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Sample records for adaptive noise-reduction filters

  1. Musical noise reduction using an adaptive filter

    Science.gov (United States)

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

    2003-10-01

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

  2. Adaptive Subband Filtering Method for MEMS Accelerometer Noise Reduction

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    Piotr PIETRZAK

    2008-12-01

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

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

    International Nuclear Information System (INIS)

    Hilts, Michelle; Jirasek, Andrew

    2008-01-01

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

  4. Binaural noise reduction via cue-preserving MMSE filter and adaptive-blocking-based noise PSD estimation

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    Azarpour, Masoumeh; Enzner, Gerald

    2017-12-01

    Binaural noise reduction, with applications for instance in hearing aids, has been a very significant challenge. This task relates to the optimal utilization of the available microphone signals for the estimation of the ambient noise characteristics and for the optimal filtering algorithm to separate the desired speech from the noise. The additional requirements of low computational complexity and low latency further complicate the design. A particular challenge results from the desired reconstruction of binaural speech input with spatial cue preservation. The latter essentially diminishes the utility of multiple-input/single-output filter-and-sum techniques such as beamforming. In this paper, we propose a comprehensive and effective signal processing configuration with which most of the aforementioned criteria can be met suitably. This relates especially to the requirement of efficient online adaptive processing for noise estimation and optimal filtering while preserving the binaural cues. Regarding noise estimation, we consider three different architectures: interaural (ITF), cross-relation (CR), and principal-component (PCA) target blocking. An objective comparison with two other noise PSD estimation algorithms demonstrates the superiority of the blocking-based noise estimators, especially the CR-based and ITF-based blocking architectures. Moreover, we present a new noise reduction filter based on minimum mean-square error (MMSE), which belongs to the class of common gain filters, hence being rigorous in terms of spatial cue preservation but also efficient and competitive for the acoustic noise reduction task. A formal real-time subjective listening test procedure is also developed in this paper. The proposed listening test enables a real-time assessment of the proposed computationally efficient noise reduction algorithms in a realistic acoustic environment, e.g., considering time-varying room impulse responses and the Lombard effect. The listening test outcome

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

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

    2011-11-15

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  7. Noise reduction with complex bilateral filter.

    Science.gov (United States)

    Matsumoto, Mitsuharu

    2017-12-01

    This study introduces a noise reduction technique that uses a complex bilateral filter. A bilateral filter is a nonlinear filter originally developed for images that can reduce noise while preserving edge information. It is an attractive filter and has been used in many applications in image processing. When it is applied to an acoustical signal, small-amplitude noise is reduced while the speech signal is preserved. However, a bilateral filter cannot handle noise with relatively large amplitudes owing to its innate characteristics. In this study, the noisy signal is transformed into the time-frequency domain and the filter is improved to handle complex spectra. The high-amplitude noise is reduced in the time-frequency domain via the proposed filter. The features and the potential of the proposed filter are also confirmed through experiments.

  8. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

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    Álvaro Moreno

    2014-08-01

    Full Text Available Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted local regression filter (LOESS and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG, smoothing spline (SSP, Interpolation for Data Reconstruction (IDR and adaptive Savitzky-Golay (ASG. This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20% reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal

  9. Noise Reduction of Measurement Data using Linear Digital Filters

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    Hitzmann B.

    2007-12-01

    Full Text Available In this paper Butterworth, Chebyshev (Type I and II and Elliptic digital filters are designed for signal noise reduction. On-line data measurements of substrate concentration from E. coli fed-batch cultivation process are used. Application of the designed filters leads to a successful noise reduction of on-line glucose measurements. The digital filters presented here are simple, easy to implement and effective - the used filters allow for a smart compromise between signal information and noise corruption.

  10. Adaptive noise reduction circuit for a sound reproduction system

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    Engebretson, A. Maynard (Inventor); O'Connell, Michael P. (Inventor)

    1995-01-01

    A noise reduction circuit for a hearing aid having an adaptive filter for producing a signal which estimates the noise components present in an input signal. The circuit includes a second filter for receiving the noise-estimating signal and modifying it as a function of a user's preference or as a function of an expected noise environment. The circuit also includes a gain control for adjusting the magnitude of the modified noise-estimating signal, thereby allowing for the adjustment of the magnitude of the circuit response. The circuit also includes a signal combiner for combining the input signal with the adjusted noise-estimating signal to produce a noise reduced output signal.

  11. Noise Reduction with Optimal Variable Span Linear Filters

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Benesty, Jacob; Christensen, Mads Græsbøll

    2016-01-01

    In this paper, the problem of noise reduction is addressed as a linear filtering problem in a novel way by using concepts from subspace-based enhancement methods, resulting in variable span linear filters. This is done by forming the filter coefficients as linear combinations of a number...... included in forming the filter. Using these concepts, a number of different filter designs are considered, like minimum distortion, Wiener, maximum SNR, and tradeoff filters. Interestingly, all these can be expressed as special cases of variable span filters. We also derive expressions for the speech...... demonstrate the advantages and properties of the variable span filter designs, and their potential performance gain compared to widely used speech enhancement methods....

  12. SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation

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    Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D [Washington University School of Medicine, St Louis, MO (United States)

    2016-06-15

    Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and

  13. SU-C-207B-02: Maximal Noise Reduction Filter with Anatomical Structures Preservation

    International Nuclear Information System (INIS)

    Maitree, R; Guzman, G; Chundury, A; Roach, M; Yang, D

    2016-01-01

    Purpose: All medical images contain noise, which can result in an undesirable appearance and can reduce the visibility of anatomical details. There are varieties of techniques utilized to reduce noise such as increasing the image acquisition time and using post-processing noise reduction algorithms. However, these techniques are increasing the imaging time and cost or reducing tissue contrast and effective spatial resolution which are useful diagnosis information. The three main focuses in this study are: 1) to develop a novel approach that can adaptively and maximally reduce noise while preserving valuable details of anatomical structures, 2) to evaluate the effectiveness of available noise reduction algorithms in comparison to the proposed algorithm, and 3) to demonstrate that the proposed noise reduction approach can be used clinically. Methods: To achieve a maximal noise reduction without destroying the anatomical details, the proposed approach automatically estimated the local image noise strength levels and detected the anatomical structures, i.e. tissue boundaries. Such information was used to adaptively adjust strength of the noise reduction filter. The proposed algorithm was tested on 34 repeating swine head datasets and 54 patients MRI and CT images. The performance was quantitatively evaluated by image quality metrics and manually validated for clinical usages by two radiation oncologists and one radiologist. Results: Qualitative measurements on repeated swine head images demonstrated that the proposed algorithm efficiently removed noise while preserving the structures and tissues boundaries. In comparisons, the proposed algorithm obtained competitive noise reduction performance and outperformed other filters in preserving anatomical structures. Assessments from the manual validation indicate that the proposed noise reduction algorithm is quite adequate for some clinical usages. Conclusion: According to both clinical evaluation (human expert ranking) and

  14. CT urography in the urinary bladder: To compare excretory phase images using a low noise index and a high noise index with adaptive noise reduction filter

    International Nuclear Information System (INIS)

    Takeyama, Nobuyuki; Hayashi, Takaki; Ohgiya, Yoshimitsu

    2011-01-01

    Background: Although CT urography (CTU) is widely used for the evaluation of the entire urinary tract, the most important drawback is the radiation exposure. Purpose: To evaluate the effect of a noise reduction filter (NRF) using a phantom and to quantitatively and qualitatively compare excretory phase (EP) images using a low noise index (NI) with those using a high NI and postprocessing NRF (pNRF). Material and Methods: Each NI value was defined for a slice thickness of 5 mm, and reconstructed images with a slice thickness of 1.25 mm were assessed. Sixty patients who were at high risk of developing bladder tumors (BT) were divided into two groups according to whether their EP images were obtained using an NI of 9.88 (29 patients; group A) or an NI of 20 and pNRF (31 patients; group B). The CT dose index volume (CTDI vol ) and the contrast-to-noise ratio (CNR) of the bladder with respect to the anterior pelvic fat were compared in both groups. Qualitative assessment of the urinary bladder for image noise, sharpness, streak artifacts, homogeneity, and the conspicuity of polypoid or sessile-shaped BTs with a short-axis diameter greater than 10 mm was performed using a 3-point scale. Results: The phantom study showed noise reduction of approximately 40% and 76% dose reduction between group A and group B. CTDI vol demonstrated a 73% reduction in group B (4.6 ± 1.1 mGy) compared with group A (16.9 ± 3.4 mGy). The CNR value was not significantly different (P = 0.60) between group A (16.1 ± 5.1) and group B (16.6 ± 7.6). Although group A was superior (P < 0.01) to group B with regard to image noise, other qualitative analyses did not show significant differences. Conclusion: EP images using a high NI and pNRF were quantitatively and qualitatively comparable to those using a low NI, except with regard to image noise

  15. Adaptive EMG noise reduction in ECG signals using noise level approximation

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    Marouf, Mohamed; Saranovac, Lazar

    2017-12-01

    In this paper the usage of noise level approximation for adaptive Electromyogram (EMG) noise reduction in the Electrocardiogram (ECG) signals is introduced. To achieve the adequate adaptiveness, a translation-invariant noise level approximation is employed. The approximation is done in the form of a guiding signal extracted as an estimation of the signal quality vs. EMG noise. The noise reduction framework is based on a bank of low pass filters. So, the adaptive noise reduction is achieved by selecting the appropriate filter with respect to the guiding signal aiming to obtain the best trade-off between the signal distortion caused by filtering and the signal readability. For the evaluation purposes; both real EMG and artificial noises are used. The tested ECG signals are from the MIT-BIH Arrhythmia Database Directory, while both real and artificial records of EMG noise are added and used in the evaluation process. Firstly, comparison with state of the art methods is conducted to verify the performance of the proposed approach in terms of noise cancellation while preserving the QRS complex waves. Additionally, the signal to noise ratio improvement after the adaptive noise reduction is computed and presented for the proposed method. Finally, the impact of adaptive noise reduction method on QRS complexes detection was studied. The tested signals are delineated using a state of the art method, and the QRS detection improvement for different SNR is presented.

  16. Design of a Low-Power VLSI Macrocell for Nonlinear Adaptive Video Noise Reduction

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    Sergio Saponara

    2004-09-01

    Full Text Available A VLSI macrocell for edge-preserving video noise reduction is proposed in the paper. It is based on a nonlinear rational filter enhanced by a noise estimator for blind and dynamic adaptation of the filtering parameters to the input signal statistics. The VLSI filter features a modular architecture allowing the extension of both mask size and filtering directions. Both spatial and spatiotemporal algorithms are supported. Simulation results with monochrome test videos prove its efficiency for many noise distributions with PSNR improvements up to 3.8 dB with respect to a nonadaptive solution. The VLSI macrocell has been realized in a 0.18 μm CMOS technology using a standard-cells library; it allows for real-time processing of main video formats, up to 30 fps (frames per second 4CIF, with a power consumption in the order of few mW.

  17. Median Filter Noise Reduction of Image and Backpropagation Neural Network Model for Cervical Cancer Classification

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    Wutsqa, D. U.; Marwah, M.

    2017-06-01

    In this paper, we consider spatial operation median filter to reduce the noise in the cervical images yielded by colposcopy tool. The backpropagation neural network (BPNN) model is applied to the colposcopy images to classify cervical cancer. The classification process requires an image extraction by using a gray level co-occurrence matrix (GLCM) method to obtain image features that are used as inputs of BPNN model. The advantage of noise reduction is evaluated by comparing the performances of BPNN models with and without spatial operation median filter. The experimental result shows that the spatial operation median filter can improve the accuracy of the BPNN model for cervical cancer classification.

  18. Single-Channel Noise Reduction using Unified Joint Diagonalization and Optimal Filtering

    DEFF Research Database (Denmark)

    Nørholm, Sidsel Marie; Benesty, Jacob; Jensen, Jesper Rindom

    2014-01-01

    consider two cases, where, respectively, no distortion and distortion are incurred on the desired signal. The former can be achieved when the covariance matrix of the desired signal is rank deficient, which is the case, for example, for voiced speech. In the latter case, the covariance matrix......In this paper, the important problem of single-channel noise reduction is treated from a new perspective. The problem is posed as a filtering problem based on joint diagonalization of the covariance matrices of the desired and noise signals. More specifically, the eigenvectors from the joint...

  19. Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

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    Min Chul Kim

    2011-10-01

    Full Text Available Recently, the range of available Radio Frequency Identification (RFID tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less Mean Squared Error (MSE than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

  20. Improved Kalman filter method for measurement noise reduction in multi sensor RFID systems.

    Science.gov (United States)

    Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon

    2011-01-01

    Recently, the range of available radio frequency identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Performance of Kalman filter is determined by a measurement and system noise covariance which are usually called the R and Q variables in the Kalman filter algorithm. Choosing a correct R and Q variable is one of the most important design factors for better performance of the Kalman filter. For this reason, we proposed an improved Kalman filter to advance an ability of noise reduction of the Kalman filter. The measurement noise covariance was only considered because the system architecture is simple and can be adjusted by the neural network. With this method, more accurate data can be obtained with smart RFID tags. In a simulation the proposed improved Kalman filter has 40.1%, 60.4% and 87.5% less mean squared error (MSE) than the conventional Kalman filter method for a temperature sensor, humidity sensor and oxygen sensor, respectively. The performance of the proposed method was also verified with some experiments.

  1. Effects of directional microphone and adaptive multichannel noise reduction algorithm on cochlear implant performance.

    Science.gov (United States)

    Chung, King; Zeng, Fan-Gang; Acker, Kyle N

    2006-10-01

    Although cochlear implant (CI) users have enjoyed good speech recognition in quiet, they still have difficulties understanding speech in noise. We conducted three experiments to determine whether a directional microphone and an adaptive multichannel noise reduction algorithm could enhance CI performance in noise and whether Speech Transmission Index (STI) can be used to predict CI performance in various acoustic and signal processing conditions. In Experiment I, CI users listened to speech in noise processed by 4 hearing aid settings: omni-directional microphone, omni-directional microphone plus noise reduction, directional microphone, and directional microphone plus noise reduction. The directional microphone significantly improved speech recognition in noise. Both directional microphone and noise reduction algorithm improved overall preference. In Experiment II, normal hearing individuals listened to the recorded speech produced by 4- or 8-channel CI simulations. The 8-channel simulation yielded similar speech recognition results as in Experiment I, whereas the 4-channel simulation produced no significant difference among the 4 settings. In Experiment III, we examined the relationship between STIs and speech recognition. The results suggested that STI could predict actual and simulated CI speech intelligibility with acoustic degradation and the directional microphone, but not the noise reduction algorithm. Implications for intelligibility enhancement are discussed.

  2. Speckle noise reduction for optical coherence tomography based on adaptive 2D dictionary

    Science.gov (United States)

    Lv, Hongli; Fu, Shujun; Zhang, Caiming; Zhai, Lin

    2018-05-01

    As a high-resolution biomedical imaging modality, optical coherence tomography (OCT) is widely used in medical sciences. However, OCT images often suffer from speckle noise, which can mask some important image information, and thus reduce the accuracy of clinical diagnosis. Taking full advantage of nonlocal self-similarity and adaptive 2D-dictionary-based sparse representation, in this work, a speckle noise reduction algorithm is proposed for despeckling OCT images. To reduce speckle noise while preserving local image features, similar nonlocal patches are first extracted from the noisy image and put into groups using a gamma- distribution-based block matching method. An adaptive 2D dictionary is then learned for each patch group. Unlike traditional vector-based sparse coding, we express each image patch by the linear combination of a few matrices. This image-to-matrix method can exploit the local correlation between pixels. Since each image patch might belong to several groups, the despeckled OCT image is finally obtained by aggregating all filtered image patches. The experimental results demonstrate the superior performance of the proposed method over other state-of-the-art despeckling methods, in terms of objective metrics and visual inspection.

  3. Optimal filter design for shielded and unshielded ambient noise reduction in fetal magnetocardiography

    International Nuclear Information System (INIS)

    Comani, S; Mantini, D; Alleva, G; Luzio, S Di; Romani, G L

    2005-01-01

    The greatest impediment to extracting high-quality fetal signals from fetal magnetocardiography (fMCG) is environmental magnetic noise, which may have peak-to-peak intensity comparable to fetal QRS amplitude. Being an unstructured Gaussian signal with large disturbances at specific frequencies, ambient field noise can be reduced with hardware-based approaches and/or with software algorithms that digitally filter magnetocardiographic recordings. At present, no systematic evaluation of filters' performances on shielded and unshielded fMCG is available. We designed high-pass and low-pass Chebychev II-type filters with zero-phase and stable impulse response; the most commonly used band-pass filters were implemented combining high-pass and low-pass filters. The achieved ambient noise reduction in shielded and unshielded recordings was quantified, and the corresponding signal-to-noise ratio (SNR) and signal-to-distortion ratio (SDR) of the retrieved fetal signals was evaluated. The study regarded 66 fMCG datasets at different gestational ages (22-37 weeks). Since the spectral structures of shielded and unshielded magnetic noise were very similar, we concluded that the same filter setting might be applied to both conditions. Band-pass filters (1.0-100 Hz) and (2.0-100 Hz) provided the best combinations of fetal signal detection rates, SNR and SDR; however, the former should be preferred in the case of arrhythmic fetuses, which might present spectral components below 2 Hz

  4. Study on the Noise Reduction of Vehicle Exhaust NOX Spectra Based on Adaptive EEMD Algorithm

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    Kai Zhang

    2017-01-01

    Full Text Available It becomes a key technology to measure the concentration of the vehicle exhaust components with the transmission spectra. But in the conventional methods for noise reduction and baseline correction, such as wavelet transform, derivative, interpolation, polynomial fitting, and so forth, the basic functions of these algorithms, the number of decomposition layers, and the way to reconstruct the signal have to be adjusted according to the characteristics of different components in the transmission spectra. The parameter settings of the algorithms above are not transcendental, so with them, it is difficult to achieve the best noise reduction effect for the vehicle exhaust spectra which are sharp and drastic in the waveform. In this paper, an adaptive ensemble empirical mode decomposition (EEMD denoising model based on a special normalized index optimization is proposed and used in the spectral noise reduction of vehicle exhaust NOX. It is shown with the experimental results that the method can effectively improve the accuracy of the spectral noise reduction and simplify the denoising process and its operation difficulty.

  5. A Background Noise Reduction Technique Using Adaptive Noise Cancellation for Microphone Arrays

    Science.gov (United States)

    Spalt, Taylor B.; Fuller, Christopher R.; Brooks, Thomas F.; Humphreys, William M., Jr.; Brooks, Thomas F.

    2011-01-01

    Background noise in wind tunnel environments poses a challenge to acoustic measurements due to possible low or negative Signal to Noise Ratios (SNRs) present in the testing environment. This paper overviews the application of time domain Adaptive Noise Cancellation (ANC) to microphone array signals with an intended application of background noise reduction in wind tunnels. An experiment was conducted to simulate background noise from a wind tunnel circuit measured by an out-of-flow microphone array in the tunnel test section. A reference microphone was used to acquire a background noise signal which interfered with the desired primary noise source signal at the array. The technique s efficacy was investigated using frequency spectra from the array microphones, array beamforming of the point source region, and subsequent deconvolution using the Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) algorithm. Comparisons were made with the conventional techniques for improving SNR of spectral and Cross-Spectral Matrix subtraction. The method was seen to recover the primary signal level in SNRs as low as -29 dB and outperform the conventional methods. A second processing approach using the center array microphone as the noise reference was investigated for more general applicability of the ANC technique. It outperformed the conventional methods at the -29 dB SNR but yielded less accurate results when coherence over the array dropped. This approach could possibly improve conventional testing methodology but must be investigated further under more realistic testing conditions.

  6. Non-Causal Time-Domain Filters for Single-Channel Noise Reduction

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Benesty, Jacob; Christensen, Mads Græsbøll

    2012-01-01

    suppression and signal distortion by allowing the filters to be non-causal. Non-causal time-domain filters require knowledge of the future, and are therefore not directly implementable. If the observed signal is processed in blocks, however, the non-causal filters are implementable. In this paper, we propose...

  7. Novel noise reduction filter for improving visibility of early computed tomography signs of hyperacute stroke. Evaluation of the filter's performance. Preliminary clinical experience

    International Nuclear Information System (INIS)

    Takahashi, Noriyuki; Ishii, Kiyoshi; Lee, Y.; Tsai, D.Y.

    2007-01-01

    The aim of this study was to evaluate the performance of a novel noise reduction filter for improving the visibility of early computed tomography (CT) signs of hyperacute stroke on nonenhanced CT images. Fourteen patients with a middle cerebral artery occlusion within 4.5 h after onset were evaluated. The signal-to-noise ratio (SNR) of the processed images with the noise reduction filter and that of original images were measured. Two neuroradiologists visually rated all the processed and original images on the visibility of normal and abnormal gray-white matter interfaces. The SNR value of the processed images was approximately eight times as high as that of the original images, and a 87% reduction of noise was achieved using this technique. For the visual assessment, the results showed that the visibility of normal gray-white matter interface and that of the loss of the gray-white matter interface were significantly improved using the proposed method (P<0.05). The noise reduction filter proposed in the present study has the potential to improve the visibility of early CT signs of hyperacute stroke on nonenhanced CT images. (author)

  8. Noise Reduction in Breath Sound Files Using Wavelet Transform Based Filter

    Science.gov (United States)

    Syahputra, M. F.; Situmeang, S. I. G.; Rahmat, R. F.; Budiarto, R.

    2017-04-01

    The development of science and technology in the field of healthcare increasingly provides convenience in diagnosing respiratory system problem. Recording the breath sounds is one example of these developments. Breath sounds are recorded using a digital stethoscope, and then stored in a file with sound format. This breath sounds will be analyzed by health practitioners to diagnose the symptoms of disease or illness. However, the breath sounds is not free from interference signals. Therefore, noise filter or signal interference reduction system is required so that breath sounds component which contains information signal can be clarified. In this study, we designed a filter called a wavelet transform based filter. The filter that is designed in this study is using Daubechies wavelet with four wavelet transform coefficients. Based on the testing of the ten types of breath sounds data, the data is obtained in the largest SNRdB bronchial for 74.3685 decibels.

  9. A multiscale filter for noise reduction of low-dose cone beam projections.

    Science.gov (United States)

    Yao, Weiguang; Farr, Jonathan B

    2015-08-21

    The Poisson or compound Poisson process governs the randomness of photon fluence in cone beam computed tomography (CBCT) imaging systems. The probability density function depends on the mean (noiseless) of the fluence at a certain detector. This dependence indicates the natural requirement of multiscale filters to smooth noise while preserving structures of the imaged object on the low-dose cone beam projection. In this work, we used a Gaussian filter, exp(-x2/2σ(2)(f)) as the multiscale filter to de-noise the low-dose cone beam projections. We analytically obtained the expression of σ(f), which represents the scale of the filter, by minimizing local noise-to-signal ratio. We analytically derived the variance of residual noise from the Poisson or compound Poisson processes after Gaussian filtering. From the derived analytical form of the variance of residual noise, optimal σ(2)(f)) is proved to be proportional to the noiseless fluence and modulated by local structure strength expressed as the linear fitting error of the structure. A strategy was used to obtain the reliable linear fitting error: smoothing the projection along the longitudinal direction to calculate the linear fitting error along the lateral direction and vice versa. The performance of our multiscale filter was examined on low-dose cone beam projections of a Catphan phantom and a head-and-neck patient. After performing the filter on the Catphan phantom projections scanned with pulse time 4 ms, the number of visible line pairs was similar to that scanned with 16 ms, and the contrast-to-noise ratio of the inserts was higher than that scanned with 16 ms about 64% in average. For the simulated head-and-neck patient projections with pulse time 4 ms, the visibility of soft tissue structures in the patient was comparable to that scanned with 20 ms. The image processing took less than 0.5 s per projection with 1024   ×   768 pixels.

  10. Real-time noise reduction for Mössbauer spectroscopy through online implementation of a modified Kalman filter

    Energy Technology Data Exchange (ETDEWEB)

    Abrecht, David G., E-mail: david.abrecht@pnnl.gov [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); Schwantes, Jon M. [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); Kukkadapu, Ravi K. [Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States); McDonald, Benjamin S.; Eiden, Gregory C.; Sweet, Lucas E. [National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd., Richland, WA 99352 (United States)

    2015-02-11

    Spectrum-processing software that incorporates a Gaussian smoothing kernel within the statistics of first-order Kalman filtration has been developed to provide cross-channel spectral noise reduction for increased real-time signal-to-noise ratios for Mössbauer spectroscopy. The filter was optimized for the breadth of the Gaussian using the Mössbauer spectrum of natural iron foil, and comparisons among the peak broadening, signal-to-noise ratios, and shifts in the calculated hyperfine parameters are presented. The results of optimization give a maximum improvement in the signal-to-noise ratio of 51.1% over the unfiltered spectrum at a Gaussian breadth of 27 channels, or 2.5% of the total spectrum width. The full-width half-maximum of the spectrum peaks showed an increase of 19.6% at this optimum point, indicating a relatively weak increase in the peak broadening relative to the signal enhancement, leading to an overall increase in the observable signal. Calculations of the hyperfine parameters showed that no statistically significant deviations were introduced from the application of the filter, confirming the utility of this filter for spectroscopy applications.

  11. Optimal noise reduction in 3D reconstructions of single particles using a volume-normalized filter

    Science.gov (United States)

    Sindelar, Charles V.; Grigorieff, Nikolaus

    2012-01-01

    The high noise level found in single-particle electron cryo-microscopy (cryo-EM) image data presents a special challenge for three-dimensional (3D) reconstruction of the imaged molecules. The spectral signal-to-noise ratio (SSNR) and related Fourier shell correlation (FSC) functions are commonly used to assess and mitigate the noise-generated error in the reconstruction. Calculation of the SSNR and FSC usually includes the noise in the solvent region surrounding the particle and therefore does not accurately reflect the signal in the particle density itself. Here we show that the SSNR in a reconstructed 3D particle map is linearly proportional to the fractional volume occupied by the particle. Using this relationship, we devise a novel filter (the “single-particle Wiener filter”) to minimize the error in a reconstructed particle map, if the particle volume is known. Moreover, we show how to approximate this filter even when the volume of the particle is not known, by optimizing the signal within a representative interior region of the particle. We show that the new filter improves on previously proposed error-reduction schemes, including the conventional Wiener filter as well as figure-of-merit weighting, and quantify the relationship between all of these methods by theoretical analysis as well as numeric evaluation of both simulated and experimentally collected data. The single-particle Wiener filter is applicable across a broad range of existing 3D reconstruction techniques, but is particularly well suited to the Fourier inversion method, leading to an efficient and accurate implementation. PMID:22613568

  12. Improved Kalman Filter Method for Measurement Noise Reduction in Multi Sensor RFID Systems

    OpenAIRE

    Eom, Ki Hwan; Lee, Seung Joon; Kyung, Yeo Sun; Lee, Chang Won; Kim, Min Chul; Jung, Kyung Kwon

    2011-01-01

    Recently, the range of available Radio Frequency Identification (RFID) tags has been widened to include smart RFID tags which can monitor their varying surroundings. One of the most important factors for better performance of smart RFID system is accurate measurement from various sensors. In the multi-sensing environment, some noisy signals are obtained because of the changing surroundings. We propose in this paper an improved Kalman filter method to reduce noise and obtain correct data. Perf...

  13. Use of the Kalman Filter for Aortic Pressure Waveform Noise Reduction.

    Science.gov (United States)

    Lam, Frank; Lu, Hsiang-Wei; Wu, Chung-Che; Aliyazicioglu, Zekeriya; Kang, James S

    2017-01-01

    Clinical applications that require extraction and interpretation of physiological signals or waveforms are susceptible to corruption by noise or artifacts. Real-time hemodynamic monitoring systems are important for clinicians to assess the hemodynamic stability of surgical or intensive care patients by interpreting hemodynamic parameters generated by an analysis of aortic blood pressure (ABP) waveform measurements. Since hemodynamic parameter estimation algorithms often detect events and features from measured ABP waveforms to generate hemodynamic parameters, noise and artifacts integrated into ABP waveforms can severely distort the interpretation of hemodynamic parameters by hemodynamic algorithms. In this article, we propose the use of the Kalman filter and the 4-element Windkessel model with static parameters, arterial compliance C , peripheral resistance R , aortic impedance r , and the inertia of blood L , to represent aortic circulation for generating accurate estimations of ABP waveforms through noise and artifact reduction. Results show the Kalman filter could very effectively eliminate noise and generate a good estimation from the noisy ABP waveform based on the past state history. The power spectrum of the measured ABP waveform and the synthesized ABP waveform shows two similar harmonic frequencies.

