洪瑞; 王正峰
2013-01-01
A novel on-off image filter algorithm is introduced in this paper, whose shape, size and orientation can vary with the image's regional structure. The filter window's size vary with the corresponding pixel's gradient magnitude, and it's shape, orientation is adjusted in such a way as to lie in the direction of the least gradient. Meanwhile, a criterion and flowchart which can classify pixels into noise-pixels and structure pixels is also proposed. Compare with the traditional mean filter, the traditional median filter and the method of an adaptive smooth filter algorithm of still images, the simulation results prove that the proposed adaptive window image filter algorithm have a better resolution and it can protects image's structure more well.%提出了一种滤波窗口方位、尺寸和形状都可随图像纹理结构自适应变化的开关滤波算法。滤波窗的尺寸可随图像梯度值的变化而变化，滤波窗方位排列与图像对应点像素最小梯度值方向一致。同时，为了将受噪声污染的点和图像的细节纹理像素点分开，给出了一种噪声点检测判据和开关算法流程。与传统均值、中值及相关文献提出的自适应平滑滤波算法相比，由于本文算法在降噪的同时兼顾了图像的局部纹理分布结构，因此在保护图像细节方面做得更好。仿真实验结果证明了本文算法的有效性。
基于卡尔曼滤波融合的移动机器人定位算法%Mobile robot localization algorithm based on Calman filter fusion
Institute of Scientific and Technical Information of China (English)
王襄
2016-01-01
移动机器人的定位是实现机器人自动导航的一项关键技术内容。为了满足移动机器人准确定位的要求，提出来基于卡尔曼滤波融合的定位算法。通过卡尔曼滤波融合算法里程计和声呐的测量数据，并针对该方法中的观测误差导致滤波器性能下降发散的问题，提出基于卡尔曼滤波融合的移动机器人定位算法，对估算位置坐标进行优化处理，以提高移动机器人定位的性能和稳定性，改善定位精度。%By Kalman filtering fusion algorithm of odometer and sonar measurements,and for the method of observation error leads to filter divergence,performance degradation problem,put forward to mobile robot localization algorithm based on Kalman filter fusion,to estimate position coordinates are optimized,in order to improve the performance and stability of mobile robot localization,improve positioning accuracy.
Grading Median Algorithm for Filtering Salt and Pepper Noises in Image%图像椒盐噪声的分阶段中值滤波算法
Institute of Scientific and Technical Information of China (English)
晏资余; 罗杨; 杨浩
2013-01-01
A grading median filtering algorithm was proposed to offset the defect of adaptive median filter ( AMF) that it left some black plaque after filtering images corrupted by high density salt and pepper noises. Through twice filter to the noise image with small size win-dow,compared with bigger ones, it reduced the blur degree of result image. For the first time,it eliminated the salt noise by using median filter ( MF ) to noise pixels, and then wiped off the black plaque by replacing pepper noise pixels with the median of the noise free pixels in its 8-neighborhood. Lastly, the simulation result shows, our algorithm either has the good capability to filter the low density noises as well as AMF or has the ability to filter higher density salt and pepper noises in image.%针对自适应中值滤波算法的缺陷---对高密度椒盐噪声图像滤波后留下黑色斑块，提出了一种分阶段中值滤波算法。该算法对图像执行两次小窗口的滤波操作，相较于采用较大窗口的滤波，其在有效去除噪声的同时降低了结果图像的模糊程度。先对所有噪声点进行一次中值滤波消除了盐粒噪声，再用窗口内非噪声点的灰度中值代替胡椒噪声点的灰度值以去除黑色斑块。最后的仿真实验结果表明，本文算法既有像自适应中值算法一样滤除低密度椒盐噪声的良好性能，又有对高密度椒盐噪声图像的降噪能力。
Genetic Algorithm-Based Design of the Active Damping for an LCL-Filter Three-Phase Active Rectifier
DEFF Research Database (Denmark)
Liserre, Marco; Aquila, Antonio Dell; Blaabjerg, Frede
2004-01-01
Active rectifiers/inverters are becoming used more and more often in regenerative systems and distributed power systems. Typically, the interface between the grid and rectifier is either an inductor or an LCL-filter. The use of an LCL-filter mitigates the switching ripple injected in the grid...... or complex calculations. Moreover, in the paper particular attention is devoted to the susceptibility of the systems in a high polluting environment....
A Parallel Algorithm of Gaussian Filtering in Time Domain on Fermi%Fermi架构下的时域高斯滤波并行算法
Institute of Scientific and Technical Information of China (English)
何兴无
2012-01-01
In order to speed up the Gaussian filtering in graphics and image processing, based on the analysis of Gaussian filtering and the computing model of Fermi, the design of this parallel Gaussian filtering algorithm on time domain has play a good performance. Test results not only show that the deviation between the GPU and CPU is negligible for most of applications (it is far smaller than 0. 0001) , but also demonstrate the obvious speedup using GPU, that is, it can achieve more than 210 times faster than the CPU implementation for the data size (512×112×128) with the filter size 11.%为提高图形图像处理中高斯滤波算法模块的计算速度,将高斯滤波与Fermi平台相结合,设计了一种高斯滤波时域的并行算法.数据测试结果显示,与基于CPU的实现相比,采用Fermi架构的GPU处理不仅可以得到误差精度小于0.0001的计算结果,而且可以取得较大的加速效果.在数据规模为512×112×128和滤波窗口大小为11的情况下能够达到约210倍的加速效果.
模型不确定非线性Markov跳变系统的滤波算法%Filter algorithm for nonlinear Markov jump systems with uncertain models
Institute of Scientific and Technical Information of China (English)
赵顺毅; 刘飞
2012-01-01
Considering the state estimation problem for the nonlinear Markov jump system with uncertain model, a novel filtering algorithm is proposed. Compared with the traditional interacting multiple particle filter method, in this method, a term of filtering error at previous time instant is introduced to increase the effect of the particles which are true but with small weights due to the inaccuracy model to improve the estimation performance in the filtering process. Simulation results show the effectiveness of this method in handling with the state estimation problem for the nonlinear Markov jump systems with uncertain model parameter.%针对模型不确定非线性Markov跳变系统,提出一种新的滤波算法.相比于传统交互多模型粒子滤波,该方法通过引入前一时刻的滤波误差来增强原先由于不精确模型而造成权值较小的真实粒子在滤波过程中的作用,以此来改善算法的估计性能.仿真结果表明,该方法在处理含不确定模型参数的非线性Markov跳变系统状态估计问题时具有较好的性能.
基于粒子群优化算法的模拟滤波器设计%Design of Analog Filter Based on Particle Swarm Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
李鹏; 马红梅; 张旭珍
2011-01-01
采用传统的网络综合法设计计波器存在带宽不精确及阻带衰减过小的问题,为此,提出一种基于粒子群优化算法的无源模拟滤波器优化设计方法.在网络综合法设计的滤波器电路基础上,利用粒子群优化算法对滤波器的整个参数空间进行高效并行搜索直到获得最优的参数值.实例表明,采用该方法设计的滤波器带宽更加准确,且具有更加陡峭的阻带衰减.%As for the problem of the filter's bandwidth imprecision and stop-band attenuation too small, a passive analog filter optimization design method is proposed based on the Particle Swarm Optimization(PSO) algorithm.The filter is designed by the network synthesis design method, and it optimizes the circuit's parameters in the whole parameters space effectively and globally by PSO until gain the best parameters.This method can improve the filter's bandwidth imprecision and the high stop-band suppression.
Phase-contrast CT: fundamental theorem and fast image reconstruction algorithms
Bronnikov, Andrei V.
2006-08-01
Phase-contrast x-ray computed tomography (CT) is an emerging imaging technique that can be implemented at third generation synchrotron radiation sources or by using a microfocus x-ray tube. Promising experimental results have recently been obtained in material science and biological applications. At the same time, the lack of a mathematical theory comparable to that of conventional absorption-based CT limits the progress in this field. We suggest such a theory and prove a fundamental theorem that plays the same role for phase-contrast CT as the Fourier slice theorem does for absorption-based CT. The fundamental theorem allows us to derive fast image reconstruction algorithms in the form of filtered backprojection (FBP).
Detection of Voltage Disturbance Based on Kalman Filter Algorithm%基于卡尔曼滤波的电压扰动检测算法
Institute of Scientific and Technical Information of China (English)
任文琳; 赵庆生; 何志方
2012-01-01
In order to real-time detect voltage disturbance in power system, the algorithm based on Kalman filter is presented. It applies a new Kalman model to calculate the effective value of voltage signal, which can achieve real-time tracking of the voltage sag and swell by setting the threshold of voltage RMS deviation with comparative analysis of sliding window RMS algorithm. The simulation results prove the real-time function and reliability of Kalman filter algorithm.%为了对电力系统中的电压扰动进行实时监测,提出了一种基于卡尔曼滤波的电压挠动检测算法,该算法采用新的卡尔曼模型求取电网电压信号的有效值,通过设定电压阈值进而实现电压凹陷和电压凸起波形的实时跟踪,并与滑动窗有效值(RMS)算法进行比较分析,仿真验证了该算法的实时性和可靠性.
Directory of Open Access Journals (Sweden)
Amor Chowdhury
2016-09-01
Full Text Available The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.