  14. Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction

    Directory of Open Access Journals (Sweden)

    Wilson S

    2015-01-01

    Full Text Available Scott Wilson,1,2 Andrea Bowyer,3 Stephen B Harrap4 1Department of Renal Medicine, The Alfred Hospital, 2Baker IDI, Melbourne, 3Department of Anaesthesia, Royal Melbourne Hospital, 4University of Melbourne, Parkville, VIC, Australia Abstract: The clinical characterization of cardiovascular dynamics during hemodialysis (HD has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information. Keywords: continuous monitoring, blood pressure

  15. Characterizing the continuously acquired cardiovascular time series during hemodialysis, using median hybrid filter preprocessing noise reduction.

    Science.gov (United States)

    Wilson, Scott; Bowyer, Andrea; Harrap, Stephen B

    2015-01-01

    The clinical characterization of cardiovascular dynamics during hemodialysis (HD) has important pathophysiological implications in terms of diagnostic, cardiovascular risk assessment, and treatment efficacy perspectives. Currently the diagnosis of significant intradialytic systolic blood pressure (SBP) changes among HD patients is imprecise and opportunistic, reliant upon the presence of hypotensive symptoms in conjunction with coincident but isolated noninvasive brachial cuff blood pressure (NIBP) readings. Considering hemodynamic variables as a time series makes a continuous recording approach more desirable than intermittent measures; however, in the clinical environment, the data signal is susceptible to corruption due to both impulsive and Gaussian-type noise. Signal preprocessing is an attractive solution to this problem. Prospectively collected continuous noninvasive SBP data over the short-break intradialytic period in ten patients was preprocessed using a novel median hybrid filter (MHF) algorithm and compared with 50 time-coincident pairs of intradialytic NIBP measures from routine HD practice. The median hybrid preprocessing technique for continuously acquired cardiovascular data yielded a dynamic regression without significant noise and artifact, suitable for high-level profiling of time-dependent SBP behavior. Signal accuracy is highly comparable with standard NIBP measurement, with the added clinical benefit of dynamic real-time hemodynamic information.

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

    Science.gov (United States)

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

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

  17. A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering.

    Science.gov (United States)

    Shen, Chong; Li, Jie; Zhang, Xiaoming; Shi, Yunbo; Tang, Jun; Cao, Huiliang; Liu, Jun

    2016-05-31

    The different noise components in a dual-mass micro-electromechanical system (MEMS) gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN), electronic-thermal noise (ETN), flicker noise (FN) and Coriolis signal in-phase noise (IPN). The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD) and time-frequency peak filtering (TFPF). There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs) by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods.

  18. A Noise Reduction Method for Dual-Mass Micro-Electromechanical Gyroscopes Based on Sample Entropy Empirical Mode Decomposition and Time-Frequency Peak Filtering

    Directory of Open Access Journals (Sweden)

    Chong Shen

    2016-05-01

    Full Text Available The different noise components in a dual-mass micro-electromechanical system (MEMS gyroscope structure is analyzed in this paper, including mechanical-thermal noise (MTN, electronic-thermal noise (ETN, flicker noise (FN and Coriolis signal in-phase noise (IPN. The structure equivalent electronic model is established, and an improved white Gaussian noise reduction method for dual-mass MEMS gyroscopes is proposed which is based on sample entropy empirical mode decomposition (SEEMD and time-frequency peak filtering (TFPF. There is a contradiction in TFPS, i.e., selecting a short window length may lead to good preservation of signal amplitude but bad random noise reduction, whereas selecting a long window length may lead to serious attenuation of the signal amplitude but effective random noise reduction. In order to achieve a good tradeoff between valid signal amplitude preservation and random noise reduction, SEEMD is adopted to improve TFPF. Firstly, the original signal is decomposed into intrinsic mode functions (IMFs by EMD, and the SE of each IMF is calculated in order to classify the numerous IMFs into three different components; then short window TFPF is employed for low frequency component of IMFs, and long window TFPF is employed for high frequency component of IMFs, and the noise component of IMFs is wiped off directly; at last the final signal is obtained after reconstruction. Rotation experimental and temperature experimental are carried out to verify the proposed SEEMD-TFPF algorithm, the verification and comparison results show that the de-noising performance of SEEMD-TFPF is better than that achievable with the traditional wavelet, Kalman filter and fixed window length TFPF methods.

  19. Background Noise Reduction Using Adaptive Noise Cancellation Determined by the Cross-Correlation

    Science.gov (United States)

    Spalt, Taylor B.; Brooks, Thomas F.; Fuller, Christopher R.

    2012-01-01

    Background noise due to flow in wind tunnels contaminates desired data by decreasing the Signal-to-Noise Ratio. The use of Adaptive Noise Cancellation to remove background noise at measurement microphones is compromised when the reference sensor measures both background and desired noise. The technique proposed modifies the classical processing configuration based on the cross-correlation between the reference and primary microphone. Background noise attenuation is achieved using a cross-correlation sample width that encompasses only the background noise and a matched delay for the adaptive processing. A present limitation of the method is that a minimum time delay between the background noise and desired signal must exist in order for the correlated parts of the desired signal to be separated from the background noise in the crosscorrelation. A simulation yields primary signal recovery which can be predicted from the coherence of the background noise between the channels. Results are compared with two existing methods.

  20. Speckle noise reduction technique for Lidar echo signal based on self-adaptive pulse-matching independent component analysis

    Science.gov (United States)

    Xu, Fan; Wang, Jiaxing; Zhu, Daiyin; Tu, Qi

    2018-04-01

    Speckle noise has always been a particularly tricky problem in improving the ranging capability and accuracy of Lidar system especially in harsh environment. Currently, effective speckle de-noising techniques are extremely scarce and should be further developed. In this study, a speckle noise reduction technique has been proposed based on independent component analysis (ICA). Since normally few changes happen in the shape of laser pulse itself, the authors employed the laser source as a reference pulse and executed the ICA decomposition to find the optimal matching position. In order to achieve the self-adaptability of algorithm, local Mean Square Error (MSE) has been defined as an appropriate criterion for investigating the iteration results. The obtained experimental results demonstrated that the self-adaptive pulse-matching ICA (PM-ICA) method could effectively decrease the speckle noise and recover the useful Lidar echo signal component with high quality. Especially, the proposed method achieves 4 dB more improvement of signal-to-noise ratio (SNR) than a traditional homomorphic wavelet method.

  1. Communal Sensor Network for Adaptive Noise Reduction in Aircraft Engine Nacelles

    Science.gov (United States)

    Jones, Kennie H.; Nark, Douglas M.; Jones, Michael G.

    2011-01-01

    Emergent behavior, a subject of much research in biology, sociology, and economics, is a foundational element of Complex Systems Science and is apropos in the design of sensor network systems. To demonstrate engineering for emergent behavior, a novel approach in the design of a sensor/actuator network is presented maintaining optimal noise attenuation as an adaptation to changing acoustic conditions. Rather than use the conventional approach where sensors are managed by a central controller, this new paradigm uses a biomimetic model where sensor/actuators cooperate as a community of autonomous organisms, sharing with neighbors to control impedance based on local information. From the combination of all individual actions, an optimal attenuation emerges for the global system.

  2. Active3 noise reduction

    International Nuclear Information System (INIS)

    Holzfuss, J.

    1996-01-01

    Noise reduction is a problem being encountered in a variety of applications, such as environmental noise cancellation, signal recovery and separation. Passive noise reduction is done with the help of absorbers. Active noise reduction includes the transmission of phase inverted signals for the cancellation. This paper is about a threefold active approach to noise reduction. It includes the separation of a combined source, which consists of both a noise and a signal part. With the help of interaction with the source by scanning it and recording its response, modeling as a nonlinear dynamical system is achieved. The analysis includes phase space analysis and global radial basis functions as tools for the prediction used in a subsequent cancellation procedure. Examples are given which include noise reduction of speech. copyright 1996 American Institute of Physics

  3. Noise reduction and functional maps image quality improvement in dynamic CT perfusion using a new k-means clustering guided bilateral filter (KMGB).

    Science.gov (United States)

    Pisana, Francesco; Henzler, Thomas; Schönberg, Stefan; Klotz, Ernst; Schmidt, Bernhard; Kachelrieß, Marc

    2017-07-01

    Dynamic CT perfusion (CTP) consists in repeated acquisitions of the same volume in different time steps, slightly before, during and slightly afterwards the injection of contrast media. Important functional information can be derived for each voxel, which reflect the local hemodynamic properties and hence the metabolism of the tissue. Different approaches are being investigated to exploit data redundancy and prior knowledge for noise reduction of such datasets, ranging from iterative reconstruction schemes to high dimensional filters. We propose a new spatial bilateral filter which makes use of the k-means clustering algorithm and of an optimal calculated guiding image. We named the proposed filter as k-means clustering guided bilateral filter (KMGB). In this study, the KMGB filter is compared with the partial temporal non-local means filter (PATEN), with the time-intensity profile similarity (TIPS) filter, and with a new version derived from it, by introducing the guiding image (GB-TIPS). All the filters were tested on a digital in-house developed brain CTP phantom, were noise was added to simulate 80 kV and 200 mAs (default scanning parameters), 100 mAs and 30 mAs. Moreover, the filters performances were tested on 7 noisy clinical datasets with different pathologies in different body regions. The original contribution of our work is two-fold: first we propose an efficient algorithm to calculate a guiding image to improve the results of the TIPS filter, secondly we propose the introduction of the k-means clustering step and demonstrate how this can potentially replace the TIPS part of the filter obtaining better results at lower computational efforts. As expected, in the GB-TIPS, the introduction of the guiding image limits the over-smoothing of the TIPS filter, improving spatial resolution by more than 50%. Furthermore, replacing the time-intensity profile similarity calculation with a fuzzy k-means clustering strategy (KMGB) allows to control the edge preserving

  4. Adaptive digital filters

    CERN Document Server

    Kovačević, Branko; Milosavljević, Milan

    2013-01-01

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

  5. HARLIE 3-D Aerosol Backscatter and Wind Profile Measurements During Recent Field Experiments: Background Noise Reduction with a Fabry-Perot Etalon Filter in the HARLIE System

    Science.gov (United States)

    Lee, Sangwoo; Miller, David O.; Schwemmer, Geary; Wilkerson, Thomas D.; Andrus, Ionio; Egbert, Cameron; Anderson, Mark; Starr, David OC. (Technical Monitor)

    2002-01-01

    Background noise reduction of War signals is one of the most important factors in achieving better signal to noise ratio and precise atmospheric data from Mar measurements. Fahey Perot etalons have been used in several lidar systems as narrow band pass filters in the reduction of scattered sunlight. An slalom with spectral bandwidth, (Delta)v=0.23/cm, free spectral range, FSR=6.7/cm, and diameter, d=24mm was installed in a fiber coupled box which included a 500 pm bandwidth interference Filter. The slalom box couples the telescope and detector with 200 pm core fibers and 21 mm focal length collimators. The angular magnification is M=48. The etalon box was inserted into the Holographic Airborne Rotating Lidar Instrument Experiment (HARLIE) system and tested during the HARGLO-2 intercomparison campaign conducted in November 2001 at Wallops Island, Virginia. This paper presents the preliminary test results of the slalom and a complete analysis will be presented at the conference.

  6. A conceptual framework for noise reduction

    CERN Document Server

    Benesty, Jacob

    2015-01-01

    Though noise reduction and speech enhancement problems have been studied for at least five decades, advances in our understanding and the development of reliable algorithms are more important than ever, as they support the design of tailored solutions for clearly defined applications. In this work, the authors propose a conceptual framework that can be applied to the many different aspects of noise reduction, offering a uniform approach to monaural and binaural noise reduction problems, in the time domain and in the frequency domain, and involving a single or multiple microphones. Moreover, the derivation of optimal filters is simplified, as are the performance measures used for their evaluation.

  7. A method of noise reduction in heterodyne interferometric vibration metrology by combining auto-correlation analysis and spectral filtering

    Science.gov (United States)

    Hao, Hongliang; Xiao, Wen; Chen, Zonghui; Ma, Lan; Pan, Feng

    2018-01-01

    Heterodyne interferometric vibration metrology is a useful technique for dynamic displacement and velocity measurement as it can provide a synchronous full-field output signal. With the advent of cost effective, high-speed real-time signal processing systems and software, processing of the complex signals encountered in interferometry has become more feasible. However, due to the coherent nature of the laser sources, the sequence of heterodyne interferogram are corrupted by a mixture of coherent speckle and incoherent additive noise, which can severely degrade the accuracy of the demodulated signal and the optical display. In this paper, a new heterodyne interferometric demodulation method by combining auto-correlation analysis and spectral filtering is described leading to an expression for the dynamic displacement and velocity of the object under test that is significantly more accurate in both the amplitude and frequency of the vibrating waveform. We present a mathematical model of the signals obtained from interferograms that contain both vibration information of the measured objects and the noise. A simulation of the signal demodulation process is presented and used to investigate the noise from the system and external factors. The experimental results show excellent agreement with measurements from a commercial Laser Doppler Velocimetry (LDV).

  8. Adaptive projective filters

    International Nuclear Information System (INIS)

    Dikusar, N.D.

    1993-01-01

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

  9. The effect of multimicrophone noise reduction systems on sound source localization by users of binaural hearing aids.

    Science.gov (United States)

    Van den Bogaert, Tim; Doclo, Simon; Wouters, Jan; Moonen, Marc

    2008-07-01

    This paper evaluates the influence of three multimicrophone noise reduction algorithms on the ability to localize sound sources. Two recently developed noise reduction techniques for binaural hearing aids were evaluated, namely, the binaural multichannel Wiener filter (MWF) and the binaural multichannel Wiener filter with partial noise estimate (MWF-N), together with a dual-monaural adaptive directional microphone (ADM), which is a widely used noise reduction approach in commercial hearing aids. The influence of the different algorithms on perceived sound source localization and their noise reduction performance was evaluated. It is shown that noise reduction algorithms can have a large influence on localization and that (a) the ADM only preserves localization in the forward direction over azimuths where limited or no noise reduction is obtained; (b) the MWF preserves localization of the target speech component but may distort localization of the noise component. The latter is dependent on signal-to-noise ratio and masking effects; (c) the MWF-N enables correct localization of both the speech and the noise components; (d) the statistical Wiener filter approach introduces a better combination of sound source localization and noise reduction performance than the ADM approach.

  10. Noise reduction in the beam current monitor

    International Nuclear Information System (INIS)

    Arai, Shigeaki.

    1982-02-01

    A simple noise reduction system using a pulse transformer and a pair of L C low pass filters has been introduced to the beam current monitor of a current transformer type at the INS electron linac. With this system, the pick-up noise has been reduced to be 1% of the noise without noise reduction. Signal deformation caused by this system is relatively small and the beam current pulse down to 20 mA is successfully monitored in the actual accelerator operation. (author)

  11. Adaptive filtering and change detection

    CERN Document Server

    Gustafsson, Fredrik

    2003-01-01

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

  12. Adaptive filtering prediction and control

    CERN Document Server

    Goodwin, Graham C

    2009-01-01

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

  13. Aircraft Noise Reduction Subproject Overview

    Science.gov (United States)

    Fernandez, Hamilton; Nark, Douglas M.; Van Zante, Dale E.

    2016-01-01

    The material presents highlights of propulsion and airframe noise research being completed for the Advanced Air Transport Technology Project. The basis of noise reduction plans along with representative work for the airframe, propulsion, and propulsion-airframe integration is discussed for the Aircraft Noise reduction Subproject.

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

    Science.gov (United States)

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

    2014-05-01

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

  15. Assessment of noise reduction potential and image quality improvement of a new generation adaptive statistical iterative reconstruction (ASIR-V) in chest CT.

    Science.gov (United States)

    Tang, Hui; Yu, Nan; Jia, Yongjun; Yu, Yong; Duan, Haifeng; Han, Dong; Ma, Guangming; Ren, Chenglong; He, Taiping

    2018-01-01

    To evaluate the image quality improvement and noise reduction in routine dose, non-enhanced chest CT imaging by using a new generation adaptive statistical iterative reconstruction (ASIR-V) in comparison with ASIR algorithm. 30 patients who underwent routine dose, non-enhanced chest CT using GE Discovery CT750HU (GE Healthcare, Waukesha, WI) were included. The scan parameters included tube voltage of 120 kVp, automatic tube current modulation to obtain a noise index of 14HU, rotation speed of 0.6 s, pitch of 1.375:1 and slice thickness of 5 mm. After scanning, all scans were reconstructed with the recommended level of 40%ASIR for comparison purpose and different percentages of ASIR-V from 10% to 100% in a 10% increment. The CT attenuation values and SD of the subcutaneous fat, back muscle and descending aorta were measured at the level of tracheal carina of all reconstructed images. The signal-to-noise ratio (SNR) was calculated with SD representing image noise. The subjective image quality was independently evaluated by two experienced radiologists. For all ASIR-V images, the objective image noise (SD) of fat, muscle and aorta decreased and SNR increased along with increasing ASIR-V percentage. The SD of 30% ASIR-V to 100% ASIR-V was significantly lower than that of 40% ASIR (p ASIR-V reconstructions had good diagnostic acceptability. However, the 50% ASIR-V to 70% ASIR-V series showed significantly superior visibility of small structures when compared with the 40% ASIR and ASIR-V of other percentages (p ASIR-V was the best series of all ASIR-V images, with a highest subjective image quality. The image sharpness was significantly decreased in images reconstructed by 80% ASIR-V and higher. In routine dose, non-enhanced chest CT, ASIR-V shows greater potential in reducing image noise and artefacts and maintaining image sharpness when compared to the recommended level of 40%ASIR algorithm. Combining both the objective and subjective evaluation of images, non

  16. Implicit LES using adaptive filtering

    Science.gov (United States)

    Sun, Guangrui; Domaradzki, Julian A.

    2018-04-01

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

  17. Observation of phase noise reduction in photonically synthesized sub-THz signals using a passively mode-locked laser diode and highly selective optical filtering

    DEFF Research Database (Denmark)

    Criado, A. R.; Acedo, P.; Carpintero, G.

    2012-01-01

    A Continuous Wave (CW) sub-THz photonic synthesis setup based on a single Passively Mode-Locked Laser Diode (PMLLD) acting as a monolithic Optical Frequency Comb Generator (OFCG) and highly selective optical filtering has been implemented to evaluate the phase noise performance of the generated sub...

  18. Noise reduction by wavelet thresholding

    National Research Council Canada - National Science Library

    Jansen, Maarten

    2001-01-01

    .... I rather present new material and own insights in the que stions involved with wavelet based noise reduction . On the other hand , the presented material does cover a whole range of methodologies, and in that sense, the book may serve as an introduction into the domain of wavelet smoothing. Throughout the text, three main properties show up ever again: spar...

  19. Periodic additive noises reduction in 3D images used in building of voxel phantoms through an efficient implementation of the 3D FFT: zipper artifacts filtering

    International Nuclear Information System (INIS)

    Oliveira, Alex C.H. de; Lima, Fernando R.A.; Vieira, Jose W.; Leal Neto, Viriato

    2009-01-01

    The anthropomorphic models used in computational dosimetry are predominantly build from scanning CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) image stacks obtained of patients or volunteers. The building of these stacks (usually called of voxel phantoms or tomography phantoms) requires computer processing to be used in an exposure computational model. Noises present in these stacks can be confused with significant structures. In a 3D image with periodic additive noise in the frequency domain, the noise is fully added to its central slice. The discrete Fourier transform is the fundamental mathematical tool that allows the switch of the spatial domain for the frequency domain, and vice versa. The FFT (fast Fourier transform) algorithm is an ideal computational tool for this switch in domain with efficiency. This paper presents a new methodology for implementation in managed C++ language (Microsoft Visual Studio R .NET) of the fast Fourier transform of 3D digital images (FFT3D) using, essentially, the trigonometric recombination. The reduction of periodic additive noise consists in filtering only the central slice of 3D image in the frequency domain and transforms it back into the spatial domain through the inverse FFT3D. An example of application of this method it is the zipper artifacts filtering in images of MRI. These processes were implemented in the software DIP (Digital Image Processing). (author)

  20. Adaptive Filtering Algorithms and Practical Implementation

    CERN Document Server

    Diniz, Paulo S R

    2013-01-01

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

  1. Low Delay Noise Reduction and Dereverberation for Hearing Aids

    Directory of Open Access Journals (Sweden)

    Heinrich W. Löllmann

    2009-01-01

    Full Text Available A new system for single-channel speech enhancement is proposed which achieves a joint suppression of late reverberant speech and background noise with a low signal delay and low computational complexity. It is based on a generalized spectral subtraction rule which depends on the variances of the late reverberant speech and background noise. The calculation of the spectral variances of the late reverberant speech requires an estimate of the reverberation time (RT which is accomplished by a maximum likelihood (ML approach. The enhancement with this blind RT estimation achieves almost the same speech quality as by using the actual RT. In comparison to commonly used post-filters in hearing aids which only perform a noise reduction, a significantly better objective and subjective speech quality is achieved. The proposed system performs time-domain filtering with coefficients adapted in the non-uniform (Bark-scaled frequency-domain. This allows to achieve a high speech quality with low signal delay which is important for speech enhancement in hearing aids or related applications such as hands-free communication systems.

  2. Adaptable Iterative and Recursive Kalman Filter Schemes

    Science.gov (United States)

    Zanetti, Renato

    2014-01-01

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

  3. Noise Reduction in the Time Domain using Joint Diagonalization

    DEFF Research Database (Denmark)

    Nørholm, Sidsel Marie; Benesty, Jacob; Jensen, Jesper Rindom

    2014-01-01

    , an estimate of the desired signal is found by subtraction of the noise estimate from the observed signal. The filter can be designed to obtain a desired trade-off between noise reduction and signal distortion, depending on the number of eigenvectors included in the filter design. This is explored through...... simulations using a speech signal corrupted by car noise, and the results confirm that the output signal-to-noise ratio and speech distortion index both increase when more eigenvectors are included in the filter design....

  4. Noise Reduction with Microphone Arrays for Speaker Identification

    Energy Technology Data Exchange (ETDEWEB)

    Cohen, Z

    2011-12-22

    Reducing acoustic noise in audio recordings is an ongoing problem that plagues many applications. This noise is hard to reduce because of interfering sources and non-stationary behavior of the overall background noise. Many single channel noise reduction algorithms exist but are limited in that the more the noise is reduced; the more the signal of interest is distorted due to the fact that the signal and noise overlap in frequency. Specifically acoustic background noise causes problems in the area of speaker identification. Recording a speaker in the presence of acoustic noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the environment where the speech sample is taken, noise reduction filtering algorithms need to be developed to clean the recorded speech of background noise. Because single channel noise reduction algorithms would distort the speech signal, the overall challenge of this project was to see if spatial information provided by microphone arrays could be exploited to aid in speaker identification. The goals are: (1) Test the feasibility of using microphone arrays to reduce background noise in speech recordings; (2) Characterize and compare different multichannel noise reduction algorithms; (3) Provide recommendations for using these multichannel algorithms; and (4) Ultimately answer the question - Can the use of microphone arrays aid in speaker identification?

  5. Adaptivni digitalni filtri / Adaptive digital filters

    Directory of Open Access Journals (Sweden)

    Dragan Petković

    2002-01-01

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

  6. Adaptive filtering primer with Matlab

    CERN Document Server

    Poularikas, Alexander D

    2006-01-01

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

  7. In vivo evaluation of mastication noise reduction for dual channel implantable microphone.

    Science.gov (United States)

    Woo, SeongTak; Jung, EuiSung; Lim, HyungGyu; Lee, Jang Woo; Seong, Ki Woong; Won, Chul Ho; Kim, Myoung Nam; Cho, Jin Ho; Lee, Jyung Hyun

    2014-01-01

    Input for fully implantable hearing devices (FIHDs) is provided by an implantable microphone under the skin of the temporal bone. However, the implanted microphone can be affected when the FIHDs user chews. In this paper, a dual implantable microphone was designed that can filter out the noise from mastication. For the in vivo experiment, a fabricated microphone was implanted in a rabbit. Pure-tone sounds of 1 kHz through a standard speaker were applied to the rabbit, which was given food simultaneously. To evaluate noise reduction, the measured signals were processed using a MATLAB program based adaptive filter. To verify the proposed method, the correlation coefficients and signal to-noise ratio before and after signal processing were calculated. By comparing the results, signal-to-noise ratio and correlation coefficients are enhanced by 6.07dB and 0.529 respectively.

  8. Partial update least-square adaptive filtering

    CERN Document Server

    Xie, Bei

    2014-01-01

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

  9. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin

    2011-01-01

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

  10. A Rapid Introduction to Adaptive Filtering

    CERN Document Server

    Vega, Leonardo Rey

    2013-01-01

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

  11. Advanced digital signal processing and noise reduction

    CERN Document Server

    Vaseghi, Saeed V

    2008-01-01

    Digital signal processing plays a central role in the development of modern communication and information processing systems. The theory and application of signal processing is concerned with the identification, modelling and utilisation of patterns and structures in a signal process. The observation signals are often distorted, incomplete and noisy and therefore noise reduction, the removal of channel distortion, and replacement of lost samples are important parts of a signal processing system. The fourth edition of Advanced Digital Signal Processing and Noise Reduction updates an

  12. Spreadsheet tool for estimating noise reduction costs

    International Nuclear Information System (INIS)

    Frank, L.; Senden, V.; Leszczynski, Y.

    2009-01-01

    The Northeast Capital Industrial Association (NCIA) represents industry in Alberta's industrial heartland. The organization is in the process of developing a regional noise management plan (RNMP) for their member companies. The RNMP includes the development of a noise reduction cost spreadsheet tool to conduct reviews of practical noise control treatments available for individual plant equipment, inclusive of ranges of noise attenuation achievable, which produces a budgetary prediction of the installed cost of practical noise control treatments. This paper discussed the noise reduction cost spreadsheet tool, with particular reference to noise control best practices approaches and spreadsheet tool development such as prerequisite, assembling data required, approach, and unit pricing database. Use and optimization of the noise reduction cost spreadsheet tool was also discussed. It was concluded that the noise reduction cost spreadsheet tool is an easy interactive tool to estimate implementation costs related to different strategies and options of noise control mitigating measures and was very helpful in gaining insight for noise control planning purposes. 2 tabs.

  13. Adaptive Filtering Using Recurrent Neural Networks

    Science.gov (United States)

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

    2005-01-01

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

  14. Convergence Performance of Adaptive Algorithms of L-Filters

    Directory of Open Access Journals (Sweden)

    Robert Hudec

    2003-01-01

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

  15. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

    Bibi, Adel Aamer

    2016-09-16

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

  16. Adaptive Filtering Queueing for Improving Fairness

    Directory of Open Access Journals (Sweden)

    Jui-Pin Yang

    2015-06-01

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

  17. Helicopter Flight Procedures for Community Noise Reduction

    Science.gov (United States)

    Greenwood, Eric

    2017-01-01

    A computationally efficient, semiempirical noise model suitable for maneuvering flight noise prediction is used to evaluate the community noise impact of practical variations on several helicopter flight procedures typical of normal operations. Turns, "quick-stops," approaches, climbs, and combinations of these maneuvers are assessed. Relatively small variations in flight procedures are shown to cause significant changes to Sound Exposure Levels over a wide area. Guidelines are developed for helicopter pilots intended to provide effective strategies for reducing the negative effects of helicopter noise on the community. Finally, direct optimization of flight trajectories is conducted to identify low noise optimal flight procedures and quantify the magnitude of community noise reductions that can be obtained through tailored helicopter flight procedures. Physically realizable optimal turns and approaches are identified that achieve global noise reductions of as much as 10 dBA Sound Exposure Level.

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

    Science.gov (United States)

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

    2015-03-01

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

  19. Adaptive iterative dose reduction algorithm in CT: Effect on image quality compared with filtered back projection in body phantoms of different sizes

    International Nuclear Information System (INIS)

    Kim, Milim; Lee, Jeong Min; Son, Hyo Shin; Han, Joon Koo; Choi, Byung Ihn; Yoon, Jeong Hee; Choi, Jin Woo

    2014-01-01

    To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.

  20. Adaptive iterative dose reduction algorithm in CT: Effect on image quality compared with filtered back projection in body phantoms of different sizes

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Milim; Lee, Jeong Min; Son, Hyo Shin; Han, Joon Koo; Choi, Byung Ihn [College of Medicine, Seoul National University, Seoul (Korea, Republic of); Yoon, Jeong Hee; Choi, Jin Woo [Dept. of Radiology, Seoul National University Hospital, Seoul (Korea, Republic of)

    2014-04-15

    To evaluate the impact of the adaptive iterative dose reduction (AIDR) three-dimensional (3D) algorithm in CT on noise reduction and the image quality compared to the filtered back projection (FBP) algorithm and to compare the effectiveness of AIDR 3D on noise reduction according to the body habitus using phantoms with different sizes. Three different-sized phantoms with diameters of 24 cm, 30 cm, and 40 cm were built up using the American College of Radiology CT accreditation phantom and layers of pork belly fat. Each phantom was scanned eight times using different mAs. Images were reconstructed using the FBP and three different strengths of the AIDR 3D. The image noise, the contrast-to-noise ratio (CNR) and the signal-to-noise ratio (SNR) of the phantom were assessed. Two radiologists assessed the image quality of the 4 image sets in consensus. The effectiveness of AIDR 3D on noise reduction compared with FBP were also compared according to the phantom sizes. Adaptive iterative dose reduction 3D significantly reduced the image noise compared with FBP and enhanced the SNR and CNR (p < 0.05) with improved image quality (p < 0.05). When a stronger reconstruction algorithm was used, greater increase of SNR and CNR as well as noise reduction was achieved (p < 0.05). The noise reduction effect of AIDR 3D was significantly greater in the 40-cm phantom than in the 24-cm or 30-cm phantoms (p < 0.05). The AIDR 3D algorithm is effective to reduce the image noise as well as to improve the image-quality parameters compared by FBP algorithm, and its effectiveness may increase as the phantom size increases.

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

    Institute of Scientific and Technical Information of China (English)

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

    2007-01-01

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

  2. Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function

    Directory of Open Access Journals (Sweden)

    Christofer Toumazou

    2013-07-01

    Full Text Available A novel noise filtering algorithm based on averaging Intrinsic Mode Function (aIMF, which is a derivation of Empirical Mode Decomposition (EMD, is proposed to remove white-Gaussian noise of foreign currency exchange rates that are nonlinear nonstationary times series signals. Noise patterns with different amplitudes and frequencies were randomly mixed into the five exchange rates. A number of filters, namely; Extended Kalman Filter (EKF, Wavelet Transform (WT, Particle Filter (PF and the averaging Intrinsic Mode Function (aIMF algorithm were used to compare filtering and smoothing performance. The aIMF algorithm demonstrated high noise reduction among the performance of these filters.

  3. Wing Leading Edge Concepts for Noise Reduction

    Science.gov (United States)

    Shmilovich, Arvin; Yadlin, Yoram; Pitera, David M.

    2010-01-01

    This study focuses on the development of wing leading edge concepts for noise reduction during high-lift operations, without compromising landing stall speeds, stall characteristics or cruise performance. High-lift geometries, which can be obtained by conventional mechanical systems or morphing structures have been considered. A systematic aerodynamic analysis procedure was used to arrive at several promising configurations. The aerodynamic design of new wing leading edge shapes is obtained from a robust Computational Fluid Dynamics procedure. Acoustic benefits are qualitatively established through the evaluation of the computed flow fields.

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

    Science.gov (United States)

    Samuyelu, Bommu; Rajesh Kumar, Pullakura

    2017-12-01

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

  5. Applying Maxi-adjustment to Adaptive Information Filtering Agents

    OpenAIRE

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

    2000-01-01

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

  6. Analysis of Beamformer Directed Single-Channel Noise Reduction System for Hearing Aid Applications

    DEFF Research Database (Denmark)

    Jensen, Jesper; Pedersen, Michael Syskind

    2015-01-01

    We study multi-microphone noise reduction systems consisting of a beamformer and a single-channel (SC) noise reduction stage. In particular, we present and analyse a maximum likelihood (ML) method for jointly estimating the target and noise power spectral densities (psd's) entering the SC filter....... We show that the estimators are minimum variance and unbiased, and provide closed-form expressions for their mean-square error (MSE). Furthermore, we show that the MSE of the noise psd estimator is particularly simple: it is independent of target signal characteristics, frequency, and microphone...

  7. Spherical Tensor Calculus for Local Adaptive Filtering

    Science.gov (United States)

    Reisert, Marco; Burkhardt, Hans

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

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

    Institute of Scientific and Technical Information of China (English)

    杨勇; 缪玲娟

    2003-01-01

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

  9. Simulation for noise cancellation using LMS adaptive filter

    Science.gov (United States)

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

    2017-06-01

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

  10. Noise reduction in optically controlled quantum memory

    Science.gov (United States)

    Ma, Lijun; Slattery, Oliver; Tang, Xiao

    2018-05-01

    Quantum memory is an essential tool for quantum communications systems and quantum computers. An important category of quantum memory, called optically controlled quantum memory, uses a strong classical beam to control the storage and re-emission of a single-photon signal through an atomic ensemble. In this type of memory, the residual light from the strong classical control beam can cause severe noise and degrade the system performance significantly. Efficiently suppressing this noise is a requirement for the successful implementation of optically controlled quantum memories. In this paper, we briefly introduce the latest and most common approaches to quantum memory and review the various noise-reduction techniques used in implementing them.

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

    NARCIS (Netherlands)

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

    2009-01-01

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

  12. Scheme of adaptive polarization filtering based on Kalman model

    Institute of Scientific and Technical Information of China (English)

    Song Lizhong; Qi Haiming; Qiao Xiaolin; Meng Xiande

    2006-01-01

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

  13. Digital Active Noise Reduction Ear Plugs

    National Research Council Canada - National Science Library

    Harley, Thomas

    1994-01-01

    .... In contrast to available ANR headsets that implement fixed analog filters, the prototype defines a digital filter that is optimally defined for the user's current acoustical environment. An above-the-ear (ATE) and an in-the-ear (ITE...

  14. User interaction with user-adaptive information filters

    NARCIS (Netherlands)

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

    2007-01-01

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

  15. User Interaction with User-Adaptive Information Filters

    NARCIS (Netherlands)

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

    2007-01-01

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

  16. Target Response Adaptation for Correlation Filter Tracking

    KAUST Repository

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

    2016-01-01

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

  17. Applications of adaptive filters in active noise control

    Science.gov (United States)

    Darlington, Paul

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

  18. Noise Reduction Potential of Cellular Metals

    Directory of Open Access Journals (Sweden)

    Björn Hinze

    2012-06-01

    Full Text Available Rising numbers of flights and aircrafts cause increasing aircraft noise, resulting in the development of various approaches to change this trend. One approach is the application of metallic liners in the hot gas path of aero-engines. At temperatures of up to 600 °C only metallic or ceramic structures can be used. Due to fatigue loading and the notch effect of the pores, mechanical properties of porous metals are superior to the ones of ceramic structures. Consequently, cellular metals like metallic foams, sintered metals, or sintered metal felts are most promising materials. However, acoustic absorption depends highly on pore morphology and porosity. Therefore, both parameters must be characterized precisely to analyze the correlation between morphology and noise reduction performance. The objective of this study is to analyze the relationship between pore morphology and acoustic absorption performance. The absorber materials are characterized using image processing based on two dimensional microscopy images. The sound absorption properties are measured using an impedance tube. Finally, the correlation of acoustic behavior, pore morphology, and porosity is outlined.

  19. Jet noise reduction via dispersed phase injection

    Science.gov (United States)

    Greska, Brent; Krothapalli, Anjaneyulu; Arakeri, Vijay

    2001-11-01

    A recently developed hot jet aeroacoustics facility at FMRL,FAMU-FSU College of Engineering has been used to study the far field noise characteristics of hot supersonic jets as influenced by the injection of a dispersed phase with low mass loading.The measured SPL from a fully expanded Mach 1.36 hot jet shows a peak value of about 139 dB at 40 deg from the jet axis.By injecting atomized water,the SPL are reduced in the angular region of about 30 deg to 50 deg with the maximum reduction being about 2 dB at 40 deg.However,with the use of non atomized aqueous polymer solution as a dispersed phase the noise levels are reduced over all angular positions by at least 1 dB with the maximum reduction being about 3 dB at 40 deg.The injection of a dispersed phase readily kills the screech; the initial results show promise and optimization studies are underway to find methods of further noise reduction.

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

    Science.gov (United States)

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

    1983-01-01

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

  1. Restoration of nuclear medicine images using adaptive Wiener filters

    International Nuclear Information System (INIS)

    Meinel, G.

    1989-01-01

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

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

    Science.gov (United States)

    Bagwell, P J; Chappell, P H

    1995-03-01

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

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

  4. Analysis of Individual Preferences for Tuning Noise-Reduction Algorithms

    NARCIS (Netherlands)

    Houben, Rolph; Dijkstra, Tjeerd M. H.; Dreschler, Wouter A.

    2012-01-01

    There is little research on user preference for different settings of noise reduction, especially for individual users. We therefore measured individual preferences for pairs of audio streams differing in the trade-off between noise reduction and speech distortion. A logistic probability model was

  5. 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.

  6. A Family of Maximum SNR Filters for Noise Reduction

    DEFF Research Database (Denmark)

    Huang, Gongping; Benesty, Jacob; Long, Tao

    2014-01-01

    significantly increase the SNR but at the expense of tremendous speech distortion. As a consequence, the speech quality improvement, measured by the perceptual evaluation of speech quality (PESQ) algorithm, is marginal if any, regardless of the number of microphones used. In the STFT domain, the maximum SNR...

  7. Generalized multi-dimensional adaptive filtering for conventional and spiral single-slice, multi-slice, and cone-beam CT

    International Nuclear Information System (INIS)

    Kachelriess, Marc; Watzke, Oliver; Kalender, Willi A.

    2001-01-01

    In modern computed tomography (CT) there is a strong desire to reduce patient dose and/or to improve image quality by increasing spatial resolution and decreasing image noise. These are conflicting demands since increasing resolution at a constant noise level or decreasing noise at a constant resolution level implies a higher demand on x-ray power and an increase of patient dose. X-ray tube power is limited due to technical reasons. We therefore developed a generalized multi-dimensional adaptive filtering approach that applies nonlinear filters in up to three dimensions in the raw data domain. This new method differs from approaches in the literature since our nonlinear filters are applied not only in the detector row direction but also in the view and in the z-direction. This true three-dimensional filtering improves the quantum statistics of a measured projection value proportional to the third power of the filter size. Resolution tradeoffs are shared among these three dimensions and thus are considerably smaller as compared to one-dimensional smoothing approaches. Patient data of spiral and sequential single- and multi-slice CT scans as well as simulated spiral cone-beam data were processed to evaluate these new approaches. Image quality was assessed by evaluation of difference images, by measuring the image noise and the noise reduction, and by calculating the image resolution using point spread functions. The use of generalized adaptive filters helps to reduce image noise or, alternatively, patient dose. Image noise structures, typically along the direction of the highest attenuation, are effectively reduced. Noise reduction values of typically 30%-60% can be achieved in noncylindrical body regions like the shoulder. The loss in image resolution remains below 5% for all cases. In addition, the new method has a great potential to reduce metal artifacts, e.g., in the hip region

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

    Science.gov (United States)

    Elad, M; Feuer, A

    1999-01-01

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

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

    Science.gov (United States)

    Dusenberry, Lisa; Hutter, Liz; Robinson, Joy

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    M. Geravanchizadeh

    2014-12-01

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

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

    Science.gov (United States)

    Galante, Joseph M.; Sanner, Robert M.

    2012-01-01

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

  12. Low-power adaptive filter based on RNS components

    DEFF Research Database (Denmark)

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

    2007-01-01

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

  13. Adaptive robust Kalman filtering for precise point positioning

    International Nuclear Information System (INIS)

    Guo, Fei; Zhang, Xiaohong

    2014-01-01

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

  14. Adaptive Filtering for Non-Gaussian Processes

    DEFF Research Database (Denmark)

    Kidmose, Preben

    2000-01-01

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

  15. A generalized adaptive mathematical morphological filter for LIDAR data

    Science.gov (United States)

    Cui, Zheng

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

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

    International Nuclear Information System (INIS)

    Marshall, D.A.G.

    1980-01-01

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

  17. Detection threshold for sound distortion resulting from noise reduction in normal-hearing and hearing-impaired listeners.

    Science.gov (United States)

    Brons, Inge; Dreschler, Wouter A; Houben, Rolph

    2014-09-01

    Hearing-aid noise reduction should reduce background noise, but not disturb the target speech. This objective is difficult because noise reduction suffers from a trade-off between the amount of noise removed and signal distortion. It is unknown if this important trade-off differs between normal-hearing (NH) and hearing-impaired (HI) listeners. This study separated the negative effect of noise reduction (distortion) from the positive effect (reduction of noise) to allow the measurement of the detection threshold for noise-reduction (NR) distortion. Twelve NH subjects and 12 subjects with mild to moderate sensorineural hearing loss participated in this study. The detection thresholds for distortion were determined using an adaptive procedure with a three-interval, two-alternative forced-choice paradigm. Different levels of distortion were obtained by changing the maximum amount of noise reduction. Participants were also asked to indicate their preferred NR strength. The detection threshold for overall distortion was higher for HI subjects than for NH subjects, suggesting that stronger noise reduction can be applied for HI listeners without affecting the perceived sound quality. However, the preferred NR strength of HI listeners was closer to their individual detection threshold for distortion than in NH listeners. This implies that HI listeners tolerate fewer audible distortions than NH listeners.

  18. Noise reduction and image enhancement using a hardware implementation of artificial neural networks

    Science.gov (United States)

    David, Robert; Williams, Erin; de Tremiolles, Ghislain; Tannhof, Pascal

    1999-03-01

    In this paper, we present a neural based solution developed for noise reduction and image enhancement using the ZISC, an IBM hardware processor which implements the Restricted Coulomb Energy algorithm and the K-Nearest Neighbor algorithm. Artificial neural networks present the advantages of processing time reduction in comparison with classical models, adaptability, and the weighted property of pattern learning. The goal of the developed application is image enhancement in order to restore old movies (noise reduction, focus correction, etc.), to improve digital television images, or to treat images which require adaptive processing (medical images, spatial images, special effects, etc.). Image results show a quantitative improvement over the noisy image as well as the efficiency of this system. Further enhancements are being examined to improve the output of the system.

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

    Science.gov (United States)

    Frost, Susan A.; Balas, Mark J.

    2011-01-01

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

  20. Interference suppression using a SAW-based adaptive filter

    Science.gov (United States)

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

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

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

    Science.gov (United States)

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

    2007-11-01

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

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

    International Nuclear Information System (INIS)

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

    2007-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2007-11-15

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

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

    Science.gov (United States)

    Abhayaratne, Charith

    2011-07-01

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

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

    Directory of Open Access Journals (Sweden)

    Saeed Mian Qaisar

    2009-01-01

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

  6. Application of system concept in vibration and noise reduction

    Directory of Open Access Journals (Sweden)

    SHENG Meiping

    2017-08-01

    Full Text Available Although certain vibration and noise control technologies are maturing, such as vibration absorption, vibration isolation, sound absorption and sound insulation, and new methods for specific frequency bands or special environments have been proposed unceasingly, there is still no guarantee that practical effective vibration and noise reduction can be obtained. An important constraint for vibration and noise reduction is the lack of a system concept, and the integrity and relevance of such practical systems as ship structure have not obtained enough attention. We have tried to use the system engineering theory in guiding vibration and noise reduction, and have already achieved certain effects. Based on the system concept, the noise control of a petroleum pipeline production workshop has been completed satisfactorily, and the abnormal noise source identification of an airplane has been accomplished successfully. We want to share our experience and suggestions to promote the popularization of the system engineering theory in vibration and noise control.

  7. Wind Noise Reduction in a Non-Porous Subsurface Windscreen

    Science.gov (United States)

    Zuckerwar, Allan J.; Shams, Qamar A.; Knight, H. Keith

    2012-01-01

    Measurements of wind noise reduction were conducted on a box-shaped, subsurface windscreen made of closed cell polyurethane foam. The windscreen was installed in the ground with the lid flush with the ground surface. The wind was generated by means of a fan, situated on the ground, and the wind speed was measured at the center of the windscreen lid with an ultrasonic anemometer. The wind speed was controlled by moving the fan to selected distances from the windscreen. The wind noise was measured on a PCB Piezotronics 3†electret microphone. Wind noise spectra were measured with the microphone exposed directly to the wind (atop the windscreen lid) and with the microphone installed inside the windscreen. The difference between the two spectra comprises the wind noise reduction. At wind speeds of 3, 5, and 7 m/s, the wind noise reduction is typically 15 dB over the frequency range of 0.1-20 Hz.

  8. BPSK Receiver Based on Recursive Adaptive Filter with Remodulation

    Directory of Open Access Journals (Sweden)

    N. Milosevic

    2011-12-01

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

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

    International Nuclear Information System (INIS)

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

    1988-01-01

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

  10. Adaptive Unscented Kalman Filter using Maximum Likelihood Estimation

    DEFF Research Database (Denmark)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2018-03-01

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

  12. Regularized Adaptive Notch Filters for Acoustic Howling Suppression

    DEFF Research Database (Denmark)

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

    2009-01-01

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

  13. Jet Noise Reduction by Microjets - A Parametric Study

    Science.gov (United States)

    Zaman, K. B. M. Q.

    2010-01-01

    The effect of injecting tiny secondary jets (microjets ) on the radiated noise from a subsonic primary jet is studied experimentally. The microjets are injected on to the primary jet near the nozzle exit with variable port geometry, working fluid and driving pressure. A clear noise reduction is observed that improves with increasing jet pressure. It is found that smaller diameter ports with higher driving pressure, but involving less thrust and mass fraction, can produce better noise reduction. A collection of data from the present as well as past experiments is examined in an attempt to correlate the noise reduction with the operating parameters. The results indicate that turbulent mixing noise reduction, as monitored by OASPL at a shallow angle, correlates with the ratio of jet to primary jet driving pressures normalized by the ratio of corresponding diameters (p d /pjD). With gaseous injection, the spectral amplitudes decrease at lower frequencies while an increase is noted at higher frequencies. It is apparent that this amplitude crossover is at least partly due to shock-associated noise from the underexpanded jets themselves. Such crossover is not seen with water injection since the flow in that case is incompressible and there is no shock-associated noise. Centerline velocity data show that larger noise reduction is accompanied by faster jet decay as well as significant reduction in turbulence intensities. While a physical understanding of the dependence of noise reduction on p d /pjD remains unclear, given this correlation, an analysis explains the observed dependence of the effect on various other parameters.

  14. Principal Components as a Data Reduction and Noise Reduction Technique

    Science.gov (United States)

    Imhoff, M. L.; Campbell, W. J.

    1982-01-01

    The potential of principal components as a pipeline data reduction technique for thematic mapper data was assessed and principal components analysis and its transformation as a noise reduction technique was examined. Two primary factors were considered: (1) how might data reduction and noise reduction using the principal components transformation affect the extraction of accurate spectral classifications; and (2) what are the real savings in terms of computer processing and storage costs of using reduced data over the full 7-band TM complement. An area in central Pennsylvania was chosen for a study area. The image data for the project were collected using the Earth Resources Laboratory's thematic mapper simulator (TMS) instrument.

  15. Adaptive training of feedforward neural networks by Kalman filtering

    International Nuclear Information System (INIS)

    Ciftcioglu, Oe.

    1995-02-01

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

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

    Science.gov (United States)

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

    2016-07-26

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

  17. Fuzzy Adaptive Cubature Kalman Filter for Integrated Navigation Systems

    Directory of Open Access Journals (Sweden)

    Chien-Hao Tseng

    2016-07-01

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

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

    Science.gov (United States)

    Mayers, Margaret; Wood, Lynnette

    1988-01-01

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

  19. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Søren Holdt

    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... diagonal (eigenvalue and singular value) decompositions and rank-revealing triangular decompositions (ULV, URV, VSV, ULLV and ULLIV). In addition we show how the subspace-based algorithms can be evaluated and compared by means of simple FIR filter interpretations. The algorithms are illustrated...... with working Matlab code and applications in speech processing....

  20. Speckle Noise Reduction for the Enhancement of Retinal Layers in Optical Coherence Tomography Images

    Directory of Open Access Journals (Sweden)

    Fereydoon Nowshiravan Rahatabad

    2015-09-01

    Full Text Available Introduction One of the most important pre-processing steps in optical coherence tomography (OCT is reducing speckle noise, resulting from multiple scattering of tissues, which degrades the quality of OCT images. Materials and Methods The present study focused on speckle noise reduction and edge detection techniques. Statistical filters with different masks and noise variances were applied on OCT and test images. Objective evaluation of both types of images was performed, using various image metrics such as peak signal-to-noise ratio (PSNR, root mean square error, correlation coefficient and elapsed time. For the purpose of recovery, Kuan filter was used as an input for edge enhancement. Also, a spatial filter was applied to improve image quality. Results The obtained results were presented as statistical tables and images. Based on statistical measures and visual quality of OCT images, Enhanced Lee filter (3×3 with a PSNR value of 43.6735 in low noise variance and Kuan filter (3×3 with a PSNR value of 37.2850 in high noise variance showed superior performance over other filters. Conclusion Based on the obtained results, by using speckle reduction filters such as Enhanced Lee and Kuan filters on OCT images, the number of compounded images, required to achieve a given image quality, could be reduced. Moreover, use of Kuan filters for promoting the edges allowed smoothing of speckle regions, while preserving image tissue texture.

  1. Digitally controlled active noise reduction with integrated speech communication

    NARCIS (Netherlands)

    Steeneken, H.J.M.; Verhave, J.A.

    2004-01-01

    Active noise reduction is a successful addition to passive ear-defenders for improvement of the sound attenuation at low frequencies. Design and assessment methods are discussed, focused on subjective and objective attenuation measurements, stability, and high noise level applications. Active noise

  2. Improved multi-microphone noise reduction preserving binaural cues

    NARCIS (Netherlands)

    Koutrouvelis, A.; Hendriks, R.C.; Jensen, J; Heusdens, R.; Dong, Min; Zheng, Thomas Fang

    2016-01-01

    We propose a new multi-microphone noise reduction technique for binaural cue preservation of the desired source and the interferers. This method is based on the linearly constrained minimum variance (LCMV) framework, where the constraints are used for the binaural cue preservation of the desired

  3. Adaptive particle filter for localization problem in service robotics

    Directory of Open Access Journals (Sweden)

    Heilig Alexander

    2018-01-01

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

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

    Directory of Open Access Journals (Sweden)

    M. Butsenko

    2016-06-01

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

  5. Model Adaptation for Prognostics in a Particle Filtering Framework

    Directory of Open Access Journals (Sweden)

    Bhaskar Saha

    2011-01-01

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

  6. Model Adaptation for Prognostics in a Particle Filtering Framework

    Science.gov (United States)

    Saha, Bhaskar; Goebel, Kai Frank

    2011-01-01

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

  7. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

    Soury, Hamza

    2014-01-06

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

  8. Reduced Rank Adaptive Filtering in Impulsive Noise Environments

    KAUST Repository

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2005-06-01

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

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

    NARCIS (Netherlands)

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

    1994-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Anthony Lombard

    2009-01-01

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

  12. A New Adaptive Framework for Collaborative Filtering Prediction.

    Science.gov (United States)

    Almosallam, Ibrahim A; Shang, Yi

    2008-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Samuel Boudet

    2014-01-01

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

  14. Adaptive anisotropic diffusion filtering of Monte Carlo dose distributions

    International Nuclear Information System (INIS)

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

    2003-01-01

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

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

    Science.gov (United States)

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

    2013-11-01

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

  16. Image noise reduction algorithm for digital subtraction angiography: clinical results.

    Science.gov (United States)

    Söderman, Michael; Holmin, Staffan; Andersson, Tommy; Palmgren, Charlotta; Babic, Draženko; Hoornaert, Bart

    2013-11-01

    To test the hypothesis that an image noise reduction algorithm designed for digital subtraction angiography (DSA) in interventional neuroradiology enables a reduction in the patient entrance dose by a factor of 4 while maintaining image quality. This clinical prospective study was approved by the local ethics committee, and all 20 adult patients provided informed consent. DSA was performed with the default reference DSA program, a quarter-dose DSA program with modified acquisition parameters (to reduce patient radiation dose exposure), and a real-time noise-reduction algorithm. Two consecutive biplane DSA data sets were acquired in each patient. The dose-area product (DAP) was calculated for each image and compared. A randomized, blinded, offline reading study was conducted to show noninferiority of the quarter-dose image sets. Overall, 40 samples per treatment group were necessary to acquire 80% power, which was calculated by using a one-sided α level of 2.5%. The mean DAP with the quarter-dose program was 25.3% ± 0.8 of that with the reference program. The median overall image quality scores with the reference program were 9, 13, and 12 for readers 1, 2, and 3, respectively. These scores increased slightly to 12, 15, and 12, respectively, with the quarter-dose program imaging chain. In DSA, a change in technique factors combined with a real-time noise-reduction algorithm will reduce the patient entrance dose by 75%, without a loss of image quality. RSNA, 2013

  17. Study of noise reduction characteristics of double-wall panels

    Science.gov (United States)

    Navaneethan, R.; Quayle, B.; Stevenson, S.; Graham, M.

    1983-05-01

    The noise reduction characteristics of general aviation type, flat, double-wall structures were investigated. The experimental study was carried out on 20-by-20 inch panels with an exposed area of 18 by 18 inches. A frequency range from 20 to 5000 Hz was covered. The experimental results, in general, follow the expected trends. At low frequencies the double-wall structures are no better than the single-wall structures. However, for depths normally used in the general aviation industry, the double-wall panels are very attractive. The graphite-spoxy skin panels have higher noise reduction at very low frequencies ( 100 Hz) than the Kevlar skin panels. But the aluminum panels have higher noise reduction in the high frequency region, due to their greater mass. Use of fiberglass insulation is not effective in the low frequency region, and at times it is even negative. But the insulation is effective in the high-frequency region. The theoretical model for predicting the transmission loss of these multilayered panels is also discussed.

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

    Science.gov (United States)

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

    2015-12-01

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

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

    International Nuclear Information System (INIS)

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

    2000-01-01

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

  20. Adaptive Kalman filtering for diagnosis of multiple component degradations

    International Nuclear Information System (INIS)

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

    2005-01-01

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

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

    Directory of Open Access Journals (Sweden)

    A. A. Golovkov

    2016-01-01

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

  2. Edge and line enhancement by adaptive lattice filtering

    International Nuclear Information System (INIS)

    Brolley, J.E.

    1979-01-01

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

  3. Reduced rank adaptive filtering in impulsive noise environments

    KAUST Repository

    Soury, Hamza

    2014-11-01

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

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

    DEFF Research Database (Denmark)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

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

    Science.gov (United States)

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

    2017-12-01

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

  7. Noise reduction methods for nucleic acid and macromolecule sequencing

    Science.gov (United States)

    Schuller, Ivan K.; Di Ventra, Massimiliano; Balatsky, Alexander

    2018-05-08

    Methods, systems, and devices are disclosed for processing macromolecule sequencing data with substantial noise reduction. In one aspect, a method for reducing noise in a sequential measurement of a macromolecule comprising serial subunits includes cross-correlating multiple measured signals of a physical property of subunits of interest of the macromolecule, the multiple measured signals including the time data associated with the measurement of the signal, to remove or at least reduce signal noise that is not in the same frequency and in phase with the systematic signal contribution of the measured signals.

  8. Low frequency noise reduction using stiff light composite panels

    Institute of Scientific and Technical Information of China (English)

    DENG Yongchang; LIN Weizheng

    2003-01-01

    The experiment presented in this paper is to investigate and analyze the noise reduction at low frequency using stiff light composite panels. Since these composite panels are made of lightweight and stiff materials, this actuation strategy will enable the creation of composite panels for duct noise control without using traditional heavy structural mass. The results suggest that the mass-spring resonance absorption in the case of a comparatively stiff thick panel with a thin flexible plate is more efficient with minimum weight, when subjected to low-frequency (<500 Hz). The efficiency of the panel absorber depends on the mass of the thin flexible plate and the stiffness of the panel.

  9. Wind Noise Reduction using Non-negative Sparse Coding

    DEFF Research Database (Denmark)

    Schmidt, Mikkel N.; Larsen, Jan; Hsiao, Fu-Tien

    2007-01-01

    We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model ...... and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task....

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

    National Research Council Canada - National Science Library

    Dhillon, Santpal

    2001-01-01

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

  11. Market Analysis of Soundproof and Noise Reduction Plate

    Directory of Open Access Journals (Sweden)

    Chang Heyu

    2017-01-01

    Full Text Available Since the reform and opening up, China has a booming economy development, transportation industry as an important part of the economic processes has made a considerable progress, and continue to promote the rapid economic development. At the end of 2012, China's highway mileage has reached 4.238 million km and highway mileage ranks first in the world. Transportation in promoting rapid economic development, but also brings the traffic noise which has a tremendous influence on residents’ daily life, and this influence has gradually deepening and widening, in this situation the production of noise reduction panel gradually emerged. In this paper, a noise barriers manufacturer’ business model in Beijing Daxing District and analyze existing data will be analyzed, it will give much guidance and reference for a new noise reduction factory. The study has shown that plant will target all levels of government departments in charge of roads and the property of management department of residence as our customer groups, increasing technological innovation and improving product quality to establish a more extensive business relationships, and gradually formed noise barriers market competitiveness.