Chowdhury, Amor; Sarjaš, Andrej
2016-09-15
The presented paper describes accurate distance measurement for a field-sensed magnetic suspension system. The proximity measurement is based on a Hall effect sensor. The proximity sensor is installed directly on the lower surface of the electro-magnet, which means that it is very sensitive to external magnetic influences and disturbances. External disturbances interfere with the information signal and reduce the usability and reliability of the proximity measurements and, consequently, the whole application operation. A sensor fusion algorithm is deployed for the aforementioned reasons. The sensor fusion algorithm is based on the Unscented Kalman Filter, where a nonlinear dynamic model was derived with the Finite Element Modelling approach. The advantage of such modelling is a more accurate dynamic model parameter estimation, especially in the case when the real structure, materials and dimensions of the real-time application are known. The novelty of the paper is the design of a compact electro-magnetic actuator with a built-in low cost proximity sensor for accurate proximity measurement of the magnetic object. The paper successively presents a modelling procedure with the finite element method, design and parameter settings of a sensor fusion algorithm with Unscented Kalman Filter and, finally, the implementation procedure and results of real-time operation.
Research of Image Filtering Algorithm Based on Adaptive Detail Preserving%自适应保细节的图像滤波算法研究
Institute of Scientific and Technical Information of China (English)
王志; 汪青
2016-01-01
本文利用Matlab软件，结合多尺度和多方向模板，提出了一种新的自适应保细节滤波算法，提高了图像信噪比。本文介绍了数字图像处理的方法和常用的去噪模型并给出了一种改进的中值滤波的特征与描述。%This paper used Matlab software, combining with multi-scale and multi-direction template, a new adaptive filtering algorithm was proposed, which improved the image signal to noise ratio.In this paper, we introduced the method of digital image processing and the commonly used denoising model and gave an im-proved median filter.
Yi, Han; Lü, Li-Ming; Zhang, Zhao; Cheng, Wen-Jing; Ji, Wei; Huang, Yan; Zhang, Yan; Li, Hong-Jie; Cui, Yin-Ping; Lin, Ming; Wang, Yi-Jie; Duan, Li-Min; Hu, Rong-Jiang; Xiao, Zhi-Gang
2016-11-01
A Flash-ADC data acquisition (DAQ) system has been developed for the drift chamber array designed for the External-Target-Experiment at the Cooling Storage Ring at the Heavy Ion Research Facility, Lanzhou. The simplified readout electronics system has been developed using the Flash-ADC modules and the whole waveform in the sampling window is obtained, with which the time and energy information can be deduced with an offline processing. A digital filter algorithm has been developed to discriminate the noise and the useful signal. With the digital filtering process, the signal to noise ratio (SNR) is increased and a better time and energy resolution can be obtained. Supported by National Basic Research Program of China (973) (2015CB856903 and 2014CB845405), partly by National Science Foundation of China (U1332207 and 11375094), and by Tsinghua University Initiative Scientific Research Program
Application of Gabor Filter in Fingerprint Enhancement Algorithm%Gabor滤波在指纹增强算法中的应用研究
Institute of Scientific and Technical Information of China (English)
陈婧; 张苏
2015-01-01
In the process of automatic fingerprint identification, fingerprint image enhancement plays a key role. Based on the texture characteristics of fingerprint ridges and Gabor filter's quality in directivity and frequency selectivity, the application of Gabor filter in fingerprint enhancement algorithm is studied and the results show that it can improve the efficiency of automatic fingerprint identification.%在自动指纹识别过程中，指纹图像增强起着关键性的作用。根据指纹自身脊线的纹理特点加之Gabor滤波器在方向性和频率选择性方面的优良特性，对Gabor滤波在指纹增强算法中的应用做了研究，结果表明，能提高自动指纹识别的效率。
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-21
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
CUDA-based algorithm for high-speed parallel Gaussian filtering%基于CUDA的高速并行高斯滤波算法
Institute of Scientific and Technical Information of China (English)
卢文龙; 王建军; 刘晓军
2011-01-01
In order to speed up the Gaussian filtering in three-dimensional surface texture analysis, an efficient method based on compute unified device architecture (CUDA) implemented on graphic processing unit (GPU) was designed. CUDA parallel computing technology was introduced into the surface texture analysis area by analyzing principle of Gaussian filtering and CUDA computing architecture. Parallel Gaussian filtering algorithm used in CUDA was given for the characteristics of low dependency of Gaussian filtering and SIMT (single instruction multiple thread) execution model of CUDA. Experiments prove that the calculation speed based on CUDA is 40 times faster than that of traditional sequence algorithm based on CPU, can effectively improve data processing capability.%为加快表面三维形貌分析中高斯滤波算法的执行速度,提出了一种基于计算统一设备构架(CUDA)的高斯滤波算法来实现高速并行处理.分析高斯滤波算法原理和CUDA并行计算体系,将CUDA并行计算技术引入到表面分析领域.针对高斯滤波数据间依赖性弱和CUDA采用单指令多线程(SIMT)执行模型的特点,总结出适合于CUDA的并行高斯滤波算法流程.实验证明:该方法与CPU串行处理方法相比.其加速比达到40倍以上,可以有效提高数据处理能力.
Institute of Scientific and Technical Information of China (English)
吕振雷; 吴丰
2016-01-01
井下光照不均、煤尘浓度大以及视频图像获取设备电路电压不稳定等各类因素的存在,导致矿井视频监控系统获取的图像存在大量噪声,影响了对矿井各类生产信息的准确判读.为此,将离散小波变换(Discrete wavelet transform,DWT)与改进中值滤波算法相结合,提出了一种矿井视频监控图像高效去噪算法.首先,对获取的矿井视频图像进行自适应噪声检测,根据检测结果,对图像采用改进中值滤波算法处理;然后对滤波后的图像进行3层离散小波变换,鉴于图像的噪声信息绝大部分集中分布于高频分解系数中,故对低频分解系数不作处理;最后对高频分解系数采用一种改进软阈值去噪函数模型进行去噪,将去噪后的高频分解系数与原始低频分解系数进行重构,得到去噪后清晰度较高的图像.采用实地获取的山西潞安某煤矿井下视频图像进行试验,并与小波软阈值去噪、中值滤波等算法进行去噪效果对比分析,此外,对各算法的试验结果分别采用信噪比(Signal noise ratio,SNR)以及算法运行时间进行评价,结果表明:新算法对于矿井视频监控图像的去噪效果优于其余2类算法,且算法运算时间也具有一定的优势.%The existing factors of uneven illumination,coal dust and circuit voltage instability of video surveilance image acquistition devices,resulting in a lot of noises are existed in video surveilance image,the accurate interpretation of mine all kinds of production information is affected. Combined with discrete wavelet transform ( DWT) and improved median filtering al-gorithm,a filtering algorithm of mine video surveilance with high efficiency is proposed. Firstly,according to the distribution characteristics of the noise in mine video surveilance image,the adaptive noise detection operator is proposed,according to the noise detection results,the improved median filtering algorithm is adopted to filtering
Research and Application of a High Speed URL Filtering Algorithm%一种高速URL过滤算法的研究与应用
Institute of Scientific and Technical Information of China (English)
黄诚
2016-01-01
当前，传统防火墙的URL过滤方式只是对于规则库中的URL进行过滤，对于新增的涉黄涉暴网站无能为力，或者管理员响应迟钝。针对当前这种现状，提出一种局域网内URL过滤系统，基于网络爬虫和敏感词过滤技术通过爬去网页文本和对于网页文本分析来判断指定URL是否合法。考虑到匹配效率和本过滤系统所使用的内存空间，使用MD5对URL计算摘要值，在此之上建立黑白名单，再结合Bloom Filter算法和改进的Hash表数据结构用以实现对URL的高速过滤。%Recently, traditional URL filtering firewall rule base only for URL filtering, for the new added website involving violence powerless, or the administrator unresponsive. For this view of the current situation, proposes a URL filtering system within a local area network, which is based on climbing web pages for text and analyzing text to determine the lawfulness of the specified URL, considering the matching effi-ciency of the words and the use of memory space in this system, uses the MD5 digest value calculated on the URL, builds on top of this black and white lists, combining Bloom Filter algorithm with improved HashMap data structure to achieve high speed for URL filtering.
Institute of Scientific and Technical Information of China (English)
Du Zheng-Cong; Tang Bin; Liu Li-Xin
2006-01-01
In this paper, a new observation equation of non-Gaussian frequency selective fading Bell Labs layered space time (BLAST) architecture system is proposed, which is used for frequency selective fading channels and non-Gaussian noise in an application environment of BLAST system. With othogonal matrix triangularization (QR decomposition) of the channel matrix, the static observation equation of frequency selective fading BLAST system is transformed into a dynamic state space model, and then the particle filter is used for space-time layered detection. Making the full use of the finite alphabet of the digital modulation communication signal, the optimal proposal distribution can be chosen to produce particle and update the weight. Incorporated with current method of reducing error propagation, a new space-time layered detection algorithm is proposed. Simulation result shows the validity of the proposed algorithm.