  12. Gene regulation and noise reduction by coupling of stochastic processes

    Science.gov (United States)

    Ramos, Alexandre F.; Hornos, José Eduardo M.; Reinitz, John

    2015-02-01

    Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.

  13. Gene regulation and noise reduction by coupling of stochastic processes.

    Science.gov (United States)

    Ramos, Alexandre F; Hornos, José Eduardo M; Reinitz, John

    2015-02-01

    Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.

  14. Potential for Landing Gear Noise Reduction on Advanced Aircraft Configurations

    Science.gov (United States)

    Thomas, Russell H.; Nickol, Craig L.; Burley, Casey L.; Guo, Yueping

    2016-01-01

    The potential of significantly reducing aircraft landing gear noise is explored for aircraft configurations with engines installed above the wings or the fuselage. An innovative concept is studied that does not alter the main gear assembly itself but does shorten the main strut and integrates the gear in pods whose interior surfaces are treated with acoustic liner. The concept is meant to achieve maximum noise reduction so that main landing gears can be eliminated as a major source of airframe noise. By applying this concept to an aircraft configuration with 2025 entry-into-service technology levels, it is shown that compared to noise levels of current technology, the main gear noise can be reduced by 10 EPNL dB, bringing the main gear noise close to a floor established by other components such as the nose gear. The assessment of the noise reduction potential accounts for design features for the advanced aircraft configuration and includes the effects of local flow velocity in and around the pods, gear noise reflection from the airframe, and reflection and attenuation from acoustic liner treatment on pod surfaces and doors. A technical roadmap for maturing this concept is discussed, and the possible drag increase at cruise due to the addition of the pods is identified as a challenge, which needs to be quantified and minimized possibly with the combination of detailed design and application of drag reduction technologies.

  15. A two-microphone noise reduction system for cochlear implant users with nearby microphones. Part II: Performance Evaluation

    OpenAIRE

    Kompis, Martin; Bertram, Matthias; Senn, Pascal; Müller, Joachim; Pelizzone, Marco; Häusler, Rudolf

    2008-01-01

    Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear) often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm) and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signa...

  16. Sensor locations and noise reduction in high-purity batch distillation control loops

    Directory of Open Access Journals (Sweden)

    Oisiovici R.M.

    2000-01-01

    Full Text Available The influence of the sensor locations on the composition control of high-purity batch distillation columns has been investigated. Using concepts of the nonlinear control theory, an input-output linearizing controller was implemented to keep the distillate composition constant at a desired value by varying the reflux ratio. An Extended Kalman Filter was developed to estimate the compositions required in the control algorithm using temperature measurements. In the presence of measurement noise, the control performance depended greatly on the sensor locations. Placing the sensors further from the top stages reduced the detrimental effects of noise but increased the inference error. To achieve accurate composition control, both noise reduction and composition estimate accuracy should be considered in the selection of the sensor locations.

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

    OpenAIRE

    Babič, Rudolf; Horvat, Bogomir; Osebik, Davorin

    2001-01-01

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

  18. Acoustic noise reduction in T 1- and proton-density-weighted turbo spin-echo imaging.

    Science.gov (United States)

    Ott, Martin; Blaimer, Martin; Breuer, Felix; Grodzki, David; Heismann, Björn; Jakob, Peter

    2016-02-01

    To reduce acoustic noise levels in T 1-weighted and proton-density-weighted turbo spin-echo (TSE) sequences, which typically reach acoustic noise levels up to 100 dB(A) in clinical practice. Five acoustic noise reduction strategies were combined: (1) gradient ramps and shapes were changed from trapezoidal to triangular, (2) variable-encoding-time imaging was implemented to relax the phase-encoding gradient timing, (3) RF pulses were adapted to avoid the need for reversing the polarity of the slice-rewinding gradient, (4) readout bandwidth was increased to provide more time for gradient activity on other axes, (5) the number of slices per TR was reduced to limit the total gradient activity per unit time. We evaluated the influence of each measure on the acoustic noise level, and conducted in vivo measurements on a healthy volunteer. Sound recordings were taken for comparison. An overall acoustic noise reduction of up to 16.8 dB(A) was obtained by the proposed strategies (1-4) and the acquisition of half the number of slices per TR only. Image quality in terms of SNR and CNR was found to be preserved. The proposed measures in this study allowed a threefold reduction in the acoustic perception of T 1-weighted and proton-density-weighted TSE sequences compared to a standard TSE-acquisition. This could be achieved without visible degradation of image quality, showing the potential to improve patient comfort and scan acceptability.

  19. Reduced-Rank Adaptive Filtering Using Krylov Subspace

    Directory of Open Access Journals (Sweden)

    Sergueï Burykh

    2003-01-01

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

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

    Science.gov (United States)

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

    2014-12-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

  2. Theory of affine projection algorithms for adaptive filtering

    CERN Document Server

    Ozeki, Kazuhiko

    2016-01-01

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

  3. Foundations of Active Control - Active Noise Reduction Helmets

    DEFF Research Database (Denmark)

    Elmkjær, Torsten Haaber Leth

    2008-01-01

    rate is increased which in turn most likely also will lead to an increased ANR bandwidth. The hybrid system is also constituted from a continuous-time feedback system (FBS) and a discrete-time FBS. The continuous-time FBS is primarily responsible for additional broadband noise reduction, whereas...... the discrete-time FBS primarily is responsible for the attenuation of periodic signals. Owing to the requirement on causal operation of a physical AC system time delays will also to a large extent determine the achievable performance in FFS design and in particular in FBS design. A quantity referred...... on a head and torso simulator (HATS), is exposed to diffuse sound field illumination. By applying the JCRSA method the spatially-weighted-averaged acquisition lead times provided by the reference sensors relative to the performance sensors are estimated to be as much as 800-900μs. The thesis also includes...

  4. Quantum Noise Reduction with Pulsed Light in Optical Fibers.

    Science.gov (United States)

    Bergman, Keren

    Optical fibers offer considerable advantages over bulk nonlinear media for the generation of squeezed states. This thesis reports on experimental investigations of reducing quantum noise by means of squeezing in nonlinear fiber optic interferometers. Fibers have low insertion loss which allows for long interaction lengths. High field intensities are easily achieved in the small cores of single mode fibers. Additionally, the nonlinear process employed is self phase modulation or the Kerr effect, whose broad band nature requires no phase matching and can be exploited with ultra-short pulses of high peak intensity. All these advantageous features of fibers result in easily obtained large nonlinear phase shifts and subsequently large squeezing parameters. By the self phase modulation process a correlation is produced between the phase and amplitude fluctuations of the optical field. The attenuated or squeezed quadrature has a lower noise level than the initial level associated with the coherent state field before propagation. The resulting reduced quantum noise quadrature can be utilized to improve the sensitivity of a phase measuring instrument such as an interferometer. Because the Kerr nonlinearity is a degenerate self pumping process, the squeezed noise is at the same frequency as the pump field. Classical pump noise can therefore interfere with the desired measurement of the quantum noise reduction. The most severe noise process is the phase noise caused by thermally induced index modulation of the fiber. This noise termed Guided Acoustic Wave Brillouin Scattering, or GAWBS, by previous researchers is studied and analyzed. Experiments performed to overcome GAWBS successfully with several schemes are described. An experimental demonstration of an interferometric measurement with better sensitivity than the standard quantum limit is described. The results lead to new understandings into the limitations of quantum noise reduction that can be achieved in the

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

    Directory of Open Access Journals (Sweden)

    Nataliya Tulyakova

    2017-03-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jan eKneissler

    2015-04-01

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

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

    International Nuclear Information System (INIS)

    Brolley, J.E.

    1978-11-01

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

  9. Spatial-temporal noise reduction method optimized for real-time implementation

    Science.gov (United States)

    Romanenko, I. V.; Edirisinghe, E. A.; Larkin, D.

    2013-02-01

    Image de-noising in the spatial-temporal domain has been a problem studied in-depth in the field of digital image processing. However complexity of algorithms often leads to high hardware resource usage, or computational complexity and memory bandwidth issues, making their practical use impossible. In our research we attempt to solve these issues with an optimized implementation of a practical spatial-temporal de-noising algorithm. Spatial-temporal filtering was performed in Bayer RAW data space, which allowed us to benefit from predictable sensor noise characteristics and reduce memory bandwidth requirements. The proposed algorithm efficiently removes different kinds of noise in a wide range of signal to noise ratios. In our algorithm the local motion compensation is performed in Bayer RAW data space, while preserving the resolution and effectively improving the signal to noise ratios of moving objects. The main challenge for the use of spatial-temporal noise reduction algorithms in video applications is the compromise between the quality of the motion prediction and the complexity of the algorithm and required memory bandwidth. In photo and video applications it is very important that moving objects should stay sharp, while the noise is efficiently removed in both the static background and moving objects. Another important use case is the case when background is also non-static as well as the foreground where objects are also moving. Taking into account the achievable improvement in PSNR (on the level of the best known noise reduction techniques, like VBM3D) and low algorithmic complexity, enabling its practical use in commercial video applications, the results of our research can be very valuable.

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

    Czech Academy of Sciences Publication Activity Database

    Dedecius, Kamil

    2017-01-01

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

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

    DEFF Research Database (Denmark)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2016-07-16

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

  13. Propulsion Noise Reduction Research in the NASA Advanced Air Transport Technology Project

    Science.gov (United States)

    Van Zante, Dale; Nark, Douglas; Fernandez, Hamilton

    2017-01-01

    The Aircraft Noise Reduction (ANR) sub-project is focused on the generation, development, and testing of component noise reduction technologies progressing toward the NASA far term noise goals while providing associated near and mid-term benefits. The ANR sub-project has efforts in airframe noise reduction, propulsion (including fan and core) noise reduction, acoustic liner technology, and propulsion airframe aeroacoustics for candidate conventional and unconventional aircraft configurations. The current suite of propulsion specific noise research areas is reviewed along with emerging facility and measurement capabilities. In the longer term, the changes in engine and aircraft configuration will influence the suite of technologies necessary to reduce noise in next generation systems.

  14. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

    Kammoun, Abla

    2016-09-13

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

  15. Adaptive spatial filtering for daytime satellite quantum key distribution

    Science.gov (United States)

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

    2014-11-01

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

  16. Optimal adaptive normalized matched filter for large antenna arrays

    KAUST Repository

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    杨元喜; 高为广

    2007-01-01

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

  20. Evaluation of a Noise Reduction Procedure for Chest Radiography

    Science.gov (United States)

    Fukui, Ryohei; Ishii, Rie; Kodani, Kazuhiko; Kanasaki, Yoshiko; Suyama, Hisashi; Watanabe, Masanari; Nakamoto, Masaki; Fukuoka, Yasushi

    2013-01-01

    Background The aim of this study was to evaluate the usefulness of noise reduction procedure (NRP), a function in the new image processing for chest radiography. Methods A CXDI-50G Portable Digital Radiography System (Canon) was used for X-ray detection. Image noise was analyzed with a noise power spectrum (NPS) and a burger phantom was used for evaluation of density resolution. The usefulness of NRP was evaluated by chest phantom images and clinical chest radiography. We employed the Bureau of Radiological Health Method for scoring chest images while carrying out our observations. Results NPS through the use of NRP was improved compared with conventional image processing (CIP). The results in image quality showed high-density resolution through the use of NRP, so that chest radiography examination can be performed with a low dose of radiation. Scores were significantly higher than for CIP. Conclusion In this study, use of NRP led to a high evaluation in these so we are able to confirm the usefulness of NRP for clinical chest radiography. PMID:24574577

  1. Supersonic impinging jet noise reduction using a hybrid control technique

    Science.gov (United States)

    Wiley, Alex; Kumar, Rajan

    2015-07-01

    Control of the highly resonant flowfield associated with supersonic impinging jet has been experimentally investigated. Measurements were made in the supersonic impinging jet facility at the Florida State University for a Mach 1.5 ideally expanded jet. Measurements included unsteady pressures on a surface plate near the nozzle exit, acoustics in the nearfield and beneath the impingement plane, and velocity field using particle image velocimetry. Both passive control using porous surface and active control with high momentum microjet injection are effective in reducing nearfield noise and flow unsteadiness over a range of geometrical parameters; however, the type of noise reduction achieved by the two techniques is different. The passive control reduces broadband noise whereas microjet injection attenuates high amplitude impinging tones. The hybrid control, a combination of two control methods, reduces both broadband and high amplitude impinging tones and surprisingly its effectiveness is more that the additive effect of the two control techniques. The flow field measurements show that with hybrid control the impinging jet is stabilized and the turbulence quantities such as streamwise turbulence intensity, transverse turbulence intensity and turbulent shear stress are significantly reduced.

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

    Science.gov (United States)

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

    2012-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

  4. A blood pressure monitor with robust noise reduction system under linear cuff inflation and deflation.

    Science.gov (United States)

    Usuda, Takashi; Kobayashi, Naoki; Takeda, Sunao; Kotake, Yoshifumi

    2010-01-01

    We have developed the non-invasive blood pressure monitor which can measure the blood pressure quickly and robustly. This monitor combines two measurement mode: the linear inflation and the linear deflation. On the inflation mode, we realized a faster measurement with rapid inflation rate. On the deflation mode, we realized a robust noise reduction. When there is neither noise nor arrhythmia, the inflation mode incorporated on this monitor provides precise, quick and comfortable measurement. Once the inflation mode fails to calculate appropriate blood pressure due to body movement or arrhythmia, then the monitor switches automatically to the deflation mode and measure blood pressure by using digital signal processing as wavelet analysis, filter bank, filter combined with FFT and Inverse FFT. The inflation mode succeeded 2440 measurements out of 3099 measurements (79%) in an operating room and a rehabilitation room. The new designed blood pressure monitor provides the fastest measurement for patient with normal circulation and robust measurement for patients with body movement or severe arrhythmia. Also this fast measurement method provides comfortableness for patients.

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

    Energy Technology Data Exchange (ETDEWEB)

    Donohoe, G.W.

    1977-01-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

  7. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

    Song, Hajoon

    2013-10-01

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

  8. An Adjoint-Based Adaptive Ensemble Kalman Filter

    KAUST Repository

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

    2013-01-01

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

  9. Variable Span Filters for Speech Enhancement

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Benesty, Jacob; Christensen, Mads Græsbøll

    2016-01-01

    In this work, we consider enhancement of multichannel speech recordings. Linear filtering and subspace approaches have been considered previously for solving the problem. The current linear filtering methods, although many variants exist, have limited control of noise reduction and speech...

  10. 40 CFR 211.207 - Computation of the noise -reduction rating (NRR).

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 24 2010-07-01 2010-07-01 false Computation of the noise -reduction... of the noise -reduction rating (NRR). Calculate the NRR for hearing protective devices by... “A”-weighting relative response corrections applied to any sound levels at the indicated octave band...

  11. Acoustical and Perceptual Comparison of Noise Reduction and Compression in Hearing Aids

    NARCIS (Netherlands)

    Brons, Inge; Houben, Rolph; Dreschler, Wouter A.

    2015-01-01

    Noise reduction and dynamic-range compression are generally applied together in hearing aids but may have opposite effects on amplification. This study evaluated the acoustical and perceptual effects of separate and combined processing of noise reduction and compression. Recordings of the output of

  12. An Assessment of Psychological Noise Reduction by Landscape Plants

    Directory of Open Access Journals (Sweden)

    Fan Yang

    2011-04-01

    Full Text Available The emphasis in the term ‘Green Transportation’ is on the word ‘green’. Green transportation focuses on the construction of a slow transport system with a visually pleasing, easy and secure trip environment composed of urban parks, green roadside spaces and some other space that is full of landscape plants. This trip environment encourages residents to make trip choices that reduce fuel consumption and pollution and is one of the most important ways of popularizing green transportation. To study the psychological benefits provided by urban parks and other landscape environments, we combined a subjective approach (a questionnaire with an objective quantitative approach (emotional tests using an electroencephalogram; EEG. Using a questionnaire survey, we found that 90% of the subjects believed that landscape plants contribute to noise reduction and that 55% overrated the plants’ actual ability to attenuate noise. Two videos (showing a traffic scene and a plant scene were shown to 40 participants on video glasses. We detected and recorded EEG values with a portable electroencephalograph, and a comparison between the results of the two groups revealed that there was a highly significant asymmetry between the EEG activity of the vegetation scene and traffic scene groups. The results suggest that the emotions aroused by noise and visual stimuli are manifested in the synchronization of beta frequency band and the desynchronization of alpha frequency band, indicating that landscape plants can moderate or buffer the effects of noise. These findings indicate that landscape plants provide excess noise attenuating effects through subjects’ emotional processing, which we term ‘psychological noise reduction’.

  13. Prediction of Landing Gear Noise Reduction and Comparison to Measurements

    Science.gov (United States)

    Lopes, Leonard V.

    2010-01-01

    Noise continues to be an ongoing problem for existing aircraft in flight and is projected to be a concern for next generation designs. During landing, when the engines are operating at reduced power, the noise from the airframe, of which landing gear noise is an important part, is equal to the engine noise. There are several methods of predicting landing gear noise, but none have been applied to predict the change in noise due to a change in landing gear design. The current effort uses the Landing Gear Model and Acoustic Prediction (LGMAP) code, developed at The Pennsylvania State University to predict the noise from landing gear. These predictions include the influence of noise reduction concepts on the landing gear noise. LGMAP is compared to wind tunnel experiments of a 6.3%-scale Boeing 777 main gear performed in the Quiet Flow Facility (QFF) at NASA Langley. The geometries tested in the QFF include the landing gear with and without a toboggan fairing and the door. It is shown that LGMAP is able to predict the noise directives and spectra from the model-scale test for the baseline configuration as accurately as current gear prediction methods. However, LGMAP is also able to predict the difference in noise caused by the toboggan fairing and by removing the landing gear door. LGMAP is also compared to far-field ground-based flush-mounted microphone measurements from the 2005 Quiet Technology Demonstrator 2 (QTD 2) flight test. These comparisons include a Boeing 777-300ER with and without a toboggan fairing that demonstrate that LGMAP can be applied to full-scale flyover measurements. LGMAP predictions of the noise generated by the nose gear on the main gear measurements are also shown.

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

    Science.gov (United States)

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

    2014-05-09

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

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

    Science.gov (United States)

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

    2018-01-01

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

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

    International Nuclear Information System (INIS)

    Huang Kuidong; Zhang Dinghua; Jin Yanfang

    2009-01-01

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

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

    Science.gov (United States)

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

    2018-02-01

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

  18. Sensitivity of PPI analysis to differences in noise reduction strategies.

    Science.gov (United States)

    Barton, M; Marecek, R; Rektor, I; Filip, P; Janousova, E; Mikl, M

    2015-09-30

    In some fields of fMRI data analysis, using correct methods for dealing with noise is crucial for achieving meaningful results. This paper provides a quantitative assessment of the effects of different preprocessing and noise filtering strategies on psychophysiological interactions (PPI) methods for analyzing fMRI data where noise management has not yet been established. Both real and simulated fMRI data were used to assess these effects. Four regions of interest (ROIs) were chosen for the PPI analysis on the basis of their engagement during two tasks. PPI analysis was performed for 32 different preprocessing and analysis settings, which included data filtering with RETROICOR or no such filtering; different filtering of the ROI "seed" signal with a nuisance data-driven time series; and the involvement of these data-driven time series in the subsequent PPI GLM analysis. The extent of the statistically significant results was quantified at the group level using simple descriptive statistics. Simulated data were generated to assess statistical improvement of different filtering strategies. We observed that different approaches for dealing with noise in PPI analysis yield differing results in real data. In simulated data, we found RETROICOR, seed signal filtering and the addition of data-driven covariates to the PPI design matrix significantly improves results. We recommend the use of RETROICOR, and data-driven filtering of the whole data, or alternatively, seed signal filtering with data-driven signals and the addition of data-driven covariates to the PPI design matrix. Copyright © 2015 Elsevier B.V. All rights reserved.

  19. Noise Reduction of MEMS Gyroscope Based on Direct Modeling for an Angular Rate Signal

    Directory of Open Access Journals (Sweden)

    Liang Xue

    2015-02-01

    Full Text Available In this paper, a novel approach for processing the outputs signal of the microelectromechanical systems (MEMS gyroscopes was presented to reduce the bias drift and noise. The principle for the noise reduction was presented, and an optimal Kalman filter (KF was designed by a steady-state filter gain obtained from the analysis of KF observability. In particular, the true angular rate signal was directly modeled to obtain an optimal estimate and make a self-compensation for the gyroscope without needing other sensor’s information, whether in static or dynamic condition. A linear fit equation that describes the relationship between the KF bandwidth and modeling parameter of true angular rate was derived from the analysis of KF frequency response. The test results indicated that the MEMS gyroscope having an ARW noise of 4.87°/h0.5 and a bias instability of 44.41°/h were reduced to 0.4°/h0.5 and 4.13°/h by the KF under a given bandwidth (10 Hz, respectively. The 1σ estimated error was reduced from 1.9°/s to 0.14°/s and 1.7°/s to 0.5°/s in the constant rate test and swing rate test, respectively. It also showed that the filtered angular rate signal could well reflect the dynamic characteristic of the input rate signal in dynamic conditions. The presented algorithm is proved to be effective at improving the measurement precision of the MEMS gyroscope.

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

    Science.gov (United States)

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

    2017-10-01

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

  1. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    Bibi, Adel Aamer

    2015-12-07

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

  2. Model Adaptation for Prognostics in a Particle Filtering Framework

    Data.gov (United States)

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

  3. Multi-template Scale-Adaptive Kernelized Correlation Filters

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2015-01-01

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

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

    NARCIS (Netherlands)

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

    2007-01-01

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

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

    Science.gov (United States)

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

    2014-06-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Bian Hongwei; Jin Zhihua; Tian Weifeng

    2006-01-01

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

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

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

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

    International Nuclear Information System (INIS)

    Semwal, Girish; Rastogi, Vipul

    2014-01-01

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

  10. Design of High Quality Chemical XOR Gates with Noise Reduction.

    Science.gov (United States)

    Wood, Mackenna L; Domanskyi, Sergii; Privman, Vladimir

    2017-07-05

    We describe a chemical XOR gate design that realizes gate-response function with filtering properties. Such gate-response function is flat (has small gradients) at and in the vicinity of all the four binary-input logic points, resulting in analog noise suppression. The gate functioning involves cross-reaction of the inputs represented by pairs of chemicals to produce a practically zero output when both are present and nearly equal. This cross-reaction processing step is also designed to result in filtering at low output intensities by canceling out the inputs if one of the latter has low intensity compared with the other. The remaining inputs, which were not reacted away, are processed to produce the output XOR signal by chemical steps that result in filtering at large output signal intensities. We analyze the tradeoff resulting from filtering, which involves loss of signal intensity. We also discuss practical aspects of realizations of such XOR gates. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    Directory of Open Access Journals (Sweden)

    Qiguang Zhu

    2014-05-01

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

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

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

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

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

    African Journals Online (AJOL)

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

  14. Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline

    Science.gov (United States)

    Yu, Xuchao; Liang, Wei; Zhang, Laibin; Jin, Hao; Qiu, Jingwei

    2016-05-01

    During the last decades, leak detection for natural gas pipeline has become one of the paramount concerns of pipeline operators and researchers across the globe. However, acoustic wave method has been proved to be an effective way to identify and localize leakage for gas pipeline. Considering the fact that noises inevitably exist in the acoustic signals collected, noise reduction should be enforced on the signals for subsequent data mining and analysis. Thus, an integrated acoustic noise reduction method based on DTCWT and SVD is proposed in this study. The method is put forward based on the idea that noise reduction strategy should match the characteristics of the noisy signal. According to previous studies, it is known that the energy of acoustic signals collected under leaking condition is mainly concentrated in low-frequency portion (0-100 Hz). And ultralow-frequency component (0-5 Hz), which is taken as the characteristic frequency band in this study, can propagate a relatively longer distance and be captured by sensors. Therefore, in order to filter the noises and to reserve the characteristic frequency band, DTCWT is taken as the core to conduct multilevel decomposition and refining for acoustic signals and SVD is employed to eliminate noises in non-characteristic bands. Both simulation and field experiments show that DTCWT-SVD is an excellent method for acoustic noise reduction. At the end of this study, application in leakage localization shows that it becomes much easier and a little more accurate to estimate the location of leak hole after noise reduction by DTCWT-SVD.

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

    Science.gov (United States)

    Hsu, Ching-Kun

    2015-01-01

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

  16. A quantitative comparison of noise reduction across five commercial (hybrid and model-based) iterative reconstruction techniques: an anthropomorphic phantom study.

    Science.gov (United States)

    Patino, Manuel; Fuentes, Jorge M; Hayano, Koichi; Kambadakone, Avinash R; Uyeda, Jennifer W; Sahani, Dushyant V

    2015-02-01

    OBJECTIVE. The objective of our study was to compare the performance of three hybrid iterative reconstruction techniques (IRTs) (ASiR, iDose4, SAFIRE) and their respective strengths for image noise reduction on low-dose CT examinations using filtered back projection (FBP) as the standard reference. Also, we compared the performance of these three hybrid IRTs with two model-based IRTs (Veo and IMR) for image noise reduction on low-dose examinations. MATERIALS AND METHODS. An anthropomorphic abdomen phantom was scanned at 100 and 120 kVp and different tube current-exposure time products (25-100 mAs) on three CT systems (for ASiR and Veo, Discovery CT750 HD; for iDose4 and IMR, Brilliance iCT; and for SAFIRE, Somatom Definition Flash). Images were reconstructed using FBP and using IRTs at various strengths. Nine noise measurements (mean ROI size, 423 mm(2)) on extracolonic fat for the different strengths of IRTs were recorded and compared with FBP using ANOVA. Radiation dose, which was measured as the volume CT dose index and dose-length product, was also compared. RESULTS. There were no significant differences in radiation dose and image noise among the scanners when FBP was used (p > 0.05). Gradual image noise reduction was observed with each increasing increment of hybrid IRT strength, with a maximum noise suppression of approximately 50% (48.2-53.9%). Similar noise reduction was achieved on the scanners by applying specific hybrid IRT strengths. Maximum noise reduction was higher on model-based IRTs (68.3-81.1%) than hybrid IRTs (48.2-53.9%) (p < 0.05). CONCLUSION. When constant scanning parameters are used, radiation dose and image noise on FBP are similar for CT scanners made by different manufacturers. Significant image noise reduction is achieved on low-dose CT examinations rendered with IRTs. The image noise on various scanners can be matched by applying specific hybrid IRT strengths. Model-based IRTs attain substantially higher noise reduction than hybrid

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

    International Nuclear Information System (INIS)

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

    2015-01-01

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

  18. Adaptive Filtering for Aeroservoelastic Response Suppression, Phase I

    Data.gov (United States)

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

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

    Directory of Open Access Journals (Sweden)

    ZHU Xiaoxiao

    2018-02-01

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

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

    Science.gov (United States)

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

    2017-07-01

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

  1. 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.

  2. TIPS bilateral noise reduction in 4D CT perfusion scans produces high-quality cerebral blood flow maps

    Energy Technology Data Exchange (ETDEWEB)

    Mendrik, Adrienne M; Van Ginneken, Bram; Viergever, Max A [Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht (Netherlands); Vonken, Evert-jan; De Jong, Hugo W; Riordan, Alan; Van Seeters, Tom; Smit, Ewoud J; Prokop, Mathias, E-mail: a.m.mendrik@gmail.com [Radiology Department, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht (Netherlands)

    2011-07-07

    Cerebral computed tomography perfusion (CTP) scans are acquired to detect areas of abnormal perfusion in patients with cerebrovascular diseases. These 4D CTP scans consist of multiple sequential 3D CT scans over time. Therefore, to reduce radiation exposure to the patient, the amount of x-ray radiation that can be used per sequential scan is limited, which results in a high level of noise. To detect areas of abnormal perfusion, perfusion parameters are derived from the CTP data, such as the cerebral blood flow (CBF). Algorithms to determine perfusion parameters, especially singular value decomposition, are very sensitive to noise. Therefore, noise reduction is an important preprocessing step for CTP analysis. In this paper, we propose a time-intensity profile similarity (TIPS) bilateral filter to reduce noise in 4D CTP scans, while preserving the time-intensity profiles (fourth dimension) that are essential for determining the perfusion parameters. The proposed TIPS bilateral filter is compared to standard Gaussian filtering, and 4D and 3D (applied separately to each sequential scan) bilateral filtering on both phantom and patient data. Results on the phantom data show that the TIPS bilateral filter is best able to approach the ground truth (noise-free phantom), compared to the other filtering methods (lowest root mean square error). An observer study is performed using CBF maps derived from fifteen CTP scans of acute stroke patients filtered with standard Gaussian, 3D, 4D and TIPS bilateral filtering. These CBF maps were blindly presented to two observers that indicated which map they preferred for (1) gray/white matter differentiation, (2) detectability of infarcted area and (3) overall image quality. Based on these results, the TIPS bilateral filter ranked best and its CBF maps were scored to have the best overall image quality in 100% of the cases by both observers. Furthermore, quantitative CBF and cerebral blood volume values in both the phantom and the

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

    Science.gov (United States)

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

    1984-11-01

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

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

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

    Science.gov (United States)

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

    1990-01-01

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

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

    KAUST Repository

    Xie, Qing

    2016-01-12

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

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

    Czech Academy of Sciences Publication Activity Database

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

    2013-01-01

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

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

    OpenAIRE

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

    2011-01-01

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

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

    Institute of Scientific and Technical Information of China (English)

    Li Shu; Zhuo Jiashou; Ren Qingwen

    2000-01-01

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

  10. Adaptive Matrices and Filters for Color Texture Classification

    NARCIS (Netherlands)

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

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

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

    Directory of Open Access Journals (Sweden)

    Mohammadreza Asghari Oskoei

    2017-11-01

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

  12. The use of anatomical information for molecular image reconstruction algorithms: Attention/Scatter correction, motion compensation, and noise reduction

    Energy Technology Data Exchange (ETDEWEB)

    Chun, Se Young [School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan (Korea, Republic of)

    2016-03-15

    PET and SPECT are important tools for providing valuable molecular information about patients to clinicians. Advances in nuclear medicine hardware technologies and statistical image reconstruction algorithms enabled significantly improved image quality. Sequentially or simultaneously acquired anatomical images such as CT and MRI from hybrid scanners are also important ingredients for improving the image quality of PET or SPECT further. High-quality anatomical information has been used and investigated for attenuation and scatter corrections, motion compensation, and noise reduction via post-reconstruction filtering and regularization in inverse problems. In this article, we will review works using anatomical information for molecular image reconstruction algorithms for better image quality by describing mathematical models, discussing sources of anatomical information for different cases, and showing some examples.

  13. Noise reduction in muon tomography for detecting high density objects

    International Nuclear Information System (INIS)

    Benettoni, M; Checchia, P; Cossutta, L; Furlan, M; Gonella, F; Pegoraro, M; Garola, A Rigoni; Ronchese, P; Vanini, S; Viesti, G; Bettella, G; Bonomi, G; Donzella, A; Subieta, M; Zenoni, A; Calvagno, G; Cortelazzo, G; Zanuttigh, P; Calvini, P; Squarcia, S

    2013-01-01

    The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

    Diaz-Ramirez, Victor H.; Kober, Vitaly

    2008-08-01

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

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

    DEFF Research Database (Denmark)

    Nadernejad, Ehsan; Forchhammer, Søren; Korhonen, Jari

    2011-01-01

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

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

    Science.gov (United States)

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

    2012-03-01

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

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

    Science.gov (United States)

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

    2017-05-01

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

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

    Science.gov (United States)

    Rabinowitz, Matthew (Inventor)

    2002-01-01

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

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

    Science.gov (United States)

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

    2018-01-07

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

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

    Institute of Scientific and Technical Information of China (English)

    Xiaogu ZHENG

    2009-01-01

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

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

    Directory of Open Access Journals (Sweden)

    GAN Yu

    2015-09-01

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

  3. The iterative shrinkage method for impulsive noise reduction from images

    International Nuclear Information System (INIS)

    Beygi, Sajjad; Kafashan, Mohammadmehdi; Bahrami, Hamid Reza; Mugler, Dale H

    2012-01-01

    In this paper, we present a novel scheme to compensate impulsive noise from images using the sparse shrinkage method. In this scheme, we assume the remaining noise after using a simple median filtering in place of corrupted pixels, found by boundary discriminative noise detection method, to be Gaussian additive noise. This assumption will later be verified by the means of simulation. Knowing that the pure image in the discrete wavelet transform (DWT) domain is a sparse vector, we define an optimization problem to minimize the l 0 -norm of the estimated image vector from the noisy one in the DWT domain. l 0 -norm makes the optimization problem a combinatorial optimization problem which is NP-hard to solve. To come up with a solution for our optimization problem, we convert the l 0 -norm problem to a continuous optimization problem which is then solved to find the estimated image with reduced noise. In the simulation and discussion part, the performance of our proposed method in reducing impulsive noise is compared to that of existing methods in the literature. We show that our proposed algorithm generally performs better in terms of both subjective and objective evaluations and is less complex. (paper)

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

    KAUST Repository

    Cannistraci, Carlo Vittorio

    2015-01-26

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

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

    KAUST Repository

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

    2015-01-01

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

  6. The effects of noise reduction technologies on the acceptance of background noise.

    Science.gov (United States)

    Lowery, Kristy Jones; Plyler, Patrick N

    2013-09-01

    Directional microphones (D-Mics) and digital noise reduction (DNR) algorithms are used in hearing aids to reduce the negative effects of background noise on performance. Directional microphones attenuate sounds arriving from anywhere other than the front of the listener while DNR attenuates sounds with physical characteristics of noise. Although both noise reduction technologies are currently available in hearing aids, it is unclear if the use of these technologies in isolation or together affects acceptance of noise and/or preference for the end user when used in various types of background noise. The purpose of the research was to determine the effects of D-Mic, DNR, or the combination of D-Mic and DNR on acceptance of noise and preference when listening in various types of background noise. An experimental study in which subjects were exposed to a repeated measures design was utilized. Thirty adult listeners with mild sloping to moderately severe sensorineural hearing loss participated (mean age 67 yr). Acceptable noise levels (ANLs) were obtained using no noise reduction technologies, D-Mic only, DNR only, and the combination of the two technologies (Combo) for three different background noises (single-talker speech, speech-shaped noise, and multitalker babble) for each listener. In addition, preference rankings of the noise reduction technologies were obtained within each background noise (1 = best, 3 = worst). ANL values were significantly better for each noise reduction technology than baseline; and benefit increased significantly from DNR to D-Mic to Combo. Listeners with higher (worse) baseline ANLs received more benefit from noise reduction technologies than listeners with lower (better) baseline ANLs. Neither ANL values nor ANL benefit values were significantly affected by background noise type; however, ANL benefit with D-Mic and Combo was similar when speech-like noise was present while ANL benefit was greatest for Combo when speech spectrum noise was

  7. Broadband transmission noise reduction of smart panels featuring piezoelectric shunt circuits and sound-absorbing material.

    Science.gov (United States)

    Kim, Jaehwan; Lee, Joong-Kuen

    2002-09-01

    The possibility of a broadband noise reduction of piezoelectric smart panels is experimentally studied. A piezoelectric smart panel is basically a plate structure on which piezoelectric patches with electrical shunt circuits are mounted and sound-absorbing material is bonded on the surface of the structure. Sound-absorbing material can absorb the sound transmitted at the midfrequency region effectively while the use of piezoelectric shunt damping can reduce the transmission at resonance frequencies of the panel structure. To be able to reduce the sound transmission at low panel resonance frequencies, piezoelectric damping using the measured electrical impedance model is adopted. A resonant shunt circuit for piezoelectric shunt damping is composed of resistor and inductor in series, and they are determined by maximizing the dissipated energy through the circuit. The transmitted noise-reduction performance of smart panels is tested in an acoustic tunnel. The tunnel is a square cross-sectional tube and a loudspeaker is mounted at one side of the tube as a sound source. Panels are mounted in the middle of the tunnel and the transmitted sound pressure across panels is measured. When an absorbing material is bonded on a single plate, a remarkable transmitted noise reduction in the midfrequency region is observed except for the fundamental resonance frequency of the plate. By enabling the piezoelectric shunt damping, noise reduction is achieved at the resonance frequency as well. Piezoelectric smart panels incorporating passive absorbing material and piezoelectric shunt damping is a promising technology for noise reduction over a broadband of frequencies.

  8. Noise Reduction Evaluation of Multi-Layered Viscoelastic Infinite Cylinder under Acoustical Wave Excitation

    Directory of Open Access Journals (Sweden)

    M.R. Mofakhami

    2008-01-01

    Full Text Available In this paper sound transmission through the multilayered viscoelastic air filled cylinders subjected to the incident acoustic wave is studied using the technique of separation of variables on the basis of linear three dimensional theory of elasticity. The effect of interior acoustic medium on the mode maps (frequency vs geometry and noise reduction is investigated. The effects of internal absorption and external moving medium on noise reduction are also evaluated. The dynamic viscoelastic properties of the structure are rigorously taken into account with a power law technique that models the viscoelastic damping of the cylinder. A parametric study is also performed for the two layered infinite cylinders to obtain the effect of viscoelastic layer characteristics such as thickness, material type and frequency dependency of viscoelastic properties on the noise reduction. It is shown that using constant and frequency dependent viscoelastic material with high loss factor leads to the uniform noise reduction in the frequency domain. It is also shown that the noise reduction obtained for constant viscoelastic material property is subjected to some errors in the low frequency range with respect to those obtained for the frequency dependent viscoelastic material.

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

    Directory of Open Access Journals (Sweden)

    Bin Sun

    2015-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Andrea Baiocchi

    2015-01-01

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

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

    Science.gov (United States)

    Wang, Zhaohui; Hardeberg, Jon Yngve

    2012-04-01

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

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

    International Nuclear Information System (INIS)

    Chen, Lin; Fan, Xiangtao; Du, Xiaoping

    2014-01-01

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

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

    Science.gov (United States)

    Lv, Zhaoshun; Wu, Hanping; Li, Junyu

    2016-10-01

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

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

    Science.gov (United States)

    Dinh, Hai; Adhami, Reza; Wang, Yi

    2015-07-01

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

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

    Institute of Scientific and Technical Information of China (English)

    杨海; 李威; 罗成名

    2015-01-01

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

  16. Acoustic performance design and optimal allocation of sound package in ship cabin noise reduction

    Directory of Open Access Journals (Sweden)

    YANG Deqing

    2017-08-01

    Full Text Available The sound package in noise reduction design of ship cabins has become the main approach for the future. The sound package is a specially designed acoustic component consisting of damping materials, absorption materials, sound isolation materials and base structural materials which can achieve the prescribed performance of noise reduction. Based on the Statistical Energy Analysis(SEAmethod, quick evaluation and design methods, and the optimal allocation theory of sound packages are investigated. The standard numerical acoustic performance evaluation model, sound package optimization design model and sound package optimal allocation model are presented. A genetic algorithm is applied to solve the presented optimization problems. Design examples demonstrate the validity and efficiency of the proposed models and solutions. The presented theory and methods benefit the standardization and programming of sound package design, and decrease noise reduction costs.

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

    DEFF Research Database (Denmark)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2018-02-06

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

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

    NARCIS (Netherlands)

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

    1999-01-01

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

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

    Science.gov (United States)

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

    2018-05-01

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

  1. Noise reduction in Arterial Spin Labeling based Functional Connectivity using nuisance variables

    Directory of Open Access Journals (Sweden)

    Kay Jann

    2016-08-01

    Full Text Available Arterial Spin Labeling (ASL perfusion image series have recently been utilized for functional connectivity (FC analysis in healthy volunteers and children with autism spectrum disorders (ASD. Noise reduction by using nuisance variables has been shown to be necessary to minimize potential confounding effects of head motion and physiological signals on BOLD based FC analysis. The purpose of the present study is to systematically evaluate the effectiveness of different noise reduction strategies using nuisance variables to improve perfusion based FC analysis in two cohorts of healthy adults using state of the art 3D background-suppressed (BS GRASE pseudo-continuous ASL (pCASL and dual-echo 2D-EPI pCASL sequences. Five different noise reduction strategies (NRS were performed in healthy volunteers to compare their performance. We then compared seed-based FC analysis using 3D BS GRASE pCASL in a cohort of 12 children with ASD (3f/9m, age 12.8±1.3y and 13 typically developing (TD children (1f/12m; age 13.9±3years in conjunction with noise reduction strategies. Regression of different combinations of nuisance variables affected FC analysis from a seed in the posterior cingulate cortex (PCC to other areas of the default mode network (DMN in both BOLD and pCASL data sets. Consistent with existing literature on BOLD-FC, we observed improved spatial specificity after physiological noise reduction and improved long-range connectivity using head movement related regressors. Furthermore, 3D BS GRASE pCASL shows much higher temporal SNR compared to dual-echo 2D-EPI pCASL and similar effects of noise reduction as those observed for BOLD. Seed-based FC analysis using 3D BS GRASE pCASL children with ASD and TD children showed that noise reduction including physiological and motion related signals as nuisance variables is crucial for identifying altered long-range connectivity from PCC to frontal brain areas associated with ASD. This is the first study that

  2. DFT-Domain Based Single-Microphone Noise Reduction for Speech Enhancement

    DEFF Research Database (Denmark)

    C. Hendriks, Richard; Gerkmann, Timo; Jensen, Jesper

    As speech processing devices like mobile phones, voice controlled devices, and hearing aids have increased in popularity, people expect them to work anywhere and at any time without user intervention. However, the presence of acoustical disturbances limits the use of these applications, degrades...... their performance, or causes the user difficulties in understanding the conversation or appreciating the device. A common way to reduce the effects of such disturbances is through the use of single-microphone noise reduction algorithms for speech enhancement. The field of single-microphone noise reduction...

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

    Directory of Open Access Journals (Sweden)

    Makowski Ryszard

    2014-06-01

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

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

    International Nuclear Information System (INIS)

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

    2008-01-01

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

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

    Science.gov (United States)

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

    2017-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Pavel Brandstetter

    2013-01-01

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

  7. Subspace-Based Noise Reduction for Speech Signals via Diagonal and Triangular Matrix Decompositions

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Søren Holdt

    2007-01-01

    We survey the definitions and use of rank-revealing matrix decompositions in single-channel noise reduction algorithms for speech signals. Our algorithms are based on the rank-reduction paradigm and, in particular, signal subspace techniques. The focus is on practical working algorithms, using both...... with working Matlab code and applications in speech processing....

  8. An Exceptional Purity of Sound: Noise Reduction Technology and the Inevitable Noise of Sound Recording

    NARCIS (Netherlands)

    Kromhout, M.

    2014-01-01

    The phenomenon of noise has resisted many attempts at framing it within a singular conceptual framework. Critically questioning the tendency to do so, this article asserts the complexities of different noise-phenomena by analysing a specific technology: technological noise reduction systems. Whereas

  9. Critical Low-Noise Technologies Being Developed for Engine Noise Reduction Systems Subproject

    Science.gov (United States)

    Grady, Joseph E.; Civinskas, Kestutis C.

    2004-01-01

    NASA's previous Advanced Subsonic Technology (AST) Noise Reduction Program delivered the initial technologies for meeting a 10-year goal of a 10-dB reduction in total aircraft system noise. Technology Readiness Levels achieved for the engine-noise-reduction technologies ranged from 4 (rig scale) to 6 (engine demonstration). The current Quiet Aircraft Technology (QAT) project is building on those AST accomplishments to achieve the additional noise reduction needed to meet the Aerospace Technology Enterprise's 10-year goal, again validated through a combination of laboratory rig and engine demonstration tests. In order to meet the Aerospace Technology Enterprise goal for future aircraft of a 50- reduction in the perceived noise level, reductions of 4 dB are needed in both fan and jet noise. The primary objectives of the Engine Noise Reduction Systems (ENRS) subproject are, therefore, to develop technologies to reduce both fan and jet noise by 4 dB, to demonstrate these technologies in engine tests, and to develop and experimentally validate Computational Aero Acoustics (CAA) computer codes that will improve our ability to predict engine noise.

  10. Objective Measures of Listening Effort: Effects of Background Noise and Noise Reduction

    Science.gov (United States)

    Sarampalis, Anastasios; Kalluri, Sridhar; Edwards, Brent; Hafter, Ervin

    2009-01-01

    Purpose: This work is aimed at addressing a seeming contradiction related to the use of noise-reduction (NR) algorithms in hearing aids. The problem is that although some listeners claim a subjective improvement from NR, it has not been shown to improve speech intelligibility, often even making it worse. Method: To address this, the hypothesis…

  11. Perceptual effects of noise reduction by time-frequency masking of noisy speech.

    Science.gov (United States)

    Brons, Inge; Houben, Rolph; Dreschler, Wouter A

    2012-10-01

    Time-frequency masking is a method for noise reduction that is based on the time-frequency representation of a speech in noise signal. Depending on the estimated signal-to-noise ratio (SNR), each time-frequency unit is either attenuated or not. A special type of a time-frequency mask is the ideal binary mask (IBM), which has access to the real SNR (ideal). The IBM either retains or removes each time-frequency unit (binary mask). The IBM provides large improvements in speech intelligibility and is a valuable tool for investigating how different factors influence intelligibility. This study extends the standard outcome measure (speech intelligibility) with additional perceptual measures relevant for noise reduction: listening effort, noise annoyance, speech naturalness, and overall preference. Four types of time-frequency masking were evaluated: the original IBM, a tempered version of the IBM (called ITM) which applies limited and non-binary attenuation, and non-ideal masking (also tempered) with two different types of noise-estimation algorithms. The results from ideal masking imply that there is a trade-off between intelligibility and sound quality, which depends on the attenuation strength. Additionally, the results for non-ideal masking suggest that subjective measures can show effects of noise reduction even if noise reduction does not lead to differences in intelligibility.

  12. Effect of digital noise reduction on the accuracy of endodontic file length determination

    International Nuclear Information System (INIS)

    Mehdizadeh, Mojdeh; Khademi, Abbas Ali; Shokraneh, Ali; Farhadi, Nastaran

    2013-01-01

    The aim of the present study was to evaluate the measurement accuracy of endodontic file length on periapical digital radiography after application of noise reduction digital enhancement. Thirty-five human single-rooted permanent teeth with canals measuring 20-24 mm in length were selected. ISO no.08 endodontic files were placed in the root canals of the teeth. The file lengths were measured with a digital caliper as the standard value. Standard periapical digital images were obtained using the Digora digital radiographic system and a dental X-ray unit. In order to produce the enhanced images, the noise reduction option was applied. Two blinded radiologists measured the file lengths on the original and enhanced images. The measurements were compared by repeated measures ANOVA and the Bonferroni test (a=0.05). Both the original and enhanced digital images provided significantly longer measurements compared with the standard value (P 0.05). Noise reduction digital enhancement did not influence the measurement accuracy of the length of the thin endodontic files on the digital periapical radiographs despite the fact that noise reduction could result in the elimination of fine details of the images.

  13. Two Methods of Mechanical Noise Reduction of Recorded Speech During Phonation in an MRI devic

    Czech Academy of Sciences Publication Activity Database

    Přibil, J.; Horáček, Jaromír; Horák, Petr

    2011-01-01

    Roč. 11, č. 3 (2011), s. 92-98 ISSN 1335-8871 R&D Projects: GA ČR GA102/09/0989 Institutional research plan: CEZ:AV0Z20760514; CEZ:AV0Z20670512 Keywords : speech processing * noise reduction * NMR imaging Subject RIV: BI - Acoustics Impact factor: 0.418, year: 2011

  14. Noise pollution of air compressor and its noise reduction procedures by using an enclosure

    Directory of Open Access Journals (Sweden)

    Farhad Forouharmajd

    2012-01-01

    Conclusions: An overall noise reduction by 25 dB with the use of mineral wool as an extra liner on the inside of the enclosure, suggests that the effectiveness of the enclosure can be increased by using such absorber materials.

  15. Noise reduction in binaural hearing aids : Analyzing the benefit over monaural systems

    NARCIS (Netherlands)

    Srinivasan, S.

    2008-01-01

    Binaural beamforming using signals from both left and right hearing aids offers greater potential for noise reduction than using signals from a single aid. However, wireless transmission of data imposes power constraints. Since most modern hearing aids have multiple microphones and are capable of

  16. A simulation study of harmonics regeneration in noise reduction for electric and acoustic stimulation.

    Science.gov (United States)

    Hu, Yi

    2010-05-01

    Recent research results show that combined electric and acoustic stimulation (EAS) significantly improves speech recognition in noise, and it is generally established that access to the improved F0 representation of target speech, along with the glimpse cues, provide the EAS benefits. Under noisy listening conditions, noise signals degrade these important cues by introducing undesired temporal-frequency components and corrupting harmonics structure. In this study, the potential of combining noise reduction and harmonics regeneration techniques was investigated to further improve speech intelligibility in noise by providing improved beneficial cues for EAS. Three hypotheses were tested: (1) noise reduction methods can improve speech intelligibility in noise for EAS; (2) harmonics regeneration after noise reduction can further improve speech intelligibility in noise for EAS; and (3) harmonics sideband constraints in frequency domain (or equivalently, amplitude modulation in temporal domain), even deterministic ones, can provide additional benefits. Test results demonstrate that combining noise reduction and harmonics regeneration can significantly improve speech recognition in noise for EAS, and it is also beneficial to preserve the harmonics sidebands under adverse listening conditions. This finding warrants further work into the development of algorithms that regenerate harmonics and the related sidebands for EAS processing under noisy conditions.

  17. A novel modulation scheme for noise reduction in analog fiber optic links

    NARCIS (Netherlands)

    Marpaung, D.A.I.; Roeloffzen, C.G.H.; van Etten, Wim

    2006-01-01

    A novel noise reduction scheme called Balanced Modulation and Detection (BMD) is proposed. In this scheme, the modulating RF signal is half-wave rectified in the optical domain, eliminating the DC optical power resulting from pre-biasing of the optical source. A link model employing this scheme has

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

    Science.gov (United States)

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

    2018-04-01

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

  19. 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.

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

    Directory of Open Access Journals (Sweden)

    Wang Liang

    2018-01-01

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

  1. SU-F-18C-15: Model-Based Multiscale Noise Reduction On Low Dose Cone Beam Projection

    International Nuclear Information System (INIS)

    Yao, W; Farr, J

    2014-01-01

    Purpose: To improve image quality of low dose cone beam CT for patient positioning in radiation therapy. Methods: In low dose cone beam CT (CBCT) imaging systems, Poisson process governs the randomness of photon fluence at x-ray source and the detector because of the independent binomial process of photon absorption in medium. On a CBCT projection, the variance of fluence consists of the variance of noiseless imaging structure and that of Poisson noise, which is proportional to the mean (noiseless) of the fluence at the detector. This requires multiscale filters to smoothen noise while keeping the structure information of the imaged object. We used a mathematical model of Poisson process to design multiscale filters and established the balance of noise correction and structure blurring. The algorithm was checked with low dose kilo-voltage CBCT projections acquired from a Varian OBI system. Results: From the investigation of low dose CBCT of a Catphan phantom and patients, it showed that our model-based multiscale technique could efficiently reduce noise and meanwhile keep the fine structure of the imaged object. After the image processing, the number of visible line pairs in Catphan phantom scanned with 4 ms pulse time was similar to that scanned with 32 ms, and soft tissue structure from simulated 4 ms patient head-and-neck images was also comparable with scanned 20 ms ones. Compared with fixed-scale technique, the image quality from multiscale one was improved. Conclusion: Use of projection-specific multiscale filters can reach better balance on noise reduction and structure information loss. The image quality of low dose CBCT can be improved by using multiscale filters

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

    Directory of Open Access Journals (Sweden)

    Xin Zhang

    2014-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Xia Zheng

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Manish Jaiswal

    2015-03-01

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

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

    International Nuclear Information System (INIS)

    Zheng Hong; Liu Xu; Wei Min

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Bizhong Xia

    2015-06-01

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

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

    Directory of Open Access Journals (Sweden)

    Fikret Caliskan

    2014-01-01

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

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

    Science.gov (United States)

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

    2018-06-01

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

  9. Utilisation of polyurethane composit with 50% composition of roystonea regia fiber as noise reduction panel on car hood

    Science.gov (United States)

    Ikhwansyah; Mulia; Gunawan, S.; Lubis, R. D. W.

    2018-02-01

    The objective is to get the characteristics of noise reduction, noise reduction level, variety of measurement spaces, and knowing the process in making acoustic material of natural fiber becomes noise reduction on a car hood. The process of making noise reduction material used casting method and pressed by using molded press. The composition of noise reduction material consist of 50% roystonea regia by 32 mesh and 50% combined by gypsum and polyurethane. The result shows that the average result of noise reduction at X1- side is 5,7% and X2- side is 3,9%, X1+ side is 0,9% and X2+ side is 6,2%, Z1- side is 8,9% and Z2- side is 10,1%, Z1+ side is 9,7% and Z2+ side is 10,01%. The main conclusion of the study shows that a noise reduction which made of roystonea regia with 32 mesh mixed by matrix of polyurethane and gypsum is appropriate for noise reduction on car hood.

  10. Detection threshold for sound distortion resulting from noise reduction in normal-hearing and hearing-impaired listeners

    NARCIS (Netherlands)

    Brons, Inge; Dreschler, Wouter A.; Houben, Rolph

    2014-01-01

    Hearing-aid noise reduction should reduce background noise, but not disturb the target speech. This objective is difficult because noise reduction suffers from a trade-off between the amount of noise removed and signal distortion. It is unknown if this important trade-off differs between

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  12. Noise Reduction of Ocean-Bottom Pressure Data Toward Real-Time Tsunami Forecasting

    Science.gov (United States)

    Tsushima, H.; Hino, R.

    2008-12-01

    We discuss a method of noise reduction of ocean-bottom pressure data to be fed into the near-field tsunami forecasting scheme proposed by Tsushima et al. [2008a]. In their scheme, the pressure data is processed in real time as follows: (1) removing ocean tide components by subtracting the sea-level variation computed from a theoretical tide model, (2) applying low-pass digital filter to remove high-frequency fluctuation due to seismic waves, and (3) removing DC-offset and linear-trend component to determine a baseline of relative sea level. However, it turns out this simple method is not always successful in extracting tsunami waveforms from the data, when the observed amplitude is ~1cm. For disaster mitigation, accurate forecasting of small tsunamis is important as well as large tsunamis. Since small tsunami events occur frequently, successful tsunami forecasting of those events are critical to obtain public reliance upon tsunami warnings. As a test case, we applied the data-processing described above to the bottom pressure records containing tsunami with amplitude less than 1 cm which was generated by the 2003 Off-Fukushima earthquake occurring in the Japan Trench subduction zone. The observed pressure variation due to the ocean tide is well explained by the calculated tide signals from NAO99Jb model [Matsumoto et al., 2000]. However, the tide components estimated by BAYTAP-G [Tamura et al., 1991] from the pressure data is more appropriate for predicting and removing the ocean tide signals. In the pressure data after removing the tide variations, there remain pressure fluctuations with frequencies ranging from about 0.1 to 1 mHz and with amplitudes around ~10 cm. These fluctuations distort the estimation of zero-level and linear trend to define relative sea-level variation, which is treated as tsunami waveform in the subsequent analysis. Since the linear trend is estimated from the data prior to the origin time of the earthquake, an artificial linear trend is

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

    Science.gov (United States)

    Ju, Hongbin

    2015-10-01

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

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

    Directory of Open Access Journals (Sweden)

    Tongyang LI

    2018-05-01

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

  15. Helicopter Non-Unique Trim Strategies for Blade-Vortex Interaction (BVI) Noise Reduction

    Science.gov (United States)

    Malpica, Carlos; Greenwood, Eric; Sim, Ben W.

    2016-01-01

    An acoustics parametric analysis of the effect of fuselage drag and pitching moment on the Blade-Vortex Interaction (BVI) noise radiated by a medium lift helicopter (S-70UH-60) in a descending flight condition was conducted. The comprehensive analysis CAMRAD II was used for the calculation of vehicle trim, wake geometry and integrated air loads on the blade. The acoustics prediction code PSU-WOPWOP was used for calculating acoustic pressure signatures for a hemispherical grid centered at the hub. This paper revisits the concept of the X-force controller for BVI noise reduction, and investigates its effectiveness on an S-70 helicopter. The analysis showed that further BVI noise reductions were achievable by controlling the fuselage pitching moment. Reductions in excess of 6 dB of the peak BVI noise radiated towards the ground were demonstrated by compounding the effect of airframe drag and pitching moment simultaneously.

  16. Joint models for noise annoyance and willingness to pay for road noise reduction

    DEFF Research Database (Denmark)

    Fosgerau, Mogens; Bue Bjørner, Thomas

    2006-01-01

    Recent contingent valuation (CV) studies of the willingness to pay (WTP) for road noise reduction have used stated annoyance as an independent variable. We argue that this may be inappropriate due to potential endogeneity bias. Instead, an alternative model is proposed that treats both WTP...... and annoyance as endogenous variables in a simultaneous equation model as a combination of a linear regression with an ordered probit with correlated error terms and possibly common parameters. Thus, information on stated annoyance is utilised to estimate WTP with increased efficiency. Application of the model...... to a dataset from Copenhagen indicates a potential for improving the precision of the estimate of WTP for noise reduction with CV data....