Chen, Chien-Yue; Deng, Qing-Long; Wu, Pei-Jung; Lin, Bor-Shyh; Chang, Hsuan T; Hwang, Hone-Ene; Huang, Guan-Syun
2014-09-20
A speckleless illuminated modified-Gerchberg-Saxton-algorithm-type computer-generated hologram, which adopts a lower frequency of the iterative algorithm and calculation time, is proposed to code a hologram with two signals and position a multiplexing phase-only function, which can reconstruct the left and the right viewing holograms on the pupillary-distance position after the decryption and still maintain the content with high contrast and definition. The reconstructed image quality presents root mean square error of 0.03, with a diffraction efficiency of 87%, and signal-to-noise ratio of 8 dB after the analysis. Furthermore, two denoising techniques for the digital filter and optical suppression are combined, in which the speckle suppression with pseudorandom phase modulation and a rotating diffuser are utilized for successfully reducing the speckle contrast, which was reduced to below 4%. The goal was to reduce visual fatigue for the viewers.
一种新的变步长自适应滤波算法研究%STUDY ON A NEW VARIABLE STEP-SIZE ADAPTIVE FILTERING ALGORITHM
Institute of Scientific and Technical Information of China (English)
马立新; 侯楚林
2012-01-01
To deal with the contradiction between convergence speed and steady-state error in constant step-size adaptive algorithm of the filter in under-modelling working condition, a variable step-size segment proportionale affine projection algorithm, namely VSS-SPAPA,is proposed in this paper. Taking into account the impacts on performance of acoustic echo cancellation by system disturbance noise and under-modelling noise incurred from Ie9s filter weight coefficient number than the length of echo path,the proposed algorithm sets up a new target function by forcing the posterior error to cancel the negative effect of these two kinds of noise. Then using this target function, a whole step-size control approach fitting the proportionate affine projection algorithm is derived. Simulation results confirm that the proposed algorithm can constitute a significant improvement in convergence speed and steady-stale performance in condition of increasing small computation load comparing with the constant step-size proportionate affine projection algorithm and other existed variable step-size algorithms.%针对滤波器在亚模型(under-modeling)工作状态下定步长自适应算法收敛速度和稳态误差之间的矛盾,提出一种变步长分割式比例仿射投影算法( VSS-SPAPA).该算法考虑到系统干扰噪声和滤波器权系数个数小于回声路径长度时引起的亚模型噪声对回声消除系统性能的影响,利用后验误差去补偿这两类噪声的负面作用,建立一个新的目标函数,根据该目标函数,导出一种适用于比例仿射投影算法整体步长的调节方法.仿真结果表明:在增加少量计算量的情况下,新算法的收敛速度和稳态性能与定步长比例仿射投影算法以及已有变步长算法相比得到了明显提高.
鲁棒的单类协同排序算法%Robust Ranking Algorithms for One-class Collaborative Filtering
Institute of Scientific and Technical Information of China (English)
李改; 李磊
2015-01-01
The problem of ranking for one-class collaborative filtering (OCCF) is a research focus. One drawback of the existing ranking algorithms for OCCF is noise sensitivity, because the noisy data of training data might bring big influences to the training process and lead to inaccuracy of the algorithm. In this paper, in order to solve the noise sensitivity problem of the ranking algorithms, we propose two robust ranking algorithms for OCCF by using the pairwise sigmoid/fidelity loss functions that are flexible and can be easily adopted by the popular matrix factorization (MF) model and the K-nearest-neighbor (KNN) model. We use stochastic gradient descent with bootstrap sampling to optimize the two robust ranking algorithms. Experimental results on three practical datasets containing a large number of noisy data show that our proposed algorithms outperform several state-of-the-art ranking algorithms for OCCF in terms of different evaluation metrics.%单类协同过滤(One-class collaborative filtering, OCCF)问题是当前的一大研究热点。之前的研究所提出的算法对噪声数据很敏感,因为训练数据中的噪声数据将给训练过程带来巨大影响,从而导致算法的不准确性。文中引入了Sigmoid 成对损失函数和Fidelity 成对损失函数,这两个函数具有很好的灵活性,能够和当前最流行的基于矩阵分解(Matrix factorization, MF)的协同过滤算法和基于最近邻(K-nearest neighbor, KNN)的协同过滤算法很好地融合在一起,进而提出了两个鲁棒的单类协同排序算法,解决了之前此类算法对噪声数据的敏感性问题。基于Bootstrap 抽样的随机梯度下降法用于优化学习过程。在包含有大量噪声数据点的实际数据集上实验验证,本文提出的算法在各个评价指标下均优于当前最新的单类协同排序算法。
Abuhadi, Nouf; Bradley, David; Katarey, Dev; Podolyak, Zsolt; Sassi, Salem
2014-03-01
Introduction: Single-Photon Emission Computed Tomography (SPECT) is used to measure and quantify radiopharmaceutical distribution within the body. The accuracy of quantification depends on acquisition parameters and reconstruction algorithms. Until recently, most SPECT images were constructed using Filtered Back Projection techniques with no attenuation or scatter corrections. The introduction of 3-D Iterative Reconstruction algorithms with the availability of both computed tomography (CT)-based attenuation correction and scatter correction may provide for more accurate measurement of radiotracer bio-distribution. The effect of attenuation and scatter corrections on accuracy of SPECT measurements is well researched. It has been suggested that the combination of CT-based attenuation correction and scatter correction can allow for more accurate quantification of radiopharmaceutical distribution in SPECT studies (Bushberg et al., 2012). However, The effect of respiratory induced cardiac motion on SPECT images acquired using higher resolution algorithms such 3-D iterative reconstruction with attenuation and scatter corrections has not been investigated. Aims: To investigate the quantitative accuracy of 3D iterative reconstruction algorithms in comparison to filtered back projection (FBP) methods implemented on cardiac SPECT/CT imaging with and without CT-attenuation and scatter corrections. Also to investigate the effects of respiratory induced cardiac motion on myocardium perfusion quantification. Lastly, to present a comparison of spatial resolution for FBP and ordered subset expectation maximization (OSEM) Flash 3D together with and without respiratory induced motion, and with and without attenuation and scatter correction. Methods: This study was performed on a Siemens Symbia T16 SPECT/CT system using clinical acquisition protocols. Respiratory induced cardiac motion was simulated by imaging a cardiac phantom insert whilst moving it using a respiratory motion motor
Collaborative Filtering Recommendation Algorithm Based on Cloud Model%基于云模型的协同过滤推荐算法
Institute of Scientific and Technical Information of China (English)
万年红
2015-01-01
传统的协同过滤推荐算法面临严峻的数据稀疏性和推荐实时性困境，推荐质量明显不高。为提高推荐效果，首先对基于云模型的用户评分项和相似性度量方法展开研究。然后定义基于云模型的推荐系统信任约束，并改进主观信任云模型的约束函数、信任变化云模型的信任变化函数。最后提出一种基于云模型的协同过滤推荐算法。实验结果表明，相比传统算法，该算法在用户评分数据稀疏的状况下仍然可以取得良好的推荐效果，具有较高的实用价值。%The traditional collaborative filtering recommendation algorithms face the dilemma of severe data sparsity and real time of recommendation, their recommendation quality is not obviously high. To improve recommendation efficiency, firstly, user rating items and similarity measurement method based on cloud model were researched. Then the definition of recommendation system trust constraint based on cloud model was given, and improved the constraint function of subjective trust cloud model and trust change function of trust change cloud model. Finally, a collaborative filtering recommendation algorithm based on cloud model was put forword. The experimental results show that the algorithm still obtains good recommendation efficiency on situation of user rating data sparsity compared to the traditional algorithms, it has high utility.