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

    Directory of Open Access Journals (Sweden)

    Yazhe Tang

    2015-01-01

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

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

    Directory of Open Access Journals (Sweden)

    HAO Yushi

    2018-04-01

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

  19. Experimental identification of noise reduction properties of honeycomb panels using a small cabin

    OpenAIRE

    D'Ortona, Vittorio; Vivolo, Marianna; Pluymers, Bert; Vandepitte, Dirk; Desmet, Wim

    2013-01-01

    A procedure to identify the noise reduction properties of panels by means of a single cabin test setup is discussed. The complexity of the sound pressure field that builds up in the acoustic volume requires the support of advanced numerical techniques allowing for the evaluation of noise and vibration performances. Numerical models are used to predict the structural dynamics and the vibro-acoustic behaviour of the tested panel. Both Finite Element and Boundary Element simulations are validate...

  20. A combined noise reduction and torque enhancement system for an I. C. engine

    Energy Technology Data Exchange (ETDEWEB)

    Hutchins, S.M.; Stothers, Ian; McDougall, N.M.

    1993-02-10

    The present invention provides a combined noise reduction and torque enhancement system for an internal combustion engine. It comprises means for periodically constricting or occluding the exhaust and/or inlet at least one frequency related to the rotation frequency of the engine. In a preferred embodiment of the invention, the constriction means comprises a plug member arranged to be oscillated in an aperture along the axis of flow of gas in the exhaust and/or inlet. (author)

  1. Application of a TiO2 nanocomposite in earplugs, a case study of noise reduction.

    Science.gov (United States)

    Ibrahimi Ghavamabadi, Leila; Fouladi Dehaghi, Behzad; Hesampour, Morteza; Ahmadi Angali, Kambiz

    2018-03-13

    Use of hearing protection devices (HPDs) has become necessary when other control measures cannot reduce noise to a safe and standard level. In most countries, more effective hearing protection devices are in demand. The aim of this study was to examine the effects of titanium dioxide (TiO 2 ) nanoparticles on noise reduction efficiency in a polyvinyl chloride (PVC) earplug. An S-60 type PVC polymer as main matrix and TiO 2 with 30 nm size were used. PVC/TiO 2 nanocomposite was mixed at a temperature of 160 °C and 40 rounds per minute (rpm) and the samples were prepared with 0, 0.2 and 0.5 wt% of TiO 2 nanoparticle concentrations. Earplug samples with PVC/TiO 2 (0.2, 0.5 wt%) nanoparticles, when compared with raw earplugs, showed almost equal noise attenuation at low frequencies (500- 125 Hz). However, at high frequencies (2-8 kHz), the power of noise reduction of earplugs containing TiO 2 nanoparticles was significantly increased. The results of the present study showed that samples containing nanoparticles of TiO 2 had more noticeable noise reduction abilities at higher frequencies in comparison with samples without the nanoparticles.

  2. Blade-Mounted Flap Control for BVI Noise Reduction Proof-of-Concept Test

    Science.gov (United States)

    Dawson, Seth; Hassan, Ahmed; Straub, Friedrich; Tadghighi, Hormoz

    1995-01-01

    This report describes a wind tunnel test of the McDonnell Douglas Helicopter Systems (MDHS) Active Flap Model Rotor at the NASA Langley 14- by 22-Foot Subsonic Tunnel. The test demonstrated that BVI noise reductions and vibration reductions were possible with the use of an active flap. Aerodynamic results supported the acoustic data trends, showing a reduction in the strength of the tip vortex with the deflection of the flap. Acoustic results showed that the flap deployment, depending on the peak deflection angle and azimuthal shift in its deployment schedule, can produce BVI noise reductions as much as 6 dB on the advancing and retreating sides. The noise reduction was accompanied by an increase in low frequency harmonic noise and high frequency broadband noise. A brief assessment of the effect of the flap on vibration showed that significant reductions were possible. The greatest vibration reductions (as much as 76%) were found in the four per rev pitching moment at the hub. Performance improvement cam results were inconclusive, as the improvements were predicted to be smaller than the resolution of the rotor balance.

  3. Research on Noise Reduction of Reed Valves of a Hermetic Refrigerator Compressor

    Science.gov (United States)

    He, Zhilong; Chen, Qian; Li, Dantong; Wang, Ju; Xia, Pu; Wang, Tao

    2017-08-01

    The noise level of the refrigerator compressor has received more and more attention in recent years. As the key component of a compressor, reed valve is its main noise source. In this paper, a new noise reduction technology of coating on reed valve surface is proposed and verified by experiments. Firstly, the reed valves were coated, and their surface characteristics were checked. Then, the refrigerator compressor p-V diagram test was carried out to investigate the influence of doped diamond-like carbon (DLC) coating on power consumption. Finally, the noise test rig for the refrigerator compressor was set up. Based on the standard test method, noise spectrum was measured in a semi-anechoic room under standard working condition. Research results showed that the compressor noise was significantly reduced by 1.8dB (A) after coating. Moreover, the effect of aerodynamic noise reduction at suction side is better than that at discharge side. However, the influence of the film thickness on noise reduction value is little. The COP was reduced by 0.6% as compared to the compressor with uncoated reed valves.

  4. Optimization of the poro-serrated trailing edges for airfoil broadband noise reduction.

    Science.gov (United States)

    Chong, Tze Pei; Dubois, Elisa

    2016-08-01

    This paper reports an aeroacoustic investigation of a NACA0012 airfoil with a number of poro-serrated trailing edge devices that contain porous materials of various air flow resistances at the gaps between adjacent members of the serrated-sawtooth trailing edge. The main objective of this work is to determine whether multiple-mechanisms on the broadband noise reduction can co-exist on a poro-serrated trailing edge. When the sawtooth gaps are filled with porous material of low-flow resistivity, the vortex shedding tone at low-frequency could not be completely suppressed at high-velocity, but a reasonably good broadband noise reduction can be achieved at high-frequency. When the sawtooth gaps are filled with porous material of very high-flow resistivity, no vortex shedding tone is present, but the serration effect on the broadband noise reduction becomes less effective. An optimal choice of the flow resistivity for a poro-serrated configuration has been identified, where it can surpass the conventional serrated trailing edge of the same geometry by achieving a further 1.5 dB reduction in the broadband noise while completely suppressing the vortex shedding tone. A weakened turbulent boundary layer noise scattering at the poro-serrated trailing edge is reflected by the lower-turbulence intensity at the near wake centreline across the whole spanwise wavelength of the sawtooth.

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Directory of Open Access Journals (Sweden)

    Noor M. Khan

    2017-01-01

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

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

    Science.gov (United States)

    Leifer, Mark Curtis

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

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

    Science.gov (United States)

    Chen, Xiyuan; Wang, Xiying; Xu, Yuan

    2014-12-09

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

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

    Directory of Open Access Journals (Sweden)

    A. Konstantaras

    2006-01-01

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

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

    Science.gov (United States)

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

    2017-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-01-01

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

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

    Science.gov (United States)

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

    2017-01-14

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

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

    International Nuclear Information System (INIS)

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

    2001-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Hairong Chu

    2017-01-01

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

  15. Noise Reduction planar bone imaging nuclear medicine with the use of wavelet transform: an assessment of its quality

    International Nuclear Information System (INIS)

    Casas Cardoso, Maria del Carmen; Perez Diaz, Marlen; Casas Cardoso, Gladis; Lorenzo Ginori, Juan; Paz Viera, Juan Enrique; Roque Diaz, Reinaldo; Cardenas Barreras, Julian

    2009-01-01

    Diagnostic imaging of Nuclear Medicine (MN), is highly used in Oncology, as it constitutes a noninvasive technique that allows early detection of tumors and assessment of therapeutic response of patients under treatment. However, particularly planar scintigraphy images, can be prone to problems of detectability of small lesions, because they are contaminated with noise, a phenomenon which is accentuated by the inability to increase the dose of the radiopharmaceutical or time acquisition of images of the patient over 'certain levels'. The aim of this work is to improve the detectability of tumors of bone. We describe an algorithm for random noise reduction using the wavelet transform (TW). The quality of the resulting images are evaluated through quantitative metrics such as Signal to Noise Ratio (SNR), the Mean Square Error (NMSEA) and Structural Similarity Index (SSIM). It also includes a subjective assessment of image quality by expert criteria, using a variant of the methodology FROC (Free-Response ROC). It was found that some of the filters designed in the wavelet domain, significantly improve the quality of planar bone imaging in terms of increased signal to noise ratio without implying notable structural distortions, which facilitates clinical diagnosis. (author)

  16. Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

    Science.gov (United States)

    Yang, Sejung; Lee, Byung-Uk

    2015-01-01

    In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach. PMID:26352138

  17. Noise Reduction in Arterial Spin Labeling Based Functional Connectivity Using Nuisance Variables.

    Science.gov (United States)

    Jann, Kay; Smith, Robert X; Rios Piedra, Edgar A; Dapretto, Mirella; Wang, Danny J J

    2016-01-01

    Arterial Spin Labeling (ASL) perfusion image series have recently been utilized for functional connectivity (FC) analysis in healthy volunteers and children with autism spectrum disorders (ASD). Noise reduction by using nuisance variables has been shown to be necessary to minimize potential confounding effects of head motion and physiological signals on BOLD based FC analysis. The purpose of the present study is to systematically evaluate the effectiveness of different noise reduction strategies (NRS) using nuisance variables to improve perfusion based FC analysis in two cohorts of healthy adults using state of the art 3D background-suppressed (BS) GRASE pseudo-continuous ASL (pCASL) and dual-echo 2D-EPI pCASL sequences. Five different NRS were performed in healthy volunteers to compare their performance. We then compared seed-based FC analysis using 3D BS GRASE pCASL in a cohort of 12 children with ASD (3f/9m, age 12.8 ± 1.3 years) and 13 typically developing (TD) children (1f/12m; age 13.9 ± 3 years) in conjunction with NRS. Regression of different combinations of nuisance variables affected FC analysis from a seed in the posterior cingulate cortex (PCC) to other areas of the default mode network (DMN) in both BOLD and pCASL data sets. Consistent with existing literature on BOLD-FC, we observed improved spatial specificity after physiological noise reduction and improved long-range connectivity using head movement related regressors. Furthermore, 3D BS GRASE pCASL shows much higher temporal SNR compared to dual-echo 2D-EPI pCASL and similar effects of noise reduction as those observed for BOLD. Seed-based FC analysis using 3D BS GRASE pCASL in children with ASD and TD children showed that noise reduction including physiological and motion related signals as nuisance variables is crucial for identifying altered long-range connectivity from PCC to frontal brain areas associated with ASD. This is the first study that systematically evaluated the effects of

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

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

  19. Two-dimensional filtering of SPECT images using the Metz and Wiener filters

    International Nuclear Information System (INIS)

    King, M.A.; Schwinger, R.B.; Penney, B.C.; Doherty, P.W.

    1984-01-01

    Presently, single photon emission computed tomographic (SPECT) images are usually reconstructed by arbitrarily selecting a one-dimensional ''window'' function for use in reconstruction. A better method would be to automatically choose among a family of two-dimensional image restoration filters in such a way as to produce ''optimum'' image quality. Two-dimensional image processing techniques offer the advantages of a larger statistical sampling of the data for better noise reduction, and two-dimensional image deconvolution to correct for blurring during acquisition. An investigation of two such ''optimal'' digital image restoration techniques (the count-dependent Metz filter and the Wiener filter) was made. They were applied both as two-dimensional ''window'' functions for preprocessing SPECT images, and for filtering reconstructed images. Their performance was compared by measuring image contrast and per cent fractional standard deviation (% FSD) in multiple-acquisitions of the Jaszczak SPECT phantom at two different count levels. A statistically significant increase in image contrast and decrease in % FSD was observed with these techniques when compared to the results of reconstruction with a ramp filter. The adaptability of the techniques was manifested in a lesser % reduction in % FSD at the high count level coupled with a greater enhancement in image contrast. Using an array processor, processing time was 0.2 sec per image for the Metz filter and 3 sec for the Wiener filter. It is concluded that two-dimensional digital image restoration with these techniques can produce a significant increase in SPECT image quality

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

    Directory of Open Access Journals (Sweden)

    Biçer Cenker

    2016-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Pau G

    2006-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Lianghong Wu

    2011-08-01

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

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

    Science.gov (United States)

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

    2002-04-01

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

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

    International Nuclear Information System (INIS)

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

    2002-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2002-04-07

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

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

    CERN Document Server

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

    2002-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Jaakko T. Astola

    2005-05-01

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

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

    Science.gov (United States)

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

    2018-03-07

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

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

    KAUST Repository

    Song, Hajoon

    2010-07-01

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

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

    Science.gov (United States)

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

    2015-10-23

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

  11. Reducing Radiation Dose in Coronary Angiography and Angioplasty Using Image Noise Reduction Technology.

    Science.gov (United States)

    Kastrati, Mirlind; Langenbrink, Lukas; Piatkowski, Michal; Michaelsen, Jochen; Reimann, Doris; Hoffmann, Rainer

    2016-08-01

    This study sought to quantitatively evaluate the reduction of radiation dose in coronary angiography and angioplasty with the use of image noise reduction technology in a routine clinical setting. Radiation dose data from consecutive 605 coronary procedures (397 consecutive coronary angiograms and 208 consecutive coronary interventions) performed from October 2014 to April 2015 on a coronary angiography system with noise reduction technology (Allura Clarity IQ) were collected. For comparison, radiation dose data from consecutive 695 coronary procedures (435 coronary angiograms and 260 coronary interventions) performed on a conventional coronary angiography system from October 2013 to April 2014 were evaluated. Patient radiation dosage was evaluated based on the cumulative dose area product. Operators and operator practice did not change between the 2 evaluated periods. Patient characteristics were collected to evaluate similarity of patient groups. Image quality was evaluated on a 5-grade scale in 30 patients of each group. There were no significant differences between the 2 evaluated groups in gender, age, weight, and fluoroscopy time (6.8 ± 6.1 vs 6.9 ± 6.3 minutes, not significant). The dose area product was reduced from 3195 ± 2359 to 983 ± 972 cGycm(2) (65%, p technology. Image quality was graded as similar between the evaluated systems (4.0 ± 0.7 vs 4.2 ± 0.6, not significant). In conclusion, a new x-ray technology with image noise reduction algorithm provides a substantial reduction in radiation exposure without the need to prolong the procedure or fluoroscopy time. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Statistical CT noise reduction with multiscale decomposition and penalized weighted least squares in the projection domain

    International Nuclear Information System (INIS)

    Tang Shaojie; Tang Xiangyang

    2012-01-01

    Purposes: The suppression of noise in x-ray computed tomography (CT) imaging is of clinical relevance for diagnostic image quality and the potential for radiation dose saving. Toward this purpose, statistical noise reduction methods in either the image or projection domain have been proposed, which employ a multiscale decomposition to enhance the performance of noise suppression while maintaining image sharpness. Recognizing the advantages of noise suppression in the projection domain, the authors propose a projection domain multiscale penalized weighted least squares (PWLS) method, in which the angular sampling rate is explicitly taken into consideration to account for the possible variation of interview sampling rate in advanced clinical or preclinical applications. Methods: The projection domain multiscale PWLS method is derived by converting an isotropic diffusion partial differential equation in the image domain into the projection domain, wherein a multiscale decomposition is carried out. With adoption of the Markov random field or soft thresholding objective function, the projection domain multiscale PWLS method deals with noise at each scale. To compensate for the degradation in image sharpness caused by the projection domain multiscale PWLS method, an edge enhancement is carried out following the noise reduction. The performance of the proposed method is experimentally evaluated and verified using the projection data simulated by computer and acquired by a CT scanner. Results: The preliminary results show that the proposed projection domain multiscale PWLS method outperforms the projection domain single-scale PWLS method and the image domain multiscale anisotropic diffusion method in noise reduction. In addition, the proposed method can preserve image sharpness very well while the occurrence of “salt-and-pepper” noise and mosaic artifacts can be avoided. Conclusions: Since the interview sampling rate is taken into account in the projection domain

  13. Noise reduction techniques used on the high power klystron modulators at Argonne National Laboratory

    International Nuclear Information System (INIS)

    Russell, T.J.

    1993-01-01

    The modulators used in the Advanced Photon Source at Argonne National Laboratory have been redesigned with an emphasis on electrical noise reduction. Since the modulators are 100 MW modulators with <700 ns rise time, electrical noise can be coupled very easily to other electronic equipment in the area. This paper will detail the efforts made to reduce noise coupled to surrounding equipment. Shielding and sound grounding techniques accomplished the goal of drastically reducing the noise induced in surrounding equipment. The approach used in grounding and shielding will be discussed, and data will be presented comparing earlier designs to the improved design

  14. Railway noise reduction by the application of CHFC material on the rail

    Directory of Open Access Journals (Sweden)

    Brigita ALTENBAHER

    2015-06-01

    Full Text Available Traffic is the most widespread source of environmental noise. Railway noise has become increasingly common in urban areas in the past few decades. Therefore environmental requirements for railway operations regarding noise are becoming very strict and will become even tighter in future. In the present paper we present actual track-based field test performed on Slovenian Railways. The significant noise reduction (up to 30dBA was achieved by the application of CHFC material on the rail using CL-E1 top anti noise system.

  15. Image noise reduction technology reduces radiation in a radial-first cardiac catheterization laboratory

    Energy Technology Data Exchange (ETDEWEB)

    Gunja, Ateka; Pandey, Yagya [Department of Veterans Affairs, Jesse Brown VA Medical Center, Chicago, IL (United States); Division of Cardiology, Department of Medicine, University of Illinois at Chicago, Chicago, IL (United States); Xie, Hui [Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL (United States); Faculty of Health Sciences, Simon Fraser University, Burnaby, BC (Canada); Wolska, Beata M. [Department of Physiology and Biophysics, Center for Cardiovascular Research, University of Illinois at Chicago, Chicago, IL (United States); Shroff, Adhir R.; Ardati, Amer K. [Department of Veterans Affairs, Jesse Brown VA Medical Center, Chicago, IL (United States); Division of Cardiology, Department of Medicine, University of Illinois at Chicago, Chicago, IL (United States); Vidovich, Mladen I., E-mail: miv@uic.edu [Department of Veterans Affairs, Jesse Brown VA Medical Center, Chicago, IL (United States); Division of Cardiology, Department of Medicine, University of Illinois at Chicago, Chicago, IL (United States)

    2017-04-15

    Background: Transradial coronary angiography (TRA) has been associated with increased radiation doses. We hypothesized that contemporary image noise reduction technology would reduce radiation doses in the cardiac catheterization laboratory in a typical clinical setting. Methods and results: We performed a single-center, retrospective analysis of 400 consecutive patients who underwent diagnostic and interventional cardiac catheterizations in a predominantly TRA laboratory with traditional fluoroscopy (N = 200) and a new image noise reduction fluoroscopy system (N = 200). The primary endpoint was radiation dose (mGy cm{sup 2}). Secondary endpoints were contrast dose, fluoroscopy times, number of cineangiograms, and radiation dose by operator between the two study periods. Radiation was reduced by 44.7% between the old and new cardiac catheterization laboratory (75.8 mGy cm{sup 2} ± 74.0 vs. 41.9 mGy cm{sup 2} ± 40.7, p < 0.0001). Radiation was reduced for both diagnostic procedures (45.9%, p < 0.0001) and interventional procedures (37.7%, p < 0.0001). There was no statistically significant difference in radiation dose between individual operators (p = 0.84). In multivariate analysis, radiation dose remained significantly decreased with the use of the new system (p < 0.0001) and was associated with weight (p < 0.0001), previous coronary artery bypass grafting (p < 0.0007) and greater than 3 stents used (p < 0.0004). TRA was used in 90% of all cases in both periods. Compared with a transfemoral approach (TFA), TRA was not associated with higher radiation doses (p = 0.20). Conclusions: Image noise reduction technology significantly reduces radiation dose in a contemporary radial-first cardiac catheterization clinical practice. - Highlights: • Radial arterial access has been associated with higher doses compared to femoral access. • In a radial-first cardiac catheterization laboratory (90% radial) we examined radiation doses reduction with a contemporary image

  16. Ensemble empirical mode decomposition based fluorescence spectral noise reduction for low concentration PAHs

    Science.gov (United States)

    Wang, Shu-tao; Yang, Xue-ying; Kong, De-ming; Wang, Yu-tian

    2017-11-01

    A new noise reduction method based on ensemble empirical mode decomposition (EEMD) is proposed to improve the detection effect for fluorescence spectra. Polycyclic aromatic hydrocarbons (PAHs) pollutants, as a kind of important current environmental pollution source, are highly oncogenic. Using the fluorescence spectroscopy method, the PAHs pollutants can be detected. However, instrument will produce noise in the experiment. Weak fluorescent signals can be affected by noise, so we propose a way to denoise and improve the detection effect. Firstly, we use fluorescence spectrometer to detect PAHs to obtain fluorescence spectra. Subsequently, noises are reduced by EEMD algorithm. Finally, the experiment results show the proposed method is feasible.

  17. Image noise reduction technology reduces radiation in a radial-first cardiac catheterization laboratory

    International Nuclear Information System (INIS)

    Gunja, Ateka; Pandey, Yagya; Xie, Hui; Wolska, Beata M.; Shroff, Adhir R.; Ardati, Amer K.; Vidovich, Mladen I.

    2017-01-01

    Background: Transradial coronary angiography (TRA) has been associated with increased radiation doses. We hypothesized that contemporary image noise reduction technology would reduce radiation doses in the cardiac catheterization laboratory in a typical clinical setting. Methods and results: We performed a single-center, retrospective analysis of 400 consecutive patients who underwent diagnostic and interventional cardiac catheterizations in a predominantly TRA laboratory with traditional fluoroscopy (N = 200) and a new image noise reduction fluoroscopy system (N = 200). The primary endpoint was radiation dose (mGy cm"2). Secondary endpoints were contrast dose, fluoroscopy times, number of cineangiograms, and radiation dose by operator between the two study periods. Radiation was reduced by 44.7% between the old and new cardiac catheterization laboratory (75.8 mGy cm"2 ± 74.0 vs. 41.9 mGy cm"2 ± 40.7, p < 0.0001). Radiation was reduced for both diagnostic procedures (45.9%, p < 0.0001) and interventional procedures (37.7%, p < 0.0001). There was no statistically significant difference in radiation dose between individual operators (p = 0.84). In multivariate analysis, radiation dose remained significantly decreased with the use of the new system (p < 0.0001) and was associated with weight (p < 0.0001), previous coronary artery bypass grafting (p < 0.0007) and greater than 3 stents used (p < 0.0004). TRA was used in 90% of all cases in both periods. Compared with a transfemoral approach (TFA), TRA was not associated with higher radiation doses (p = 0.20). Conclusions: Image noise reduction technology significantly reduces radiation dose in a contemporary radial-first cardiac catheterization clinical practice. - Highlights: • Radial arterial access has been associated with higher doses compared to femoral access. • In a radial-first cardiac catheterization laboratory (90% radial) we examined radiation doses reduction with a contemporary image-noise compared to

  18. Electrocardiogram (ECG Signal Modeling and Noise Reduction Using Hopfield Neural Networks

    Directory of Open Access Journals (Sweden)

    F. Bagheri

    2013-02-01

    Full Text Available The Electrocardiogram (ECG signal is one of the diagnosing approaches to detect heart disease. In this study the Hopfield Neural Network (HNN is applied and proposed for ECG signal modeling and noise reduction. The Hopfield Neural Network (HNN is a recurrent neural network that stores the information in a dynamic stable pattern. This algorithm retrieves a pattern stored in memory in response to the presentation of an incomplete or noisy version of that pattern. Computer simulation results show that this method can successfully model the ECG signal and remove high-frequency noise.

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

    Directory of Open Access Journals (Sweden)

    Ming-Hung Chen

    2015-01-01

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

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

    Science.gov (United States)

    Chen, Ming-Hung

    2015-01-01

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

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

    International Nuclear Information System (INIS)

    Ye, Min; Guo, Hui; Cao, Binggang

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

  3. Low-Frequency Noise Reduction by Earmuffs with Flax Fibre-Reinforced Polypropylene Ear Cups

    Directory of Open Access Journals (Sweden)

    Linus Yinn Leng Ang

    2018-01-01

    Full Text Available Soldiers and supporting engineers are frequently exposed to high low-frequency (<500 Hz cabin noise in military vehicles. Despite the use of commercial hearing protection devices, the risk of auditory damage is still imminent because the devices may not be optimally customised for such applications. This study considers flax fibre-reinforced polypropylene (Flax-PP as an alternative to the material selection for the ear cups of commercial earmuffs, which are typically made of acrylonitrile butadiene styrene (ABS. Different weaving configurations (woven and nonwoven and various noise environments (pink noise, cabin booming noise, and firing noise were considered to investigate the feasibility of the proposed composite earmuffs for low-frequency noise reduction. The remaining assembly components of the earmuff were kept consistent with those of a commercial earmuff, which served as a benchmark for results comparison. In contrast to the commercial earmuff, the composite earmuffs were shown to be better in mitigating low-frequency noise by up to 16.6 dB, while compromising midfrequency acoustical performance. Consequently, the proposed composite earmuffs may be an alternative for low-frequency noise reduction in vehicle cabins, at airports, and at construction sites involving heavy machineries.

  4. The correlation study of parallel feature extractor and noise reduction approaches

    Energy Technology Data Exchange (ETDEWEB)

    Dewi, Deshinta Arrova; Sundararajan, Elankovan; Prabuwono, Anton Satria [Industrial Computing Research Group, Centre for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, 43600 Bangi (Malaysia)

    2015-05-15

    This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation, image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE.

  5. The correlation study of parallel feature extractor and noise reduction approaches

    International Nuclear Information System (INIS)

    Dewi, Deshinta Arrova; Sundararajan, Elankovan; Prabuwono, Anton Satria

    2015-01-01

    This paper presents literature reviews that show variety of techniques to develop parallel feature extractor and finding its correlation with noise reduction approaches for low light intensity images. Low light intensity images are normally displayed as darker images and low contrast. Without proper handling techniques, those images regularly become evidences of misperception of objects and textures, the incapability to section them. The visual illusions regularly clues to disorientation, user fatigue, poor detection and classification performance of humans and computer algorithms. Noise reduction approaches (NR) therefore is an essential step for other image processing steps such as edge detection, image segmentation, image compression, etc. Parallel Feature Extractor (PFE) meant to capture visual contents of images involves partitioning images into segments, detecting image overlaps if any, and controlling distributed and redistributed segments to extract the features. Working on low light intensity images make the PFE face challenges and closely depend on the quality of its pre-processing steps. Some papers have suggested many well established NR as well as PFE strategies however only few resources have suggested or mentioned the correlation between them. This paper reviews best approaches of the NR and the PFE with detailed explanation on the suggested correlation. This finding may suggest relevant strategies of the PFE development. With the help of knowledge based reasoning, computational approaches and algorithms, we present the correlation study between the NR and the PFE that can be useful for the development and enhancement of other existing PFE

  6. Coherent noise reduction in digital holographic microscopy by averaging multiple holograms recorded with a multimode laser.

    Science.gov (United States)

    Pan, Feng; Yang, Lizhi; Xiao, Wen

    2017-09-04

    In digital holographic microscopy (DHM), it is undesirable to observe coherent noise in the reconstructed images. The sources of the noise are mainly the parasitic interference fringes caused by multiple reflections and the speckle pattern caused by the optical scattering on the object surface. Here we propose a noise reduction approach in DHM by averaging multiple holograms recorded with a multimode laser. Based on the periodicity of the temporal coherence of a multimode semiconductor laser, we acquire a series of holograms by changing the optical path length difference between the reference beam and object beam. Because of the use of low coherence light, we can remove the parasitic interference fringes caused by multiple reflections in the holograms. In addition, the coherent noise patterns change in this process due to the different optical paths. Therefore, the coherent noise can be reduced by averaging the multiple reconstructions with uncorrelated noise patterns. Several experiments have been carried out to validate the effectiveness of the proposed approach for coherent noise reduction in DHM. It is shown a remarkable improvement both in amplitude imaging quality and phase measurement accuracy.

  7. Comparison study of noise reduction algorithms in dual energy chest digital tomosynthesis

    Science.gov (United States)

    Lee, D.; Kim, Y.-S.; Choi, S.; Lee, H.; Choi, S.; Kim, H.-J.