System Design and Implementation of Collaborative Filtering Algorithm%协同过滤算法系统设计与实现
Institute of Scientific and Technical Information of China (English)
汪刚
2015-01-01
Research on the system design and implementation of collaborative filtering algorithm, improve the mechanism of the network is recommended. In the process of network is recommended, and puts forward a kind of collaborative filtering algorithm based on nonlinear neighborhood. In the process of recommendation, according to the actual state of recommendation system, access to recommend the selection object, in the process of computing similarity between objects was recommended. According to the recommended template, choose reasonable recommended group, according to the measurement method of the nonlinear neighborhood complete network recommended, recommended to obtain ideal results. The experimental results show that the algorithm presented in this paper the design system, collaborative recommendation algorithm can improve the rationality of the recommended, meet the practical needs of recommendation system.%研究协同过滤算法系统的设计与实现方法，完善网络推荐机制。在网络推荐的过程中，提出一种基于非线性邻域的协同过滤算法。在推荐的过程中，根据推荐系统的实际状态，获取推荐过程中的选择对象，计算不同推荐对象之间的相似性。根据推荐模板，选取合理的推荐群，根据非线性邻域度量方法，完成网络推荐，获取理想的推荐结果。实验结果表明，利用本文算法设计协同推荐算法系统，可以提高推荐的合理性，满足推荐系统的实际需求。
Institute of Scientific and Technical Information of China (English)
李振博; 徐桂琼; 査九
2014-01-01
Abatract:Considering the sparsity,accuracy and the real-time problem of traditional collaborative filtering recommendation algorithms in electronic commerce system,a new collaborative filtering algorithm based on user spectral clustering is proposed. Firstly,it employs non-negative matrix factorization algorithm to fill the missing ratings. Then,it uses spectral clustering method of improved similarity to cluster users. Finally,it finds the nearest neighbors of the user according to the user's cluster and generates recommendations. Spectral clustering can be performed by off-line,which will accelerate the speed of online recommendation. The experimental results on MovieLens show that the new algorithm improves recommendation quality in MAE,recall and precision.%针对电子商务系统中传统协同过滤推荐算法面临的稀疏性、准确性、实时性等问题，提出了一种基于用户谱聚类的协同过滤推荐算法。首先利用非负矩阵分解的方法对原始稀疏评分矩阵进行平滑处理，然后利用改进相似度的谱聚类方法将用户聚类，最后在用户所属类中寻找最近邻并产生推荐。用户谱聚类过程可离线完成，加快了在线推荐速度。在数据集MovieLens上的实验结果表明，该算法在平均绝对偏差、召回率、准确率等方面都有了较大改善，提高了推荐质量。
基于记忆效应的协同过滤推荐算法%Collaborative Filtering Recommendation Algorithm Based on Memory Effect
Institute of Scientific and Technical Information of China (English)
杨福萍; 王洪国; 董树霞; 赵学臣
2012-01-01
Existing collaborative filtering algorithms can not promptly reflect the change of users' interest. For this reason, this paper introduces the human brain's characteristics of memory and forgetting to personalized recommendation, and proposes a collaborative filtering algorithm based on memory. The effective use of short-term memory reflects users' recent interest. Long-term memory emphasizes the importance of users' early interest. At the same time, it combines the short-term memory with the long-term memory and proposes the reconciled memory, which makes the recommender system adaptively track the change of users' interest. Experimental results show that the proposed algorithm has high quality of precision and rapid convergence rate and that it overcomes the low efficiency of CF, SCF, AUICF algorithms to some extent.%针对传统协同过滤算法无法及时反映用户兴趣变化的情况,将人脑的记忆和遗忘特性引入到个性化推荐中,提出基于记忆效应的协同过滤推荐算法.利用短时记忆体现用户近期兴趣变化,应用长时记忆强调用户早期兴趣的重要性,给出将短时记忆和长时记忆相结合的调和记忆,使推荐系统可以自适应地跟踪用户兴趣变化.实验结果表明,与CF算法、SCF算法和AUICF算法相比,该算法的推荐精度更高、收敛速度更快.
Hybrid collaborative filtering algorithm based on KNN-SVM%基于KNN-SVM的混合协同过滤推荐算法
Institute of Scientific and Technical Information of China (English)
吕成戍; 王维国; 丁永健
2012-01-01
数据稀疏性问题对协同过滤推荐系统的推荐精度有很大影响,为此,融合缺失数据平衡方法,提出了一个基于KNN-SVM的混合协同过滤推荐算法.利用K-最近邻法对训练集中的缺失数据进行填补,然后通过支持向量机交叉验证进行分类,综合两者优点,从而克服数据质量对推荐算法的影响.在标杆数据集上进行了仿真实验,数值结果证明了方法的有效性.%The problem of data sparsenees has great influence on collaborative filtering recommendation system' s accuracy, balance for this missing data fusion method, this paper proposed a hybrid collaborative filtering algorithms based on KNN-SVM. K-nearest neighbor method used the training set to fill the missing data, and then cross-validated by SVM classification. Comprehend advantages both KNN and SVM in order to overcome impact of data quality on the recommended algorithm. The proposed approach was applied to benchmark problems, and the simulation results show it is valid.
基于果蝇优化算法的模拟滤波器设计%Design of Analog Filter Based on Fruit Fly Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
肖正安
2012-01-01
基于粒子群优化算法的无源模拟滤波器优化设计方法容易陷入局部最优,收敛速度慢迭代次数多、运算量大且稳定性不够好。提出果蝇优化算法对滤波器的整个参数空间进行高效并行搜索直到获得最优的参数值,实例仿真表明,采用该方法设计的滤波器在相同的带宽准确度及阻带衰减的情况下,具有更快的运算速度及收敛性能。%The optimum design of passive simulation filters based on Particle Swarm Optimization algorithm has slow convergence velocity and may easily fall into local optimum,more iterative times,large computational complexity,and stability is not good enough.A passive analog filter optimization design method is proposed based on the Fruit Fly Optimization Algorithm（FOA）,and it optimizes the circuit＇s parameters in the whole parameters space effectively and globally by FOA until gain the best parameters.The simulation results on the MATLAB show that our algorithm has global convergence and higher speed of optimization.
The Particle Filter Algorithm Based on Improved Resampling Techonology%基于重采样技术改进的粒子滤波算法
Institute of Scientific and Technical Information of China (English)
李小婷; 史健芳
2016-01-01
There exists some defects in Particle Filter algorithm based on the traditional resampling techonology such as large calculation ,severe particle impoverishment ,low estimation accuracy and so on .To solve these problems , this paper proposes an improved Particle Filter algorithm based on resampling techonology . First , the initial particles need to be processed before resampling to get a new set of particles .Then all the particles are divided into two types :one is medium weight particle collection , the other one is large weight and small weight particle collection .The medium weight particle collection remain unchanged without resamping .However ,the other one collection first has to be judged whether it can satisfy the condition for resampling .Finally ,a linear combination of large weight and small weight particles will be used to get new particles if it meets the resampling requisites .Results of simulation shows that the proposed improved Particle Filter algorithm is feasible .%基于传统重采样技术的粒子滤波算法存在着计算量大、粒子枯竭现象严重、估计精度较差的缺陷，针对这些问题，提出一种基于重采样技术改进的粒子滤波算法（Improved Resample Particle Filter ，IRPF ）。该算法首先在重采样时对粒子进行处理，得到新粒子集；然后对所有的粒子进行分类，得到两类粒子集，对中等权值粒子集不进行重采样；最后对大权值粒子和小权值粒子组成的粒子集先进行判断，若符合重采样条件，则对其使用线性组合方式进行重采样得到新粒子。仿真结果表明，提出的算法是可行的。
Calzado, A; Geleijns, J; Joemai, R M S; Veldkamp, W J H
2014-01-01
Objective: To compare low-contrast detectability (LCDet) performance between a model [non–pre-whitening matched filter with an eye filter (NPWE)] and human observers in CT images reconstructed with filtered back projection (FBP) and iterative [adaptive iterative dose reduction three-dimensional (AIDR 3D; Toshiba Medical Systems, Zoetermeer, Netherlands)] algorithms. Methods: Images of the Catphan® phantom (Phantom Laboratories, New York, NY) were acquired with Aquilion ONE™ 320-detector row CT (Toshiba Medical Systems, Tokyo, Japan) at five tube current levels (20–500 mA range) and reconstructed with FBP and AIDR 3D. Samples containing either low-contrast objects (diameters, 2–15 mm) or background were extracted and analysed by the NPWE model and four human observers in a two-alternative forced choice detection task study. Proportion correct (PC) values were obtained for each analysed object and used to compare human and model observer performances. An efficiency factor (η) was calculated to normalize NPWE to human results. Results: Human and NPWE model PC values (normalized by the efficiency, η = 0.44) were highly correlated for the whole dose range. The Pearson's product-moment correlation coefficients (95% confidence interval) between human and NPWE were 0.984 (0.972–0.991) for AIDR 3D and 0.984 (0.971–0.991) for FBP, respectively. Bland–Altman plots based on PC results showed excellent agreement between human and NPWE [mean absolute difference 0.5 ± 0.4%; range of differences (−4.7%, 5.6%)]. Conclusion: The NPWE model observer can predict human performance in LCDet tasks in phantom CT images reconstructed with FBP and AIDR 3D algorithms at different dose levels. Advances in knowledge: Quantitative assessment of LCDet in CT can accurately be performed using software based on a model observer. PMID:24837275
基于Gabor滤波器族的地震图像增强算法%Seismic image enhancement algorithm based on Gabor filter bank
Institute of Scientific and Technical Information of China (English)
刘天时; 杨雪; 李湘眷
2015-01-01
为在增强地震剖面图像时获取纹线的构造及层序信息，提出一种基于地震剖面图像纹线方向的地震图像增强算法。根据地震剖面纹线的方向性，设计一个Gabor滤波器族，用其对低频分量图进行滤波去噪。利用小波变换与Gabor滤波器族各自的优点，实现地震剖面图像纹线的增强，提高整体运算效率。仿真结果表明，该算法对地震剖面图像处理后，均方误差与峰值信噪比均有明显的改善。%To obtain the structure and sequence information of stripe lines accurately while enhancing the seismic profile image ,a seismic image enhancement algorithm was proposed based on the directions of stripe lines on seismic profile image .Based on the directionality of stripe lines on seismic profile image ,a Gabor filter bank was designed ,by which low frequency component figure was filtered and denoised .With the advantages of wavelet transform and Gabor filter bank ,the enhancement of stripe lines on seismic profile was realized ,which improved overall operational efficiency .The simulation results show that the mean square er‐ror and peak signal to noise ratio are improved obviously after the seismic profile image being processed using this algorithm .