    2018-04-01

    Dual energy chest digital tomosynthesis (CDT) is a recently developed medical technique that takes advantage of both tomosynthesis and dual energy X-ray images. However, quantum noise, which occurs in dual energy X-ray images, strongly interferes with diagnosis in various clinical situations. Therefore, noise reduction is necessary in dual energy CDT. In this study, noise-compensating algorithms, including a simple smoothing of high-energy images (SSH) and anti-correlated noise reduction (ACNR), were evaluated in a CDT system. We used a newly developed prototype CDT system and anthropomorphic chest phantom for experimental studies. The resulting images demonstrated that dual energy CDT can selectively image anatomical structures, such as bone and soft tissue. Among the resulting images, those acquired with ACNR showed the best image quality. Both coefficient of variation and contrast to noise ratio (CNR) were the highest in ACNR among the three different dual energy techniques, and the CNR of bone was significantly improved compared to the reconstructed images acquired at a single energy. This study demonstrated the clinical value of dual energy CDT and quantitatively showed that ACNR is the most suitable among the three developed dual energy techniques, including standard log subtraction, SSH, and ACNR.

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

    Directory of Open Access Journals (Sweden)

    Wang Wei

    2016-01-01

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

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

    DEFF Research Database (Denmark)

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

    2014-01-01

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

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

    Science.gov (United States)

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

    2014-06-01

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

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

    Science.gov (United States)

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

    2013-01-01

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

  12. Study of performance of acoustic fixture for using in noise reduction rate tests of hearing protection devices

    Directory of Open Access Journals (Sweden)

    zam Biabani

    2016-06-01

    Full Text Available Introduction:One of the recommended methods for evaluation effectiveness of hearing protection is use the acoustic fixture accordance with standard ISO 4869-3. The aim of this study was evaluate the acoustic performance of fixture for using in noise reduction rate tests of hearing protection devices in the laboratory. Methods: In this cross-sectional study , noise reduction rates of five common ear muffs used in the Iran industries were investigated based on the ISO 11904 standard, microphone in real ear method, using noise dosimeter (SVANTEK , Model SV102 equipped with microphone SV25 model which can install inside the ear on 30 subjects under laboratory conditions. Also, noise reduction rate of earmuffs was determined using the fixture model AVASINA9402 accordance with standard procedures. Data were analyzed using the software SPSS21. Results: The results showed the real noise reduction rates of the earmuffs on the studied subjects are from 59% to 94% nominal reduction rates. That rates for the ear muffs on the studied fixture are from 64% to 92.The results showed that the noise reduction rates of the ear muffs on subjects compared with and noise reduction rates of the ear muffs on fixture were not statistically significant (p> 0.05. Conclusion: The results showed the accuracy of noise reduction rate of earmuffs using the fixture compared with real subjects is acceptable. Hence, the fixture is good choice for environments where there’s no possibility of acoustic evaluation on real subjects, also for quality control of productions in the earmuff manufacturers.

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

    Directory of Open Access Journals (Sweden)

    Young-Seok Choi

    2013-01-01

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

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

    Science.gov (United States)

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

    2018-04-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    International Nuclear Information System (INIS)

    Ciftcioglu, O.

    1991-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Chen Siyu

    2017-01-01

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

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

    Science.gov (United States)

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

    2005-03-01

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

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

    Directory of Open Access Journals (Sweden)

    Byeong Hak Kim

    2017-12-01

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

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

    Science.gov (United States)

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

    2017-01-01

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

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

    Science.gov (United States)

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

    2017-12-27

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

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

    Science.gov (United States)

    Zhao, Haiquan; Zhang, Jiashu

    2009-12-01

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

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Directory of Open Access Journals (Sweden)

    Doo Yong Choi

    2016-04-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

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

    Directory of Open Access Journals (Sweden)

    Dalei Song

    2012-10-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-06-15

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

  8. Comparative study of adaptive-noise-cancellation algorithms for intrusion detection systems

    International Nuclear Information System (INIS)

    Claassen, J.P.; Patterson, M.M.

    1981-01-01

    Some intrusion detection systems are susceptible to nonstationary noise resulting in frequent nuisance alarms and poor detection when the noise is present. Adaptive inverse filtering for single channel systems and adaptive noise cancellation for two channel systems have both demonstrated good potential in removing correlated noise components prior detection. For such noise susceptible systems the suitability of a noise reduction algorithm must be established in a trade-off study weighing algorithm complexity against performance. The performance characteristics of several distinct classes of algorithms are established through comparative computer studies using real signals. The relative merits of the different algorithms are discussed in the light of the nature of intruder and noise signals

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

    International Nuclear Information System (INIS)

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

    2014-01-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-08-15

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

  11. AN AMELIORATED DETECTION STATISTICS FOR ADAPTIVE MASK MEDIAN FILTRATION OF HEAVILY NOISED DIGITAL IMAGES

    Directory of Open Access Journals (Sweden)

    Geeta Hanji

    2016-11-01

    Full Text Available Noise reduction is an important area of research in image processing applications. The performance of the digital image noise filtering method primarily depends upon the accuracy of noise detection scheme. This paper presents an effective detector based, adaptive mask, median filtration of heavily noised digital images affected with fixed value (or salt and pepper impulse noise. The proposed filter presents a novel approach; an ameliorated Rank Ordered Absolute Deviation (ROAD statistics to judge whether the input pixel is noised or noise free. If a pixel is detected as corrupted, it is subjected to adaptive mask median filtration; otherwise, it is kept unchanged. Extensive experimental results and comparative performance evaluations demonstrate that the proposed filter outperforms the existing decision type, median based filters with powerful noise detectors in terms of objective performance measures and visual retrieviation accuracy.

  12. Definition of 1992 Technology Aircraft Noise Levels and the Methodology for Assessing Airplane Noise Impact of Component Noise Reduction Concepts

    Science.gov (United States)

    Kumasaka, Henry A.; Martinez, Michael M.; Weir, Donald S.

    1996-01-01

    This report describes the methodology for assessing the impact of component noise reduction on total airplane system noise. The methodology is intended to be applied to the results of individual study elements of the NASA-Advanced Subsonic Technology (AST) Noise Reduction Program, which will address the development of noise reduction concepts for specific components. Program progress will be assessed in terms of noise reduction achieved, relative to baseline levels representative of 1992 technology airplane/engine design and performance. In this report, the 1992 technology reference levels are defined for assessment models based on four airplane sizes - an average business jet and three commercial transports: a small twin, a medium sized twin, and a large quad. Study results indicate that component changes defined as program final goals for nacelle treatment and engine/airframe source noise reduction would achieve from 6-7 EPNdB reduction of total airplane noise at FAR 36 Stage 3 noise certification conditions for all of the airplane noise assessment models.

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

    Directory of Open Access Journals (Sweden)

    Shuangshu Tian

    2015-12-01

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

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

    KAUST Repository

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

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Hongjian Wang

    2014-01-01

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

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

    Science.gov (United States)

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

    2016-01-01

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

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

    Science.gov (United States)

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

    2015-01-01

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

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

    KAUST Repository

    Kammoun, Abla

    2017-10-25

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

  19. An Interactive Procedure to Preserve the Desired Edges during the Image Processing of Noise Reduction

    Directory of Open Access Journals (Sweden)

    Lin-Tsang Lee

    2010-01-01

    Full Text Available The paper propose a new procedure including four stages in order to preserve the desired edges during the image processing of noise reduction. A denoised image can be obtained from a noisy image at the first stage of the procedure. At the second stage, an edge map can be obtained by the Canny edge detector to find the edges of the object contours. Manual modification of an edge map at the third stage is optional to capture all the desired edges of the object contours. At the final stage, a new method called Edge Preserved Inhomogeneous Diffusion Equation (EPIDE is used to smooth the noisy images or the previously denoised image at the first stage for achieving the edge preservation. The Optical Character Recognition (OCR results in the experiments show that the proposed procedure has the best recognition result because of the capability of edge preservation.

  20. On the dynamics of the preference-performance relation for hearing aid noise reduction

    DEFF Research Database (Denmark)

    Fischer, Rosa-Linde; Wagener, Kirsten C.; Vormann, Matthias

    on the data collected during the first laboratory assessment of the study. In particular, the influence of hearing aid experience and individual noise sensitivity on the preference-performance relation will be presented and discussed. REFERENCES S. Getzmann, E. Wascher and M. Falkenstein (2015). "What does......Previous research has shown that hearing aid users can differ substantially in their preference for noise reduction (NR) strength, and that preference for and speech recognition with NR processing typically are not correlated (e.g. Neher 2014; Serman et al. 2016). In other words, hearing aid users...... may prefer a certain NR setting, but perform better with a different one. The aim of the present work was to investigate the influence of individual noise sensitivity, hearing aid experience and acclimatization on the preference-performance relation for different NR settings. For this purpose...

  1. Image processing methods for noise reduction in the TJ-II Thomson Scattering diagnostic

    Energy Technology Data Exchange (ETDEWEB)

    Dormido-Canto, S., E-mail: sebas@dia.uned.es [Departamento de Informatica y Automatica, UNED, Madrid 28040 (Spain); Farias, G. [Pontificia Universidad Catolica de Valparaiso, Valparaiso (Chile); Vega, J.; Pastor, I. [Asociacion EURATOM/CIEMAT para Fusion, Madrid 28040 (Spain)

    2012-12-15

    Highlights: Black-Right-Pointing-Pointer We describe an approach in order to reduce or mitigate the stray-light on the images and show the exceptional results. Black-Right-Pointing-Pointer We analyze the parameters to take account in the proposed process. Black-Right-Pointing-Pointer We report a simplified exampled in order to explain the proposed process. - Abstract: The Thomsom Scattering diagnostic of the TJ-II stellarator provides temperature and density profiles. The CCD camera acquires images corrupted with noise that, in some cases, can produce unreliable profiles. The main source of noise is the so-called stray-light. In this paper we describe an approach that allows mitigation of the effects that stray-light has on the images: extraction regions with connected-components. In addition, the robustness and effectiveness of the noise reduction technique is validated in two ways: (1) supervised classification and (2) comparison of electron temperature profiles.

  2. Engine-propeller power plant aircraft community noise reduction key methods

    Science.gov (United States)

    Moshkov P., A.; Samokhin V., F.; Yakovlev A., A.

    2018-04-01

    Basic methods of aircraft-type flying vehicle engine-propeller power plant noise reduction were considered including single different-structure-and-arrangement propellers and piston engines. On the basis of a semiempirical model the expressions for blade diameter and number effect evaluation upon propeller noise tone components under thrust constancy condition were proposed. Acoustic tests performed at Moscow Aviation institute airfield on the whole qualitatively proved the obtained ratios. As an example of noise and detectability reduction provision a design-and-experimental estimation of propeller diameter effect upon unmanned aircraft audibility boundaries was performed. Future investigation ways were stated to solve a low-noise power plant design problem for light aircraft and unmanned aerial vehicles.

  3. Study on ventilation and noise reduction in the main transformer room in indoor substation

    Directory of Open Access Journals (Sweden)

    Hu Sheng

    2016-01-01

    Full Text Available The noise emission should be considered in the ventilation and cooling design for the main transformer room of indoor substation. In this study, based on Soundplan software, effects of four common ventilation and cooling schemes on the cooling and sound insulation were compared. The research showed that the region with low noise requirement, the ventilation could be set on the outer wall or on the door of the main transformer room, while the region with high noise requirement, air inlet muffler or ventilation through the cable interlayer under the main transformer room must be used. All of the four kinds of ventilation schemes, ventilation through the cable interlayer is the best in cooling and noise reduction.

  4. Sound quality measures for speech in noise through a commercial hearing aid implementing digital noise reduction.

    Science.gov (United States)

    Ricketts, Todd A; Hornsby, Benjamin W Y

    2005-05-01

    This brief report discusses the affect of digital noise reduction (DNR) processing on aided speech recognition and sound quality measures in 14 adults fitted with a commercial hearing aid. Measures of speech recognition and sound quality were obtained in two different speech-in-noise conditions (71 dBA speech, +6 dB SNR and 75 dBA speech, +1 dB SNR). The results revealed that the presence or absence of DNR processing did not impact speech recognition in noise (either positively or negatively). Paired comparisons of sound quality for the same speech in noise signals, however, revealed a strong preference for DNR processing. These data suggest that at least one implementation of DNR processing is capable of providing improved sound quality, for speech in noise, in the absence of improved speech recognition.

  5. Application of an active device for helicopter noise reduction in JAXA

    International Nuclear Information System (INIS)

    Saito, Shigeru; Kobiki, Noboru; Tanabe, Yasutada

    2010-01-01

    Important issues in noise problems for current helicopters are described. An active tab (AT) was developed as a new active device for noise/vibration reduction under research cooperation between Japan Aerospace Exploration Agency (JAXA) and Kawada Industries, Inc. The wind tunnel test was conducted in order to investigate the effectiveness of the AT on the aeroacoustic characteristics of a helicopter. From the wind tunnel test, the capability of reducing blade vortex interaction (BVI) noise by an AT was verified. A new control law using instantaneous pressure change on a blade during BVI phenomena was introduced and applied to the wind tunnel testing. This new control law shows reasonable controllability for helicopter noise reduction. Furthermore, in order to analyze noise characteristics, the advanced computational fluid dynamics (CFD) code named JAXA o v3d was developed in JAXA and extended to include CFD-CSD (computational structure dynamics) coupling by using the beam theory for blade deformation. (invited paper)

  6. Prewhitening for Rank-Deficient Noise in Subspace Methods for Noise Reduction

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Jensen, Søren Holdt

    2005-01-01

    A fundamental issue in connection with subspace methods for noise reduction is that the covariance matrix for the noise is required to have full rank, in order for the prewhitening step to be defined. However, there are important cases where this requirement is not fulfilled, e.g., when the noise...... has narrow-band characteristics, or in the case of tonal noise. We extend the concept of prewhitening to include the case when the noise covariance matrix is rank deficient, using a weighted pseudoinverse and the quotient SVD, and we show how to formulate a general rank-reduction algorithm that works...... also for rank deficient noise. We also demonstrate how to formulate this algorithm by means of a quotient ULV decomposition, which allows for faster computation and updating. Finally we apply our algorithm to a problem involving a speech signal contaminated by narrow-band noise....

  7. Localized, Non-Harmonic Active Flap Motions for Low Frequency In-Plane Rotor Noise Reduction

    Science.gov (United States)

    Sim, Ben W.; Potsdam, Mark; Kitaplioglu, Cahit; LeMasurier, Philip; Lorber, Peter; Andrews, Joseph

    2012-01-01

    A first-of-its-kind demonstration of the use of localized, non-harmonic active flap motions, for suppressing low frequency, in-plane rotor noise, is reported in this paper. Operational feasibility is verified via testing of the full-scale AATD/Sikorsky/UTRC active flap demonstration rotor in the NFAC's 40- by 80-Foot anechoic wind tunnel. Effectiveness of using localized, non-harmonic active flap motions are compared to conventional four-per-rev harmonic flap motions, and also active flap motions derived from closed-loop acoustics implementations. All three approaches resulted in approximately the same noise reductions over an in-plane three-by-three microphone array installed forward and near in-plane of the rotor in the nearfield. It is also reported that using an active flap in this localized, non-harmonic manner, resulted in no more that 2% rotor performance penalty, but had the tendency to incur higher hub vibration levels.

  8. Generative Adversarial Networks for Noise Reduction in Low-Dose CT.

    Science.gov (United States)

    Wolterink, Jelmer M; Leiner, Tim; Viergever, Max A; Isgum, Ivana

    2017-12-01

    Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained to distinguish the output of the generator from routine-dose CT images. The performance of this discriminator was used as an adversarial loss for the generator. Experiments were performed using CT images of an anthropomorphic phantom containing calcium inserts, as well as patient non-contrast-enhanced cardiac CT images. The phantom and patients were scanned at 20% and 100% routine clinical dose. Three training strategies were compared: the first used only voxelwise loss, the second combined voxelwise loss and adversarial loss, and the third used only adversarial loss. The results showed that training with only voxelwise loss resulted in the highest peak signal-to-noise ratio with respect to reference routine-dose images. However, CNNs trained with adversarial loss captured image statistics of routine-dose images better. Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels. Testing took less than 10 s per CT volume. CNN-based low-dose CT noise reduction in the image domain is feasible. Training with an adversarial network improves the CNNs ability to generate images with an appearance similar to that of reference routine-dose CT images.

  9. Multichannel Signal Enhancement using Non-Causal, Time-Domain Filters

    DEFF Research Database (Denmark)

    Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Benesty, Jacob

    2013-01-01

    In the vast amount of time-domain filtering methods for speech enhancement, the filters are designed to be causal. Recently, however, it was shown that the noise reduction and signal distortion capabilities of such single-channel filters can be improved by allowing the filters to be non-causal. W......In the vast amount of time-domain filtering methods for speech enhancement, the filters are designed to be causal. Recently, however, it was shown that the noise reduction and signal distortion capabilities of such single-channel filters can be improved by allowing the filters to be non......-causal, multichannel filters for enhancement based on an orthogonal decomposition is proposed. The evaluation shows that there is a potential gain in noise reduction and signal distortion by introducing non-causality. Moreover, experiments on real-life speech show that we can improve the perceptual quality....

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

    Directory of Open Access Journals (Sweden)

    Li N.

    2017-01-01

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

  11. Effects of cavity resonances on sound transmission into a thin cylindrical shell. [noise reduction in aircraft fuselage

    Science.gov (United States)

    Koval, L. R.

    1978-01-01

    In the context of the transmission of airborne noise into an aircraft fuselage, a mathematical model is presented for the effects of internal cavity resonances on sound transmission into a thin cylindrical shell. The 'noise reduction' of the cylinder is defined and computed, with and without including the effects of internal cavity resonances. As would be expected, the noise reduction in the absence of cavity resonances follows the same qualitative pattern as does transmission loss. Numerical results show that cavity resonances lead to wide fluctuations and a general decrease of noise reduction, especially at cavity resonances. Modest internal absorption is shown to greatly reduce the effect of cavity resonances. The effects of external airflow, internal cabin pressurization, and different acoustical properties inside and outside the cylinder are also included and briefly examined.

  12. A Two-Microphone Noise Reduction System for Cochlear Implant Users with Nearby Microphones—Part I: Signal Processing Algorithm Design and Development

    Directory of Open Access Journals (Sweden)

    Marco Pelizzone

    2008-06-01

    Full Text Available Users of cochlear implant systems, that is, of auditory aids which stimulate the auditory nerve at the cochlea electrically, often complain about poor speech understanding in noisy environments. Despite the proven advantages of multimicrophone directional noise reduction systems for conventional hearing aids, only one major manufacturer has so far implemented such a system in a product, presumably because of the added power consumption and size. We present a physically small (intermicrophone distance 7 mm and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. Supporting algorithms, which allow the adjustment of the opening angle and the maximum noise suppression, are proposed and evaluated. A portable real-time device for test in real acoustic environments is presented.

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

    Science.gov (United States)

    Balas, Mark J.; Frost, Susan

    2011-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Mingyue Cui

    2016-09-01

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

  15. Effect of Two Advanced Noise Reduction Technologies on the Aerodynamic Performance of an Ultra High Bypass Ratio Fan

    Science.gov (United States)

    Hughes, Christoper E.; Gazzaniga, John A.

    2013-01-01

    A wind tunnel experiment was conducted in the NASA Glenn Research Center anechoic 9- by 15-Foot Low-Speed Wind Tunnel to investigate two new advanced noise reduction technologies in support of the NASA Fundamental Aeronautics Program Subsonic Fixed Wing Project. The goal of the experiment was to demonstrate the noise reduction potential and effect on fan model performance of the two noise reduction technologies in a scale model Ultra-High Bypass turbofan at simulated takeoff and approach aircraft flight speeds. The two novel noise reduction technologies are called Over-the-Rotor acoustic treatment and Soft Vanes. Both technologies were aimed at modifying the local noise source mechanisms of the fan tip vortex/fan case interaction and the rotor wake-stator interaction. For the Over-the-Rotor acoustic treatment, two noise reduction configurations were investigated. The results showed that the two noise reduction technologies, Over-the-Rotor and Soft Vanes, were able to reduce the noise level of the fan model, but the Over-the-Rotor configurations had a significant negative impact on the fan aerodynamic performance; the loss in fan aerodynamic efficiency was between 2.75 to 8.75 percent, depending on configuration, compared to the conventional solid baseline fan case rubstrip also tested. Performance results with the Soft Vanes showed that there was no measurable change in the corrected fan thrust and a 1.8 percent loss in corrected stator vane thrust, which resulted in a total net thrust loss of approximately 0.5 percent compared with the baseline reference stator vane set.

  16. Adaptive partial median filter for early CT signs of acute cerebral infarction

    International Nuclear Information System (INIS)

    Lee, Yongbum; Tsai, Du-Yih; Takahashi, Noriyuki; Ishii, Kiyoshi

    2007-01-01

    Purpose: Detection of early CT signs of infarct in non- enhanced CT image is mandatory in patients with acute ischemic stroke. Loss of the gray-white matter interface at the lentiform nucleus or the insular ribbon has been an important early CT sign of acute cerebral infarction, which affects decisions on thrombolytic therapy. However, its detection is difficult, since the principal early CT sign is subtle hypoattenuation. An image processing method to reduce local noise with edges preserved was developed to improve infarct detection. Rationale: An adaptive partial median filter (APMF) was selected for this application, since the APMF can markedly improve the visibility of the normal gray-white matter interface. APMF should enhance the conspicuity of gray-white matter interface changes due to hypoattenuation that accompanies cerebral infarction. Method: In a criterion referenced performance study using simulated CT images with gray-white matter interfaces, a total of 14 conventional smoothing filters were also used for comparison to validate the usefulness of the proposed APMF. The APMF indicated the highest performance among the compared methods. Then, observer performance study by receiver operator characteristic (ROC) analysis was performed with 4 radiologist observers using a database with 18 abnormal and 33 normal head CT images. The average A z values of ROC curves for all radiologists increased from 0.876 without the APMF images to 0.926 with the APMF images, and this difference was statistically significant (P = 0.04). The results from the two observer performance studies demonstrated that APMF has significant potential to improve the diagnosis of acute cerebral infarction using non-enhanced CT images. (orig.)

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

    Science.gov (United States)

    Kuznetsova, T. A.

    2018-05-01

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

  18. Application of the fourier and wavelet transforms in noise reduction of the out of the ordinary data

    International Nuclear Information System (INIS)

    Tafreshi, M. A.; Sadeghi, Y.

    2006-01-01

    In this article the noise reduction of the experimental data by the Fourier and the wavelet transforms has been investigated. Using both simulated and experimental data (from the plasma focus facility, Dena), the sensitive features of the application of the Fourier transform are visualized and discussed. Then, the main idea of the wavelet transform and the results of the noise reduction with this transform are presented. Due to this investigation, for the cases such as the current derivative of the Dena facility, where the reliability of the Fourier transform can be doubtful, the wavelet transform can be considered as a more accurate alternative approach

  19. A bulk-controlled ring-VCO with 1/f-noise reduction for frequency ΔΣ modulator

    DEFF Research Database (Denmark)

    Tuan Vu, CAO; Wisland, Dag T.; Lande, Tor Sverre

    The paper introduces a bulk-controlled ring-VCO with a tail transistor utilizing flicker-noise (1/f-noise) reduction techniques for a frequency-based DeltaSigma modulator (FDSM). This VCO converts an analog input voltage to phase information under various bias conditions ranging from sub-threshol......The paper introduces a bulk-controlled ring-VCO with a tail transistor utilizing flicker-noise (1/f-noise) reduction techniques for a frequency-based DeltaSigma modulator (FDSM). This VCO converts an analog input voltage to phase information under various bias conditions ranging from sub...

  20. The effect of oblique angle of sound incidence, realistic edge conditions, curvature and in-plane panel stresses on the noise reduction characteristics of general aviation type panels

    Science.gov (United States)

    Grosveld, F.; Lameris, J.; Dunn, D.

    1979-01-01

    Experiments and a theoretical analysis were conducted to predict the noise reduction of inclined and curved panels. These predictions are compared to the experimental results with reasonable agreement between theory and experiment for panels under an oblique angle of sound incidence. Theoretical as well as experimental results indicate a big increase in noise reduction when a flat test panel is curved. Further curving the panel slightly decreases the noise reduction. Riveted flat panels are shown to give a higher noise reduction in the stiffness-controlled frequency region, while bonded panels are superior in this region when the test panel is curved. Experimentally measured noise reduction characteristics of flat aluminum panels with uniaxial in-plane stresses are presented and discussed. These test results indicate an important improvement in the noise reduction of these panels in the frequency range below the fundamental panel/cavity frequency.

  1. Noise Exposure of Teachers in Nursery Schools-Evaluation of Measures for Noise Reduction When Dropping DUPLO Toy Bricks into Storage Cases by Sound Analyses.

    Science.gov (United States)

    Gebauer, Konstanze; Scharf, Thomas; Baumann, Uwe; Groneberg, David A; Bundschuh, Matthias

    2016-07-04

    Although noise is one of the leading work-related health risk factors for teachers, many nursery schools lack sufficient noise reduction measures. This intervention study evaluated the noise exposure of nursery school teachers when dropping DUPLO toy bricks into storage cases. Sound analyses of the impact included assessment of the maximum sound pressure level (LAFmax) as well as frequency analyses with 1/3 octave band filter. For the purpose of standardization, a customized gadget was developed. Recordings were performed in 11 cases of different materials and designs to assess the impact on sound level reduction. Thereby, the acoustic effects of three damping materials (foam rubber, carpet, and PU-foam) were investigated. The lowest LAFmax was measured in cases consisting of "metal grid" (90.71 dB) or of a woven willow "basket" (91.61 dB), whereas a case of "aluminium" (103.34 dB) generated the highest impact LAFmax. The frequency analyses determined especially low LAFmax in the frequency bands between 80 and 2500 Hz in cases designs "metal grid" and "basket". The insertion of PU-foam achieved the most significant attenuation of LAFmax (-13.88 dB) and, in the frequency analyses, the best sound damping. The dropping of DUPLO bricks in cases contributes to the high noise level in nursery schools, but measured LAFmax show no evidence for the danger of acute hearing loss. However, continuous exposure may lead to functional impairment of the hair cells and trigger stress reactions. We recommend noise reduction by utilizing cases of woven "basket" with an insert of PU-foam.

  2. Noise Exposure of Teachers in Nursery Schools—Evaluation of Measures for Noise Reduction When Dropping DUPLO Toy Bricks into Storage Cases by Sound Analyses

    Directory of Open Access Journals (Sweden)

    Konstanze Gebauer

    2016-07-01

    Full Text Available Background: Although noise is one of the leading work-related health risk factors for teachers, many nursery schools lack sufficient noise reduction measures. Methods: This intervention study evaluated the noise exposure of nursery school teachers when dropping DUPLO toy bricks into storage cases. Sound analyses of the impact included assessment of the maximum sound pressure level (LAFmax as well as frequency analyses with 1/3 octave band filter. For the purpose of standardization, a customized gadget was developed. Recordings were performed in 11 cases of different materials and designs to assess the impact on sound level reduction. Thereby, the acoustic effects of three damping materials (foam rubber, carpet, and PU-foam were investigated. Results: The lowest LAFmax was measured in cases consisting of “metal grid” (90.71 dB or of a woven willow “basket” (91.61 dB, whereas a case of “aluminium” (103.34 dB generated the highest impact LAFmax. The frequency analyses determined especially low LAFmax in the frequency bands between 80 and 2500 Hz in cases designs “metal grid” and “basket”. The insertion of PU-foam achieved the most significant attenuation of LAFmax (−13.88 dB and, in the frequency analyses, the best sound damping. Conclusion: The dropping of DUPLO bricks in cases contributes to the high noise level in nursery schools, but measured LAFmax show no evidence for the danger of acute hearing loss. However, continuous exposure may lead to functional impairment of the hair cells and trigger stress reactions. We recommend noise reduction by utilizing cases of woven “basket” with an insert of PU-foam.

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

    International Nuclear Information System (INIS)

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

    2010-01-01

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

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

    Science.gov (United States)

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

    2011-02-01

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

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

    International Nuclear Information System (INIS)

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

    2011-01-01

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

  6. Flexible Riser Monitoring Using Hybrid Magnetic/Optical Strain Gage Techniques through RLS Adaptive Filtering

    Directory of Open Access Journals (Sweden)

    Daniel Pipa

    2010-01-01

    Full Text Available Flexible riser is a class of flexible pipes which is used to connect subsea pipelines to floating offshore installations, such as FPSOs (floating production/storage/off-loading unit and SS (semisubmersible platforms, in oil and gas production. Flexible risers are multilayered pipes typically comprising an inner flexible metal carcass surrounded by polymer layers and spiral wound steel ligaments, also referred to as armor wires. Since these armor wires are made of steel, their magnetic properties are sensitive to the stress they are subjected to. By measuring their magnetic properties in a nonintrusive manner, it is possible to compare the stress in the armor wires, thus allowing the identification of damaged ones. However, one encounters several sources of noise when measuring electromagnetic properties contactlessly, such as movement between specimen and probe, and magnetic noise. This paper describes the development of a new technique for automatic monitoring of armor layers of flexible risers. The proposed approach aims to minimize these current uncertainties by combining electromagnetic measurements with optical strain gage data through a recursive least squares (RLSs adaptive filter.