Spears, J. Reid; Schoepf, U. Joseph; Henzler, Thomas; Joshi, Gayatri; Moscariello, Antonio; Vliegenthart, Rozemarijn; Cho, Young Jun; Apfaltrer, Paul; Rowe, Garrett; Weininger, Markus; Ebersberger, Ullrich
2014-01-01
Rationale and Objectives: To investigate the impact of iterative reconstruction in image space (IRIS) on image noise, image quality (10), and postprocessing at coronary computed tomography angiography (cCTA) compared to traditional filtered back-projection (FBP). Materials and Methods: The cCTA resu
Spears, J. Reid; Schoepf, U. Joseph; Henzler, Thomas; Joshi, Gayatri; Moscariello, Antonio; Vliegenthart, Rozemarijn; Cho, Young Jun; Apfaltrer, Paul; Rowe, Garrett; Weininger, Markus; Ebersberger, Ullrich
2014-01-01
Rationale and Objectives: To investigate the impact of iterative reconstruction in image space (IRIS) on image noise, image quality (10), and postprocessing at coronary computed tomography angiography (cCTA) compared to traditional filtered back-projection (FBP). Materials and Methods: The cCTA resu
Application of particle filter algorithm in traveling salesman problem%利用粒子滤波求解旅行商问题
Institute of Scientific and Technical Information of China (English)
吴新杰; 黄国兴
2012-01-01
The existing optimization algorithm for solving the Traveling Salesman Problem (TSP) easily falls into local extremum. To overcome this shortcoming, a new optimization method based on the particle filter, which regarded the searching process of the best route of TSP as a dynamic time-varying system, was brought forward. The basic ideas using particle filter principle to search the best route of TSP were enunciated, and its implementation procedures were given. In order to reduce the possibility of sinking into local extreme, the crossover and mutation operator of Genetic Algorithm ( GA) was introduced into the new optimization algorithm to enhance the variety of particles in the sampling process. Finally, the simulation experiments were performed to prove the validity of the new method. The new optimization method based on particle filter can find better solutions than those of other optimisation algorithms.%针对现有优化算法求解旅行商问题(TSP)时容易陷入局部极值的缺点,提出一种基于粒子滤波的优化搜索算法,该算法将TSP最优路径的搜索过程看成是一个动态时变系统.阐述了利用粒子滤波求解TSP最优路径的基本思想,给出了该方法的具体实现步骤.为了增强算法跳出局部极值的能力,在采样过程中引入了遗传算法的交叉和变异操作来丰富样本的多样性.最后为了验证新算法的有效性,进行了仿真实验,结果表明基于粒子滤波的优化算法能够找到比其他优化算法更好的解.
Navigating Earthquake Physics with High-Resolution Array Back-Projection
Meng, Lingsen
Understanding earthquake source dynamics is a fundamental goal of geophysics. Progress toward this goal has been slow due to the gap between state-of-art earthquake simulations and the limited source imaging techniques based on conventional low-frequency finite fault inversions. Seismic array processing is an alternative source imaging technique that employs the higher frequency content of the earthquakes and provides finer detail of the source process with few prior assumptions. While the back-projection provides key observations of previous large earthquakes, the standard beamforming back-projection suffers from low resolution and severe artifacts. This thesis introduces the MUSIC technique, a high-resolution array processing method that aims to narrow the gap between the seismic observations and earthquake simulations. The MUSIC is a high-resolution method taking advantage of the higher order signal statistics. The method has not been widely used in seismology yet because of the nonstationary and incoherent nature of the seismic signal. We adapt MUSIC to transient seismic signal by incorporating the Multitaper cross-spectrum estimates. We also adopt a "reference window" strategy that mitigates the "swimming artifact," a systematic drift effect in back projection. The improved MUSIC back projections allow the imaging of recent large earthquakes in finer details which give rise to new perspectives on dynamic simulations. In the 2011 Tohoku-Oki earthquake, we observe frequency-dependent rupture behaviors which relate to the material variation along the dip of the subduction interface. In the 2012 off-Sumatra earthquake, we image the complicated ruptures involving orthogonal fault system and an usual branching direction. This result along with our complementary dynamic simulations probes the pressure-insensitive strength of the deep oceanic lithosphere. In another example, back projection is applied to the 2010 M7 Haiti earthquake recorded at regional distance. The
Directory of Open Access Journals (Sweden)
Ren Zhi Ying.
2014-04-01
Full Text Available Dual tree complex wavelet transform (DT-CWT exhibits superiority of shift invariance, directional selectivity, perfect reconstruction (PR, and limited redundancy and can effectively separate various surface components. However, in nano scale the morphology contains pits and convexities and is more complex to characterize. This paper presents an improved approach which can simultaneously separate reference and waviness and allows an image to remain robust against abnormal signals. We included a bilateral filtering (BF stage in DT-CWT to solve imaging problems. In order to verify the feasibility of the new method and to test its performance we used a computer simulation based on three generations of Wavelet and Improved DT-CWT and we conducted two case studies. Our results show that the improved DT-CWT not only enhances the robustness filtering under the conditions of abnormal interference, but also possesses accuracy and reliability of the reference and waviness from the 3-D nano scalar surfaces.
基于粒子滤波的声源方位跟踪算法%SOUND SOURCE ORIENTATION TRACKING ALGORITHM BASED ON PARTICLE FILTERING
Institute of Scientific and Technical Information of China (English)
殷侠; 蔡卫平; 徐健; 陆泽橼
2012-01-01
Aiming at speaker' s orientation tracking issue in reverberant environment, a sound source orientation tracking algorithm based on particle filtering is presented in this paper. According to the moving characteristic of the speaker, the algorithm sets up based on the Lan-gevin equations a dynamic model of sound source orientation, the steered response power with phase transform weight (SRP-PHAT) is used as the localisation function, and the particle filtering is employed to track the sound source orientation. The real acoustic data recorded in a typical meeting room using a small-scale microphone array is used for tracking experiments. Results show that the proposed algorithm can realise effectively the orientation tracking of the randomly walking speaker, in which, the root mean square error of tracking in direction of the horizontal angle and the elevation are all less than 5°.%针对混响环境中说话人方位跟踪问题,提出一种基于粒子滤波的声源方位跟踪算法.该算法根据说话人的运动特点,在Langevin方程的基础上构建声源方位的动态模型,采用相位变换加权的可控响应功率作为定位函数,运用粒子滤波对声源方位进行跟踪.使用典型会议室环境下小型麦克风阵列接收的真实数据来做实验.结果表明,该算法能有效地实现随机走动说话人的方位跟踪,并且在水平角和仰角方向的均方根误差均小于5..
New Adaptive Active Queue Management Algorithm with Kalman Filter%自适应卡尔曼滤波的主动队列管理算法
Institute of Scientific and Technical Information of China (English)
闫巧; 胡晓娟; 雷琼钰
2012-01-01
controller accelerates the regulation speed of the controller through differential factor. But the parameters of PID controller are fixed,they can't be adapted with dynamic network,so the stability of the queue can't be controlled effectively. A new adaptive active queue management(AQM) algorithm with Kalman filter was presented according to the adaptivity of the neural network The new algorithm combines Kalman filter law with neural network, which has the merits of both. It can determinate future queue length based on queue lengths and some rates of change in the queue length. The results of simulation show that the new AQM algorithm is superior to the typical PID controller on the queue stability, time delay and link utilization.%PID控制器通过微分环节加快了控制器的调节速度,但PID的参数是固定的,不能根据动态的网络自调整参数,故不能有效控制队列的稳定性.由于神经元网络有自适应性,提出了一种自适应卡尔曼滤波的主动队列管理算法(adaptive-KF-AQM).它结合卡尔曼滤波和神经元网络方法,根据队列长度及其变化率来估计下一时刻的队列长度,使队列长度在期望值附近波动.仿真结果表明,该算法在队列稳定性、收敛速度、延时和链路利用率等方面都明显优于传统的PID算法.
基于卡尔曼滤波的运动人体跟踪算法研究%Research on Moving Human Tracking Algorithm Based on Kalman Filter
Institute of Scientific and Technical Information of China (English)
乔坤; 郭朝勇; 史进伟
2012-01-01
提出一种基于卡尔曼滤波的运动目标快速跟踪算法.利用卡尔曼滤波器的预测功能,预测运动人体目标在下一帧中的位置,在Matlab仿真环境下实现该跟踪算法,实验结果表明:该算法对人体目标的运动趋势能够做出正确的预测估计,跟踪效果和性能较为稳定和可靠.此外,该算法将图像全局搜索问题转换为局部搜索,使运算量减少,满足实时性跟踪要求,实现了对运动目标的快速跟踪.%A real-time moving object tracking algorithm based on Kalman filter is proposed The possible position of the moving human in the next frame is predicted by Kalman filter's predictive function. Based on the Matlab simulation environment to achieve the tracking algorithm and the experimental results show that the algorithm can correctly estimate the human's motion trend and the tracking results and performance is better. In addition, the global searching scope of an image is converted to local scope, thus reduce the computation and meet the requirements of real-time tracking, and the speedy tracking of moving human is realized.
Directory of Open Access Journals (Sweden)
Salaheddine Derouiche
2013-11-01
Full Text Available In this paper, we present the implemented denoising section in the coding strategy of cochlear implants, the technique used is the technique of wavelet bionic BWT (Bionic Wavelet Transform. We have implemented the algorithm for denoising Raise the speech signal by the hybrid method BWT in the FPGA (Field Programmable Gate Array, Xilinx (Virtex5 XC5VLX110T. In our study, we considered the following: at the beginning, we present how to demonstrate features of this technique. We present an algorithm implementation we proposed, we present simulation results and the performance of this technique in terms of improvement of the SNR (Signal to Noise Ratio. The proposed implementations are realized in VHDL (Very high speed integrated circuits Hardware Description Language. Different algorithms for speech processing, including CIS (Continuous Interleaved Sampling have been implemented the strategy in this processor and tested successfully.
Derivative free filtering using Kalmtool
DEFF Research Database (Denmark)
Bayramoglu, Enis; Hansen, Søren; Ravn, Ole;
2010-01-01
In this paper we present a toolbox enabling easy evaluation and comparison of different filtering algorithms. The toolbox is called Kalmtool 4 and is a set of MATLAB tools for state estimation of nonlinear systems. The toolbox contains functions for extended Kalman filtering as well as for DD1...... filter and the DD2 filter. It also contains functions for Unscented Kalman filters as well as several versions of particle filters. The toolbox requires MATLAB version 7, but no additional toolboxes are required....
基于自适应渐消 EKF 的 FastSLAM 算法%FastSLAM algorithm based on adaptive fading extended Kalman filter
Institute of Scientific and Technical Information of China (English)
刘丹; 段建民; 于宏啸
2016-01-01
快速同时定位与建图（fast simultaneous localization and mapping，FastSLAM）算法的采样过程会带来粒子退化问题，为了改进算法的性能，提高估计精度，从研究粒子滤波的建议分布函数出发，提出基于自适应渐消扩展卡尔曼滤波（adaptive fading extended Kalman filter，AFEKF）的 FastSLAM 算法。该算法基于 FastSLAM的基本框架，利用 AFEKF 产生一种参数可自适应调节的建议分布函数，使其更接近移动机器人的后验位姿概率分布，减缓粒子集的退化。因此在同等粒子数的情况下，该算法有效提高了 SLAM 精度，以此减少所使用的粒子数，降低算法的复杂度。基于模拟器和标准数据集的实验仿真结果验证了该算法的有效性。%Sampling process often causes particle degradation in fast simultaneous localization and mapping (FastSLAM).From the point view of the proposal distribution function,a method named the FastSLAM based on adaptive fading extended Kalman filter is proposed to improve the performance of the algorithm and increase estimation accuracy.It uses the adaptive fading extended Kalman filter (AFEKF)to compute proposal distribu-tion based on the basic framework of FastSLAM,then this proposal distribution is more close to the posterior position of the mobile robot and the degree of particle degradation is reduced.In the case of the same number of particles,the algorithm can effectively improve the accuracy of SLAM.Hence it can reduce the number of parti-cles used in the algorithm and the complexity of the algorithm.The validity of the proposed algorithm is verified by the experimental simulation results based on the simulator and the standard data set.
Research of the Advanced Median Filtering Algorithm Based on FPGA%基于FPGA的改进型中值滤波算法研究
Institute of Scientific and Technical Information of China (English)
沈学利; 王肃国
2014-01-01
On the basis of existing algorithms ,this paper ,adapting 3*3 template ,improve the algorithm .Without reducing the real-time ,it simplifies the hardware structure . As result of simulation and analysis results of experimental ,it can be concluded that the feasibility of this algorithm in image filtering processing ,and can meet the real-time requirement .%在已有算法的基础上，采用3*3窗口，进行算法的改进。在不降低实时性的的前提下，对硬件结构进行简化。该算法通过实验进行仿真验证，并对实验结果分析，得出这种算法在图像中值滤波处理中的可行性，能很好地满足实时性要求。
Directory of Open Access Journals (Sweden)
Xiangwei Guo
2016-02-01
Full Text Available An estimation of the power battery state of charge (SOC is related to the energy management, the battery cycle life and the use cost of electric vehicles. When a lithium-ion power battery is used in an electric vehicle, the SOC displays a very strong time-dependent nonlinearity under the influence of random factors, such as the working conditions and the environment. Hence, research on estimating the SOC of a power battery for an electric vehicle is of great theoretical significance and application value. In this paper, according to the dynamic response of the power battery terminal voltage during a discharging process, the second-order RC circuit is first used as the equivalent model of the power battery. Subsequently, on the basis of this model, the least squares method (LS with a forgetting factor and the adaptive unscented Kalman filter (AUKF algorithm are used jointly in the estimation of the power battery SOC. Simulation experiments show that the joint estimation algorithm proposed in this paper has higher precision and convergence of the initial value error than a single AUKF algorithm.
Institute of Scientific and Technical Information of China (English)
叶军; 陈坚; 石国祥
2011-01-01
针对自标定加速度计组合动基座试验数据中存在的数据异常问题,推导并运用自适应Kalman滤波算法剔除异常数据,通过对不同Kalman滤波算法自标定精度解算结果的均值和标准差进行比较,表明自适应Kalman滤波算法更加有效.%Aiming at the problems of abnormal data in the test data of autonomous calibration accelerometer-unit on dynamicbase,deducing and using adaptive Kalman filtering algorithm eliminates abnormal data, according the comparison of results from calibration precision by different Kalman filtering algorithm, it shows that the adaptive Kalman filtering algorithm is more effective.
Local multi-scale Retinex algorithm based on guided image filtering%图像引导滤波的局部多尺度Retinex算法
Institute of Scientific and Technical Information of China (English)
方帅; 杨静荣; 曹洋; 武鹏飞; 饶瑞中
2012-01-01
Retinex算法是一种用于消除由光照变化给图像所带来的负面影响的图像增强算法.该算法的求解通常需要基于入射分量分段光滑的假设,利用正则化的方法迭代求解,计算效率低.文中基于一项最近提出的研究——“图像引导滤波”,提出一种非迭代的Retinex算法框架.基于反射分量也满足分段光滑的假设,采用两次图像引导滤波克服了图像噪声所带来的影响.然后在基于小波变换域图像融合策略的基础上,提出基于图像引导滤波的多尺度Retinex算法,实现图像细节增强与颜色保真之间的平衡.实验结果表明,与各种算法相比,该算法在克服噪声、细节增强和颜色保真方面能够取得更好的效果.%Retinex algorithm deals with the removal of unfavorable illumination effects from a given image. Solving it is typically done by introducing a regularization that forces a spatial smoothness on the illumination, which is computational expensive. In this paper we propose a non-iterative retinex algorithm based on a recent "guided image filter" . Assuming a spatial smoothness on the reflectance, a method using two guided image filters is applied to eliminate artifacts caused by noise. Then, a multi-resolution framework combining guided image filtering and wavelet thresholding, is presented. Our framework is very effective in achieving a trade-off between detail enhancement and color constancy. Compared to other enhancement algorithms, our results verify the new approach's efficiency in eliminating artifacts caused by noise, detail enhancement, and color constancy.
X-ray Liquid Images Filtering Algorithm Based on Hybrid Filter%基于混合滤波器的X射线液体图像滤波算法
Institute of Scientific and Technical Information of China (English)
杨希; 杨立瑞
2013-01-01
According to the properties of noise and the features of grayscale variation in dual - energy X - ray liquid images , the paper proposed a method combining improved adaptive median filtering and NL - means filtering with noise statistic estimator to cancel both Gaussian noise and pulse noise. Firstly, image pixels are classified into impulse noisy pixels、edge pixels and Gaussian noisy pixels by noise detection operators. The paper a-dopted improved adaptive median filtering to process impulse noisy pixels and edge pixels, NL - means filtering with noise statistic estimator to process Gaussian noisy pixels. Then, a method of image quality assessment based on material feature curves is applied, the results are compared with the calibration curve. Extensive simulation experiments demonstrate that new algorithm can preserve edge detail, material features and save time. The method of image quality assessment combines objective and subjective factors to meet needs of practical applications.%针对双能X射线液体图像灰度连续变化的特性和主要受高斯噪声、脉冲噪声干扰的特点,提出一种改进的自适应中值与非局部均值结合的滤波方法.首先,利用改进自适应中值方法滤除噪声检测出的边缘、脉冲噪声污染区的噪声；再利用带有噪声估计的非局部均值方法(NL-means)平滑高斯噪声.提出了基于材料特征曲线的滤波器性能评价方法,滤波结果与标准曲线进行对比.实验结果表明,提出的滤波器保护了边缘、材料信息,提高了速度；滤波器评价方法考虑主观与客观因素,满足了应用需要.
Directory of Open Access Journals (Sweden)
Mona M.Jamjoom
2016-05-01
Full Text Available Noisy training data have a huge negative impact on machine learning algorithms. Noise-filtering algorithms have been proposed to eliminate such noisy instances. In this work, we empirically show that the most popular noise-filtering algorithms have a large False Positive (FP error rate. In other words, these noise filters mistakenly identify genuine instances as outliers and eliminate them. Therefore, we propose more conservative outlier identification criteria that improve the FP error rate and, thus, the performance of the noise filters. With the new filter, an instance is eliminated if and only if it is misclassified by a mutual decision of Naïve Bayesian (NB classifier and the original filtering criteria being used. The number of genuine instances that are incorrectly eliminated is reduced as a result, thereby improving the classification accuracy.
Ziegler, Andy; Köhler, Thomas; Nielsen, Tim; Proksa, Roland
2006-12-01
In cone-beam transmission tomography the measurements are performed with a divergent beam of x-rays. The reconstruction with iterative methods is an approach that offers the possibility to reconstruct the corresponding images directly from these measurements. Another approach based on spherically symmetric basis functions (blobs) has been reported with results demonstrating a better image quality for iterative reconstruction algorithms. When combining the two approaches (i.e., using blobs in iterative cone-beam reconstruction of divergent rays) the problem of blob sampling without introducing aliasing must be addressed. One solution to this problem is to select a blob size large enough to ensure a sufficient sampling, but this prevents a high resolution reconstruction, which is not desired. Another solution is a heuristic low-pass filtering, which removes this aliasing, but neglects the different contributions of blobs to the absorption depending on the spatial position in the volume and, therefore, cannot achieve the best image quality. This article presents a model of sampling the blobs which is motivated by the beam geometry. It can be used for high resolution reconstruction and can be implementedefficiently.
Signal processing of image noise filtering algorithm based on MA TLAB%基于MATLAB滤波算法对图像噪声信号处理的实现
Institute of Scientific and Technical Information of China (English)
肖玉芝
2012-01-01
结合中值滤波和均值滤波算法，通过MATLAB语言设计程序，对嵌入了椒盐和高斯噪声的图像进行滤波处理。结果表明，中值滤波方法适于去除椒盐噪声，同时能较好保护图像边界，均值滤波适合于去除高斯噪声。%Combination of median filter and mean filter algorithm, embedded in the image of the Salt & Pepper and Gaussian noise filtered by the MATEAB language program. The results show that Median filtering method is suitable to remove the salt and pepper noise, and better protect the image boundary, the mean filter for removal of Gaussian noise.
Resampling algorithm for particle filter based on layered transacting MCMC%基于分层转移的粒子滤波MCMC重采样算法
Institute of Scientific and Technical Information of China (English)
田隽; 钱建生; 李世银
2011-01-01
针对粒子滤波中如何设计重采样策略以解决“权值蜕化”，同时又可避免“样本贫化”的问题，提出一种基于分层转移的Monte Carlo Markov链（MCMC）重采样算法．当样本容量检测出现“蜕化”时，将样本集按权值蜕化程度进行分层，利用提出的变异繁殖算法，将其与PSO融合产生MCMC转移核，并施以分层子集；然后通过Metroplis—Hastings算法进行接收-拒绝采样，由此构建的Markov链可收敛到与目标真实后验等价的平稳分布．数值仿真结果表明，所提出的算法能以更快的收敛速度和更小的估计误差贴近目标真%To resolve weight degeneracy and avoid sample impoverishment in resampling algorithms of particle filter, a method, named layered transacting MCMC-resampling algorithm, is proposed. When the effective sample size is below a fixed threshold, particles are dived into two sample subsets according to their individual weights. Mutation operator and PSO, which are considered as transition kernels of MCMC, are applied to sample subsets respectively. Then an acceptancerejection rule of Metropolis-Hastings algorithm is used to generate the Markov chain with the stationary distribution which is equivalent to target posterior density. The simulation results show that the proposed method is superior to other resampling algorithms both in accuracy and convergence speed.
A Kind of Personalized Collaborative Filtering Hybrid Recommendation Algorithm%一种个性化协同过滤混合推荐算法
Institute of Scientific and Technical Information of China (English)
蒋宗礼; 汪瑜彬
2016-01-01
传统协同过滤算法主要根据稀疏的评分矩阵向用户作出推荐，存在推荐质量较差的问题。为此，提出一种基于信息熵的综合项目相似度度量方法。考虑到用户的兴趣会随时间发生变化，而且不同用户群体的兴趣变化不同，受艾宾浩斯记忆遗忘规律启发，提出适应于不同用户群体兴趣变化的数据权重。基于movielens数据集的实验结果表明，改进后算法不仅能缓解评分数据稀疏问题，而且能提高算法的准确率。%T raditional collaborative filtering algorithm exists poor recommendation quality for recommending to the user based solely on sparse rating matrix .A new comprehensive item similarity measurement algorithm based on information entropy is proposed in this paper to dispose the data sparse problem .Meanwhile ,taking into account the user's interest will change over time ,and the change is not same in different user groups ,this paper put forward the weight of adapt to different user interest changes ,which is inspired by Ebbinghaus's memory rule .The performed experiment based on the dataset of movielens shows that the modified algorithm can not only alleviate the problem of rating data sparse ,but also can improve the accuracy of the algorithm .
Novelli, Antonio
2016-08-01
Leaf Area Index (LAI) is essential in ecosystem and agronomic studies, since it measures energy and gas exchanges between vegetation and atmosphere. In the last decades, LAI values have widely been estimated from passive remotely sensed data. Common approaches are based on semi-empirical/statistic techniques or on radiative transfer model inversion. Although the scientific community has been providing several LAI retrieval methods, the estimated results are often affected by noise and measurement uncertainties. The sequential data assimilation theory provides a theoretical framework to combine an imperfect model with incomplete observation data. In this document a data fusion Kalman filter algorithm is proposed in order to estimate the time evolution of LAI by combining MODIS LAI data and PROBA-V surface reflectance data. The reflectance data were linked to LAI by using the Reduced Simple Ratio index. The main working hypotheses were lacking input data necessary for climatic models and canopy reflectance models.
Mikhaylova, E.; Kolstein, M.; De Lorenzo, G.; Chmeissani, M.
2014-07-01
A novel positron emission tomography (PET) scanner design based on a room-temperature pixelated CdTe solid-state detector is being developed within the framework of the Voxel Imaging PET (VIP) Pathfinder project [1]. The simulation results show a great potential of the VIP to produce high-resolution images even in extremely challenging conditions such as the screening of a human head [2]. With unprecedented high channel density (450 channels/cm3) image reconstruction is a challenge. Therefore optimization is needed to find the best algorithm in order to exploit correctly the promising detector potential. The following reconstruction algorithms are evaluated: 2-D Filtered Backprojection (FBP), Ordered Subset Expectation Maximization (OSEM), List-Mode OSEM (LM-OSEM), and the Origin Ensemble (OE) algorithm. The evaluation is based on the comparison of a true image phantom with a set of reconstructed images obtained by each algorithm. This is achieved by calculation of image quality merit parameters such as the bias, the variance and the mean square error (MSE). A systematic optimization of each algorithm is performed by varying the reconstruction parameters, such as the cutoff frequency of the noise filters and the number of iterations. The region of interest (ROI) analysis of the reconstructed phantom is also performed for each algorithm and the results are compared. Additionally, the performance of the image reconstruction methods is compared by calculating the modulation transfer function (MTF). The reconstruction time is also taken into account to choose the optimal algorithm. The analysis is based on GAMOS [3] simulation including the expected CdTe and electronic specifics.
Maneuvering Target Tracking Algorithm Based on Adaptive Fading Kalman Filter%基于改进 Singer 模型的机动目标跟踪方法
Institute of Scientific and Technical Information of China (English)
张燕; 柳超; 李云鹏
2015-01-01
Considering the divergence and poor precision due to inaccurate modeling of the system or noise, an a-daptive fading kalman filter based on singer model (AKF - Singer)is proposed. It adopts a fading factor to restrain the memory length of kalman filter. Simulation shows that the proposed algorithm can effectively restrain the diver-gence of filtering, and is more steady and more precise compared to traditional singer model.%Singer 模型使用标准卡尔曼滤波器对机动目标进行跟踪，当系统模型不准确或噪声统计特性不确定时，容易引起滤波发散或跟踪精度下降等问题。针对这种情况，本文提出了一种采用自适应渐消卡尔曼滤波的 Singer 模型算法(AKF Singer)，通过引入渐消因子来抑制滤波器的记忆长度，自适应的调整新息权重和滤波器增益，从而避免发散。仿真结果表明，本文所提算法能够有效抑制滤波发散，相比于传统 Singer 模型，具有更好的跟踪稳定性和更高的跟踪精度。
基于模糊支持度采样的粒子滤波算法%An Improved Particle Filtering Algorithm Based on Fuzzy Support Degree Sampling
Institute of Scientific and Technical Information of China (English)
李小偎; 胡振涛; 潘泉; 张洪才
2012-01-01
针对粒子滤波中重采样导致粒子多样性减弱造成的滤波精度下降问题,给出了一种基于模糊支持度采样的改进粒子滤波算法;该算法在重采样过程后,首先根据MCMC (Markov Chain Monte Carlo)原理抽取候选粒子,然后依据重采样粒子和候选粒子自身数据中的蕴含信息,并结合模糊理论构建支持度函数和支持度矩阵,以充分地提取数据中的有效信息,在增强粒子多样性的同时实现其对于粒子的优选；最后仿真结果表明,该算法可有效地提高对于系统状态的估计精度.%By the analysis that the degeneracy of particles diversity in the course of re-sampling causes the descent of filtering precision, an improved particle filtering algorithm based on fuzzy support degree sampling is proposed. After the re-sampling process, the new algorithm firstly extracts candidate particles based on Markov Chain Monte Carlo principle, and then, according to the information implicated from the re-sampling and candidate particles and fuzzy theory establishes support degree function and matrix to make fully use of effective information. Thereby the increasing of the diversity of particles and the optimizing selection of particles are realized. Finally, simulation results show the method can effectively improve state estimation precision.
Study on the Spline Filter Algorithm in Ballistic Missile Boost-phase%弹道导弹主动段样条滤波算法
Institute of Scientific and Technical Information of China (English)
张峰; 田康生
2012-01-01
对弹道导弹主动段进行跟踪是弹道导弹主动段防御中极其重要的任务,它是制导拦截的基础.针对传统方法在弹道导弹主动段跟踪能力不足,建立了弹道导弹主动段样条滤波算法.该滤波算法首先用样条函数建立了主动段运动模型,其次在此基础上将运动状态进行了解耦,建立了状态方程,最后基于解耦模型,应用Kalman滤波进行了状态估计,并且在估计中设计了模型更新方法,使算法具有很好的机动跟踪性能.仿真实验证明,该跟踪算法估计精度高于其它算法.%The tracking of ballistic missile under boost-phase is extremely important in the missile early defense program. It is the base of interception. In respect that traditional methods have essential deficiency on the tracking of ballistic missile under boost-phase, this paper introduces a filter algorithm based on spline modeling. First, a ballistic missile under boost-phase motion model is established based on spline function. Then movement state is decoupled. Equation of State is established. At last, based on the decoupling model, state is estimated by using Kalman Filter. This paper has designed a model updating method in order to make it capable of maneuver target tracking. The simulation result shows that this algorithm has high precision and good performance.
一种双重过滤式特征选择算法%New feature selection algorithm based on two-phase filter
Institute of Scientific and Technical Information of China (English)
计智伟; 胡珉
2011-01-01
特征选择是模式识别和机器学习领域的重要问题.针对目前Filter和Wrapper方法,以及传统二阶段组合式方法存在的缺陷,提出了一种双重过滤式特征选择方法FSTPF,并在三个国际公认数据集和一个盾构隧道施工实时数据集上进行了验证测试.实验结果表明,FSTPF算法降维效果好,且获得的优化特征子集的分类准确率得到了提高.%Feature selection is an important problem in the pattern recognition and machine learning areas.Aimed at the question that there are some shortcomings in the actual Filter, Wrapper and tradictional two-phase combined methods, this paper proposes a Feature Selection algorithm based on Two-Phase Filter(FSTPF),and it is used to test in three international accepted datasets and a shield tunneling construncting real-time dataset.The emulational experiment shows that FSTPF can get good effect of reducting dimension and improve the classification accuracy of best feature subset.
An improved Mean Shift algorithm used for point cloud data filtering%一种改进的Mean Shift点云数据滤波
Institute of Scientific and Technical Information of China (English)
孙正林; 邹峥嵘; 吴爱琴
2011-01-01
Point cloud data filter is the key to the quality of three-dimensional reconstruction. The paperproposed an improved Mean Shift algorithm to process point cloud data directly to avoid spending a lot oftime to establish topological relations between point clouds during filtering which makes every point movedto the maximum of the kernel density estimate function quickly, so as to achieve the purpose of reducingnoise.%点云数据滤波是三维重建质量好坏的关键,为了避免在进行滤波时花费大量计算时间建立点云间的拓扑关系,提出利用改进的Mean Shift算法直接对点云数据进行处理,使其快速移动到核密度估计函数的最大值点,从而达到降低噪声的目的.
采用八方向Gabor滤波的指纹识别算法%Fingerprint Recognition Algorithm Based on the Eight Directions Gabor Filter
Institute of Scientific and Technical Information of China (English)
毛元; 冯桂; 汤继生
2013-01-01
This paper solves the center point localization on poor quality fingerprints effectively, by using the fingerprint image enhancement algorithm coupled with the localization of core point by the complex filters, and realizes the translation invariant. In addition, the eight directions Gabor filter can capture the global and local ridge structures by using the method on structure-based fingerprint feature extraction, and realize the rotation invariant The simulation experimental results based on the FVC2004 fingerprint database show that the performance of the paper is better than that of the single finger code-based and the minutiae-based method.%采用指纹增强算法与复滤波器中心点定位结合的方法,有效地解决低质量指纹图像中心点定位问题,实现了平移不变.同时,采用基于结构的指纹特征提取算法,用八方向Gabor滤波提取指纹的全局特征和局部脊线特征,实现了旋转不变.在FVC2004指纹库上的仿真实验表明:该算法取得较好的识别效果,优于基于单Finger Code特征和基于点模式的指纹识别算法.
融合争议度特征的协同过滤推荐算法%Collaborative Filtering Algorithm Combining the Feature of Controversy
Institute of Scientific and Technical Information of China (English)
张学胜; 陈超; 张迎峰; 俞能海
2012-01-01
基于项目的协同过滤推荐算法在电子商务中有着广泛的引用,该算法的核心是计算项目之间的相似度.传统的计算项目相似度算法仅仅通过项目间共同用户评分值差异来计算,在数据稀疏情况下,项目间共同用户评分值很少,导致此类算法性能严重下降.针对此问题,从项目间的整体评分角度出发,提出争议相似度的概念,争议相似度从项目间评分方差差异的角度衡量项目间相似性.将争议度特征融合到基于项目之间共同用户评分的传统相似度算法中,进而提出了融合项目争议度特征的协同过滤推荐算法,最终缓解了传统算法在稀疏数据情况下相似度计算不准确的问题.实验结果表明该算法在数据稀疏环境下可以明显提升推荐质量.%Item-based Collaborative Filtering (CF) algorithm has been widely used in e-commerce. The most critical component of the algorithm is bow to measure the similarity between items. Traditional calculations of similarities relied on the scores of the items that two users both rated, which suffers from data sparsity and poor prediction quality problems. In this paper, we consider the whole ratings between items and propose the conception of "Item Controversy Similarity(ICS)" .which measures the items' similarity by calculating the divergence of variance of the rating values between items. Combing the ICS to the traditional similarity calculation algorithm, we propose a new CF algorithm, which could reduce the inaccurate similarity in data sparsity. Empirical studies on dataset MovieLens show that algorithm outperforms other state-of-the-art CF algorithms and it is more robust against data sparsity.
Automated Integrated Analog Filter Design Issues
2015-01-01
An analysis of modern automated integrated analog circuits design methods and their use in integrated filter design is done. Current modern analog circuits automated tools are based on optimization algorithms and/or new circuit generation methods. Most automated integrated filter design methods are only suited to gmC and switched current filter topologies. Here, an algorithm for an active RC integrated filter design is proposed, that can be used in automated filter designs. The algorithm is t...
A modified OSEM algorithm for PET reconstruction using wavelet processing.
Lee, Nam-Yong; Choi, Yong
2005-12-01
Ordered subset expectation-maximization (OSEM) method in positron emission tomography (PET) has been very popular recently. It is an iterative algorithm and provides images with superior noise characteristics compared to conventional filtered backprojection (FBP) algorithms. Due to the lack of smoothness in images in OSEM iterations, however, some type of inter-smoothing is required. For this purpose, the smoothing based on the convolution with the Gaussian kernel has been used in clinical PET practices. In this paper, we incorporated a robust wavelet de-noising method into OSEM iterations as an inter-smoothing tool. The proposed wavelet method is based on a hybrid use of the standard wavelet shrinkage and the robust wavelet shrinkage to have edge preserving and robust de-noising simultaneously. The performances of the proposed method were compared with those of the smoothing methods based on the convolution with Gaussian kernel using software phantoms, physical phantoms, and human PET studies. The results demonstrated that the proposed wavelet method provided better spatial resolution characteristic than the smoothing methods based on the Gaussian convolution, while having comparable performance in noise removal.
An FBP image reconstruction algorithm for x-ray differential phase contrast CT
Qi, Zhihua; Chen, Guang-Hong
2008-03-01
Most recently, a novel data acquisition method has been proposed and experimentally implemented for x-ray differential phase contrast computed tomography (DPC-CT), in which a conventional x-ray tube and a Talbot-Lau type interferometer were utilized in data acquisition. The divergent nature of the data acquisition system requires a divergent-beam image reconstruction algorithm for DPC-CT. This paper focuses on addressing this image reconstruction issue. We developed a filtered backprojection algorithm to directly reconstruct the DPC-CT images from acquired projection data. The developed algorithm allows one to directly reconstruct the decrement of the real part of the refractive index from the measured data. In order to accurately reconstruct an image, the data need to be acquired over an angular range of at least 180° plus the fan-angle. Different from the parallel beam data acquisition and reconstruction methods, a 180° rotation angle for data acquisition system does not provide sufficient data for an accurate reconstruction of the entire field of view. Numerical simulations have been conducted to validate the image reconstruction algorithm.
Performance of Hull-Detection Algorithms For Proton Computed Tomography Reconstruction
Schultze, Blake; Censor, Yair; Schulte, Reinhard; Schubert, Keith Evan
2014-01-01
Proton computed tomography (pCT) is a novel imaging modality developed for patients receiving proton radiation therapy. The purpose of this work was to investigate hull-detection algorithms used for preconditioning of the large and sparse linear system of equations that needs to be solved for pCT image reconstruction. The hull-detection algorithms investigated here included silhouette/space carving (SC), modified silhouette/space carving (MSC), and space modeling (SM). Each was compared to the cone-beam version of filtered backprojection (FBP) used for hull-detection. Data for testing these algorithms included simulated data sets of a digital head phantom and an experimental data set of a pediatric head phantom obtained with a pCT scanner prototype at Loma Linda University Medical Center. SC was the fastest algorithm, exceeding the speed of FBP by more than 100 times. FBP was most sensitive to the presence of noise. Ongoing work will focus on optimizing threshold parameters in order to define a fast and effic...