  7. RLS adaptive filtering for physiological interference reduction in NIRS brain activity measurement: a Monte Carlo study

    International Nuclear Information System (INIS)

    Zhang, Y; Sun, J W; Rolfe, P

    2012-01-01

    The non-invasive measurement of cerebral functional haemodynamics using near-infrared spectroscopy (NIRS) instruments is often affected by physiological interference. The suppression of this interference is crucial for reliable recovery of brain activity measurements because it can significantly affect the signal quality. In this study, we present a recursive least-squares (RLS) algorithm for adaptive filtering to reduce the magnitude of the physiological interference component. To evaluate it, we implemented Monte Carlo simulations based on a five-layer slab model of a human adult head with a multidistance source–detector arrangement, of a short pair and a long pair, for NIRS measurement. We derived measurements by adopting different interoptode distances, which is relevant to the process of optimizing the NIRS probe configuration. Both RLS and least mean squares (LMS) algorithms were used to attempt the removal of physiological interference. The results suggest that the RLS algorithm is more capable of minimizing the effect of physiological interference due to its advantages of faster convergence and smaller mean squared error (MSE). The influence of superficial layer thickness on the performance of the RLS algorithm was also investigated. We found that the near-detector position is an important variable in minimizing the MSE and a short source–detector separation less than 9 mm is robust to superficial layer thickness variation. (paper)

  8. Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

    Directory of Open Access Journals (Sweden)

    Ghalib R. Ibrahim

    2012-01-01

    Full Text Available Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio, which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal and fault 2 (50% tooth removal under loads (0%, and 80% of the total load. The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

  9. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  10. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  11. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  12. Measured and calculated noise reduction of rail dampers and absorption plates on a high speed slab track

    NARCIS (Netherlands)

    Dittrich, M.G.; Graafland, F.; Eisses, A.R.; Nijhof, M.J.J.

    2015-01-01

    Rail dampers and sound absorbing plates have been tested on a high speed railway slab track in a walled cutting at a noise sensitive location. Their noise reduction has been determined from pass-by measurements during service and predicted using BEM calculations. The cutting depth, noise barrier

  13. Meta-Analysis of Free-Response Studies, 1992-2008: Assessing the Noise Reduction Model in Parapsychology

    Science.gov (United States)

    Storm, Lance; Tressoldi, Patrizio E.; Di Risio, Lorenzo

    2010-01-01

    We report the results of meta-analyses on 3 types of free-response study: (a) ganzfeld (a technique that enhances a communication anomaly referred to as "psi"); (b) nonganzfeld noise reduction using alleged psi-enhancing techniques such as dream psi, meditation, relaxation, or hypnosis; and (c) standard free response (nonganzfeld, no noise…

  14. Damping layout optimization for ship's cabin noise reduction based on statistical energy analysis

    Directory of Open Access Journals (Sweden)

    WU Weiguo

    2017-08-01

    Full Text Available An optimization analysis study concerning the damping control of ship's cabin noise was carried out in order to improve the effect and reduce the weight of damping. Based on the Statistical Energy Analysis (SEA method, a theoretical deduction and numerical analysis of the first-order sensitivity analysis of the A-weighted sound pressure level concerning the damping loss factor of the subsystem were carried out. On this basis, a mathematical optimization model was proposed and an optimization program developed. Next, the secondary development of VA One software was implemented through the use of MATLAB, while the cabin noise damping control layout optimization system was established. Finally, the optimization model of the ship was constructed and numerical experiments of damping control optimization conducted. The damping installation region was divided into five parts with different damping thicknesses. The total weight of damping was set as an objective function and the A-weighted sound pressure level of the target cabin was set as a constraint condition. The best damping thickness was obtained through the optimization program, and the total damping weight was reduced by 60.4%. The results show that the damping noise reduction effect of unit weight is significantly improved through the optimization method. This research successfully solves the installation position and thickness selection problems in the acoustic design of damping control, providing a reliable analysis method and guidance for the design.

  15. Mastication noise reduction method for fully implantable hearing aid using piezo-electric sensor.

    Science.gov (United States)

    Na, Sung Dae; Lee, Gihyoun; Wei, Qun; Seong, Ki Woong; Cho, Jin Ho; Kim, Myoung Nam

    2017-07-20

    Fully implantable hearing devices (FIHDs) can be affected by generated biomechanical noise such as mastication noise. To reduce the mastication noise using a piezo-electric sensor, the mastication noise is measured with the piezo-electric sensor, and noise reduction is practiced by the energy difference. For the experiment on mastication noise, a skull model was designed using artificial skull model and a piezo-electric sensor that can measure the vibration signals better than other sensors. A 1 kHz pure-tone sound through a standard speaker was applied to the model while the lower jawbone of the model was moved in a masticatory fashion. The correlation coefficients and signal-to-noise ratio (SNR) before and after application of the proposed method were compared. It was found that the signal-to-noise ratio and correlation coefficients increased by 4.48 dB and 0.45, respectively. The mastication noise is measured by piezo-electric sensor as the mastication noise that occurred during vibration. In addition, the noise was reduced by using the proposed method in conjunction with MATLAB. In order to confirm the performance of the proposed method, the correlation coefficients and signal-to-noise ratio before and after signal processing were calculated. In the future, an implantable microphone for real-time processing will be developed.

  16. Noise Reduction Properties of an Experimental Bituminous Slurry with Crumb Rubber Incorporated by the Dry Process

    Directory of Open Access Journals (Sweden)

    Moisés Bueno

    2014-08-01

    Full Text Available Nowadays, cold technology for asphalt pavement in the field of road construction is considered as an alternative solution to conventional procedures from both an economic and environmental point of view. Among these techniques, bituminous slurry surfacing is obtaining an important role due to the properties of the obtained wearing course. The functional performance of this type of surfaces is directly related to its rough texture. Nevertheless, this parameter has a significant influence on the tire/road noise generation. To reduce this undesirable effect on the sound performance, new designs of elastic bituminous slurries have been developed. Within the FENIX project, this work presents the acoustical characterization of an experimental bituminous slurry with crumb rubber from wasted automobile tires incorporated by the dry process. The obtained results show that, under controlled operational parameters, the close proximity sound levels associated to the experimental slurry are considerably lower than those emitted by a conventional slurry wearing course. However, after one year of supporting traffic loads and different weather conditions, the evaluated bituminous slurry, although it conserves the original noise reduction properties in relation to the conventional one, noticeably increases the generated sound emission. Therefore, it is required to continue improving the design of experimental surfaces in order to enhance its long-term performance.

  17. Evaluation of Novel Liner Concepts for Fan and Airframe Noise Reduction

    Science.gov (United States)

    Jones, M. G.; Howerton, B. M.

    2016-01-01

    This paper presents a review of four novel liner concepts: soft vanes, over-the-rotor liners, external liners, and flap side-edge liners. A number of similarities in the design and evaluation of these concepts emerged during these investigations. Since these were the first attempts to study these particular liner concepts, there was limited information to guide the design process. In all cases, the target frequencies (or frequency range) were known, but the optimum acoustic impedance and optimum liner placement were typically not known. For these cases, the maximum available surface was used and a c-impedance was targeted based on the assumption the sound field impinges on the surface at normal incidence. This choice proved fruitful for every application. An impedance prediction model was used to design variable-depth liner configurations, and a graphical design code (ILIAD) was developed to aid in this process. The ability to build increasingly complex liner configurations via additive manufacturing was key, such that multiple designs could quickly be tested in a normal incidence impedance tube. The Two-Thickness Method was used to evaluate available bulk materials, such that bulk liners could also be considered for each application. These novel liner concepts provide sufficient noise reduction to warrant further investigations.

  18. Impact of Noise Reduction Algorithm in Cochlear Implant Processing on Music Enjoyment.

    Science.gov (United States)

    Kohlberg, Gavriel D; Mancuso, Dean M; Griffin, Brianna M; Spitzer, Jaclyn B; Lalwani, Anil K

    2016-06-01

    Noise reduction algorithm (NRA) in speech processing strategy has positive impact on speech perception among cochlear implant (CI) listeners. We sought to evaluate the effect of NRA on music enjoyment. Prospective analysis of music enjoyment. Academic medical center. Normal-hearing (NH) adults (N = 16) and CI listeners (N = 9). Subjective rating of music excerpts. NH and CI listeners evaluated country music piece on three enjoyment modalities: pleasantness, musicality, and naturalness. Participants listened to the original version and 20 modified, less complex versions created by including subsets of musical instruments from the original song. NH participants listened to the segments through CI simulation and CI listeners listened to the segments with their usual speech processing strategy, with and without NRA. Decreasing the number of instruments was significantly associated with increase in the pleasantness and naturalness in both NH and CI subjects (p  0.05): this was true for the original and the modified music segments with one to three instruments (p > 0.05). NRA does not affect music enjoyment in CI listener or NH individual with CI simulation. This suggests that strategies to enhance speech processing will not necessarily have a positive impact on music enjoyment. However, reducing the complexity of music shows promise in enhancing music enjoyment and should be further explored.

  19. Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

    Science.gov (United States)

    Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

    2009-02-01

    The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

  20. The effect of hearing aid noise reduction on listening effort in hearing-impaired adults.

    Science.gov (United States)

    Desjardins, Jamie L; Doherty, Karen A

    2014-01-01

    The purpose of the present study was to evaluate the effect of a noise-reduction (NR) algorithm on the listening effort hearing-impaired participants expend on a speech in noise task. Twelve hearing-impaired listeners fitted with behind-the-ear hearing aids with a fast-acting modulation-based NR algorithm participated in this study. A dual-task paradigm was used to measure listening effort with and without the NR enabled in the hearing aid. The primary task was a sentence-in-noise task presented at fixed overall speech performance levels of 76% (moderate listening condition) and 50% (difficult listening condition) correct performance, and the secondary task was a visual-tracking test. Participants also completed measures of working memory (Reading Span test), and processing speed (Digit Symbol Substitution Test) ability. Participants' speech recognition in noise scores did not significantly change with the NR algorithm activated in the hearing aid in either listening condition. The NR algorithm significantly decreased listening effort, but only in the more difficult listening condition. Last, there was a tendency for participants with faster processing speeds to expend less listening effort with the NR algorithm when listening to speech in background noise in the difficult listening condition. The NR algorithm reduced the listening effort adults with hearing loss must expend to understand speech in noise.

  1. Objective measures of listening effort: effects of background noise and noise reduction.

    Science.gov (United States)

    Sarampalis, Anastasios; Kalluri, Sridhar; Edwards, Brent; Hafter, Ervin

    2009-10-01

    This work is aimed at addressing a seeming contradiction related to the use of noise-reduction (NR) algorithms in hearing aids. The problem is that although some listeners claim a subjective improvement from NR, it has not been shown to improve speech intelligibility, often even making it worse. To address this, the hypothesis tested here is that the positive effects of NR might be to reduce cognitive effort directed toward speech reception, making it available for other tasks. Normal-hearing individuals participated in 2 dual-task experiments, in which 1 task was to report sentences or words in noise set to various signal-to-noise ratios. Secondary tasks involved either holding words in short-term memory or responding in a complex visual reaction-time task. At low values of signal-to-noise ratio, although NR had no positive effect on speech reception thresholds, it led to better performance on the word-memory task and quicker responses in visual reaction times. Results from both dual tasks support the hypothesis that NR reduces listening effort and frees up cognitive resources for other tasks. Future hearing aid research should incorporate objective measurements of cognitive benefits.

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

    Science.gov (United States)

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

    2016-12-01

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

  3. Sensorless Control of Interior Permanent Magnet Synchronous Motor in Low-Speed Region Using Novel Adaptive Filter

    Directory of Open Access Journals (Sweden)

    Lisi Tian

    2016-12-01

    Full Text Available This paper presents a novel position and speed estimation method for low-speed sensorless control of interior permanent-magnet synchronous machines (IPMSMs. The parameter design of the position and speed estimator is based on the sampled current rather than the motor electrical parameters. The proposed method not only simplifies the parameter design, it enables the estimator to work normally even in the condition that the electrical parameters are uncertain or varied. The adaptive filters are adopted to extract the desired high frequency current. The structure and corresponding transfer function are analyzed. To address the shortage of insufficient stop-band attenuation, the structure of the adaptive filter is modified to provide suitable bandwidth and stop-band attenuation simultaneously. The effectiveness of the proposed sensorless control strategy has been verified by simulations and experiments.

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

    Science.gov (United States)

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

    2017-09-01

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

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

    Science.gov (United States)

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

    2012-11-01

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

  6. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in brain CT

    International Nuclear Information System (INIS)

    Ren, Qingguo; Dewan, Sheilesh Kumar; Li, Ming; Li, Jianying; Mao, Dingbiao; Wang, Zhenglei; Hua, Yanqing

    2012-01-01

    Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI vol ) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique

  7. Comparison of adaptive statistical iterative and filtered back projection reconstruction techniques in brain CT

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Qingguo, E-mail: renqg83@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Dewan, Sheilesh Kumar, E-mail: sheilesh_d1@hotmail.com [Department of Geriatrics, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Ming, E-mail: minli77@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Li, Jianying, E-mail: Jianying.Li@med.ge.com [CT Imaging Research Center, GE Healthcare China, Beijing (China); Mao, Dingbiao, E-mail: maodingbiao74@163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China); Wang, Zhenglei, E-mail: Williswang_doc@yahoo.com.cn [Department of Radiology, Shanghai Electricity Hospital, Shanghai 200050 (China); Hua, Yanqing, E-mail: cjr.huayanqing@vip.163.com [Department of Radiology, Hua Dong Hospital of Fudan University, Shanghai 200040 (China)

    2012-10-15

    Purpose: To compare image quality and visualization of normal structures and lesions in brain computed tomography (CT) with adaptive statistical iterative reconstruction (ASIR) and filtered back projection (FBP) reconstruction techniques in different X-ray tube current–time products. Materials and methods: In this IRB-approved prospective study, forty patients (nineteen men, twenty-one women; mean age 69.5 ± 11.2 years) received brain scan at different tube current–time products (300 and 200 mAs) in 64-section multi-detector CT (GE, Discovery CT750 HD). Images were reconstructed with FBP and four levels of ASIR-FBP blending. Two radiologists (please note that our hospital is renowned for its geriatric medicine department, and these two radiologists are more experienced in chronic cerebral vascular disease than in neoplastic disease, so this research did not contain cerebral tumors but as a discussion) assessed all the reconstructed images for visibility of normal structures, lesion conspicuity, image contrast and diagnostic confidence in a blinded and randomized manner. Volume CT dose index (CTDI{sub vol}) and dose-length product (DLP) were recorded. All the data were analyzed by using SPSS 13.0 statistical analysis software. Results: There was no statistically significant difference between the image qualities at 200 mAs with 50% ASIR blending technique and 300 mAs with FBP technique (p > .05). While between the image qualities at 200 mAs with FBP and 300 mAs with FBP technique a statistically significant difference (p < .05) was found. Conclusion: ASIR provided same image quality and diagnostic ability in brain imaging with greater than 30% dose reduction compared with FBP reconstruction technique.

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

    Science.gov (United States)

    Piretzidis, Dimitrios; Sideris, Michael G.

    2017-09-01

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

  9. Noise reduction in long‐period seismograms by way of array summing

    Science.gov (United States)

    Ringler, Adam; Wilson, David; Storm, Tyler; Marshall, Benjamin T.; Hutt, Charles R.; Holland, Austin

    2016-01-01

    Long‐period (>100  s period) seismic data can often be dominated by instrumental noise as well as local site noise. When multiple collocated sensors are installed at a single site, it is possible to improve the overall station noise levels by applying stacking methods to their traces. We look at the noise reduction in long‐period seismic data by applying the time–frequency phase‐weighted stacking method of Schimmel and Gallart (2007) as well as the phase‐weighted stacking (PWS) method of Schimmel and Paulssen (1997) to four collocated broadband sensors installed in the quiet Albuquerque Seismological Laboratory underground vault. We show that such stacking methods can improve vertical noise levels by as much as 10 dB over the mean background noise levels at 400 s period, suggesting that greater improvements could be achieved with an array involving multiple sensors. We also apply this method to reduce local incoherent noise on horizontal seismic records of the 2 March 2016 Mw 7.8 Sumatra earthquake, where the incoherent noise levels at very long periods are similar in amplitude to the earthquake signal. To maximize the coherency, we apply the PWS method to horizontal data where relative azimuths between collocated sensors are estimated and compared with a simpler linear stack with no azimuthal rotation. Such methods could help reduce noise levels at various seismic stations where multiple high‐quality sensors have been deployed. Such small arrays may also provide a solution to improving long‐period noise levels at Global Seismographic Network stations.

  10. Compressed air noise reductions from using advanced air gun nozzles in research and development environments.

    Science.gov (United States)

    Prieve, Kurt; Rice, Amanda; Raynor, Peter C

    2017-08-01

    The aims of this study were to evaluate sound levels produced by compressed air guns in research and development (R&D) environments, replace conventional air gun models with advanced noise-reducing air nozzles, and measure changes in sound levels to assess the effectiveness of the advanced nozzles as engineering controls for noise. Ten different R&D manufacturing areas that used compressed air guns were identified and included in the study. A-weighted sound level and Z-weighted octave band measurements were taken simultaneously using a single instrument. In each area, three sets of measurements, each lasting for 20 sec, were taken 1 m away and perpendicular to the air stream of the conventional air gun while a worker simulated typical air gun work use. Two different advanced noise-reducing air nozzles were then installed. Sound level and octave band data were collected for each of these nozzles using the same methods as for the original air guns. Both of the advanced nozzles provided sound level reductions of about 7 dBA, on average. The highest noise reductions measured were 17.2 dBA for one model and 17.7 dBA for the other. In two areas, the advanced nozzles yielded no sound level reduction, or they produced small increases in sound level. The octave band data showed strong similarities in sound level among all air gun nozzles within the 10-1,000 Hz frequency range. However, the advanced air nozzles generally had lower noise contributions in the 1,000-20,000 Hz range. The observed decreases at these higher frequencies caused the overall sound level reductions that were measured. Installing new advanced noise-reducing air nozzles can provide large sound level reductions in comparison to existing conventional nozzles, which has direct benefit for hearing conservation efforts.

  11. RANS Analyses of Turbofan Nozzles with Internal Wedge Deflectors for Noise Reduction

    Science.gov (United States)

    DeBonis, James R.

    2009-01-01

    Computational fluid dynamics (CFD) was used to evaluate the flow field and thrust performance of a promising concept for reducing the noise at take-off of dual-stream turbofan nozzles. The concept, offset stream technology, reduces the jet noise observed on the ground by diverting (offsetting) a portion of the fan flow below the core flow, thickening and lengthening this layer between the high-velocity core flow and the ground observers. In this study a wedge placed in the internal fan stream is used as the diverter. Wind, a Reynolds averaged Navier-Stokes (RANS) code, was used to analyze the flow field of the exhaust plume and to calculate nozzle performance. Results showed that the wedge diverts all of the fan flow to the lower side of the nozzle, and the turbulent kinetic energy on the observer side of the nozzle is reduced. This reduction in turbulent kinetic energy should correspond to a reduction in noise. However, because all of the fan flow is diverted, the upper portion of the core flow is exposed to the freestream, and the turbulent kinetic energy on the upper side of the nozzle is increased, creating an unintended noise source. The blockage due to the wedge reduces the fan mass flow proportional to its blockage, and the overall thrust is consequently reduced. The CFD predictions are in very good agreement with experimental flow field data, demonstrating that RANS CFD can accurately predict the velocity and turbulent kinetic energy fields. While this initial design of a large scale wedge nozzle did not meet noise reduction or thrust goals, this study identified areas for improvement and demonstrated that RANS CFD can be used to improve the concept.

  12. Reward Pays the Cost of Noise Reduction in Motor and Cognitive Control.

    Science.gov (United States)

    Manohar, Sanjay G; Chong, Trevor T-J; Apps, Matthew A J; Batla, Amit; Stamelou, Maria; Jarman, Paul R; Bhatia, Kailash P; Husain, Masud

    2015-06-29

    Speed-accuracy trade-off is an intensively studied law governing almost all behavioral tasks across species. Here we show that motivation by reward breaks this law, by simultaneously invigorating movement and improving response precision. We devised a model to explain this paradoxical effect of reward by considering a new factor: the cost of control. Exerting control to improve response precision might itself come at a cost--a cost to attenuate a proportion of intrinsic neural noise. Applying a noise-reduction cost to optimal motor control predicted that reward can increase both velocity and accuracy. Similarly, application to decision-making predicted that reward reduces reaction times and errors in cognitive control. We used a novel saccadic distraction task to quantify the speed and accuracy of both movements and decisions under varying reward. Both faster speeds and smaller errors were observed with higher incentives, with the results best fitted by a model including a precision cost. Recent theories consider dopamine to be a key neuromodulator in mediating motivational effects of reward. We therefore examined how Parkinson's disease (PD), a condition associated with dopamine depletion, alters the effects of reward. Individuals with PD showed reduced reward sensitivity in their speed and accuracy, consistent in our model with higher noise-control costs. Including a cost of control over noise explains how reward may allow apparent performance limits to be surpassed. On this view, the pattern of reduced reward sensitivity in PD patients can specifically be accounted for by a higher cost for controlling noise. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

  13. Aeroacoustic characteristics and noise reduction of a centrifugal fan for a vacuum cleaner

    Energy Technology Data Exchange (ETDEWEB)

    Jeon, Wan Ho; Rew, Ho Seon; Kim, Chang Joon [LG Electronics, Seoul (Korea, Republic of)

    2004-02-01

    The aeroacoustic characteristics of a centrifugal fan for a vacuum cleaner and its noise reduction method are studied in this paper. The major noise source of a vacuum cleaner is the centrifugal fan. The impeller of the fan rotates at over 30000 rpm, and generates very high-level noise. It was revealed that the dominant noise source is the aerodynamic interaction between the rotating impeller and stationary diffuser. The directivity of acoustic pressure showed that most of the noise propagates backward direction of the fan-motor assembly. In order to reduce the high tonal sound generated from the aerodynamic interaction, unevenly pitched impeller and diffuser, and tapered impeller designs were proposed and experiments were performed. Uneven pitch design of the impeller changes the sound quality while the overall Sound Power Level (SPL) and the performance remains similar. The effect of the tapered design of impeller was evaluated. The trailing edge of the tapered fan is inclined. This reduces the flow interaction between the rotating impeller and the stationary diffuser because of some phase shifts. The static efficiency of the new impeller design is slightly lower than the previous design. However, the overall SPL is reduced by about 4 dB(A). The SPL of the fundamental Blade Passing Frequency (BPF) is reduced by about 6 dB(A) and the 2{sup nd} BPF is reduced about 20 dB(A). The vacuum cleaner with the tapered impeller design produces lower noise level than the previous one, and the strong tonal sound was dramatically reduced.

  14. Aeroacoustic characteristics and noise reduction of a centrifugal fan for a vacuum cleaner

    International Nuclear Information System (INIS)

    Jeon, Wan Ho; Rew, Ho Seon; Kim, Chang Joon

    2004-01-01

    The aeroacoustic characteristics of a centrifugal fan for a vacuum cleaner and its noise reduction method are studied in this paper. The major noise source of a vacuum cleaner is the centrifugal fan. The impeller of the fan rotates at over 30000 rpm, and generates very high-level noise. It was revealed that the dominant noise source is the aerodynamic interaction between the rotating impeller and stationary diffuser. The directivity of acoustic pressure showed that most of the noise propagates backward direction of the fan-motor assembly. In order to reduce the high tonal sound generated from the aerodynamic interaction, unevenly pitched impeller and diffuser, and tapered impeller designs were proposed and experiments were performed. Uneven pitch design of the impeller changes the sound quality while the overall Sound Power Level (SPL) and the performance remains similar. The effect of the tapered design of impeller was evaluated. The trailing edge of the tapered fan is inclined. This reduces the flow interaction between the rotating impeller and the stationary diffuser because of some phase shifts. The static efficiency of the new impeller design is slightly lower than the previous design. However, the overall SPL is reduced by about 4 dB(A). The SPL of the fundamental Blade Passing Frequency (BPF) is reduced by about 6 dB(A) and the 2 nd BPF is reduced about 20 dB(A). The vacuum cleaner with the tapered impeller design produces lower noise level than the previous one, and the strong tonal sound was dramatically reduced

  15. Fan interaction noise reduction using a wake generator: experiments and computational aeroacoustics

    Science.gov (United States)

    Polacsek, C.; Desbois-Lavergne, F.

    2003-08-01

    A control grid (wake generator) aimed at reducing rotor-stator interaction modes in fan engines when mounted upstream of the rotor has been studied here. This device complements other active noise control systems currently proposed. The compressor model of the instrumented ONERA CERF-rig is used to simulate suitable conditions. The design of the grid is drafted out using semi-empirical models for wake and potential flow, and experimentally achieved. Cylindrical rods are able to generate a spinning mode of the same order and similar level as the interaction mode. Mounting the rods on a rotating ring allows for adjusting the phase of the control mode so that an 8 dB sound pressure level (SPL) reduction at the blade passing frequency is achieved when the two modes are out of phase. Experimental results are assessed by a numerical approach using computational fluid dynamics (CFD). A Reynolds averaged Navier-Stokes 2-D solver, developed at ONERA, is used to provide the unsteady force components on blades and vanes required for acoustics. The loading noise source term of the Ffowcs Williams and Hawkings equation is used to model the interaction noise between the sources, and an original coupling to a boundary element method (BEM) code is realized to take account of the inlet geometry effects on acoustic in-duct propagation. Calculations using the classical analytical the Green function of an infinite annular duct are also addressed. Simple formulations written in the frequency domain and expanded into modes are addressed and used to compute an in-duct interaction mode and to compare with the noise reduction obtained during the tests. A fairly good agreement between predicted and measured SPL is found when the inlet geometry effects are part of the solution (by coupling with the BEM). Furthermore, computed aerodynamic penalties due to the rods are found to be negligible. These results partly validate the computation chain and highlight the potential of the wake generator

  16. Noise reduction by sparse representation in learned dictionaries for application to blind tip reconstruction problem

    International Nuclear Information System (INIS)

    Jóźwiak, Grzegorz

    2017-01-01

    Scanning probe microscopy (SPM) is a well known tool used for the investigation of phenomena in objects in the nanometer size range. However, quantitative results are limited by the size and the shape of the nanoprobe used in experiments. Blind tip reconstruction (BTR) is a very popular method used to reconstruct the upper boundary on the shape of the probe. This method is known to be very sensitive to all kinds of interference in the atomic force microscopy (AFM) image. Due to mathematical morphology calculus, the interference makes the BTR results biased rather than randomly disrupted. For this reason, the careful choice of methods used for image enhancement and denoising, as well as the shape of a calibration sample are very important. In the paper, the results of thorough investigations on the shape of a calibration standard are shown. A novel shape is proposed and a tool for the simulation of AFM images of this calibration standard was designed. It was shown that careful choice of the initial tip allows us to use images of hole structures to blindly reconstruct the shape of a probe. The simulator was used to test the impact of modern filtration algorithms on the BTR process. These techniques are based on sparse approximation with function dictionaries learned on the basis of an image itself. Various learning algorithms and parameters were tested to determine the optimal combination for sparse representation. It was observed that the strong reduction of noise does not guarantee strong reduction in reconstruction errors. It seems that further improvements will be possible by the combination of BTR and a noise reduction procedure. (paper)

  17. Usefulness of acoustic noise reduction in brain MRI using Quiet-T{sub 2}-

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Se Jy [Dept. of Medical science Graduate school, Chonnam National University, Gwangju (Korea, Republic of); Kim, Young Keun [Dept. of Radiotechnology, Gwangju Health university, Gwangju (Korea, Republic of)

    2016-03-15

    Acoustic noise during magnetic resonance imaging (MRI) is the main source for patient discomfort. we report our preliminary experience with this technique in neuroimaging with regard to subjective and objective noise levels and image quality. 60 patients(29 males, 31 females, average age of 60.1) underwent routine brain MRI with 3.0 Tesla (MAGNETOM Tim Trio; Siemens, Germany) system and 12-channel head coil. Q-T{sub 2} and T{sub 2} sequence were performed. Measurement of sound pressure levels (SPL) and heart rate on Q-T{sub 2} and T{sub 2} was performed respectively. Quantitative analysis was carried out by measuring the SNR, CNR, and SIR values of Q-T{sub 2}, T{sub 2} and a statistical analysis was performed using independent sample T-test. Qualitative analysis was evaluated by the eyes for the overall quality image of Q-T{sub 2} and T{sub 2}. A 5-point evaluation scale was used, including excellent(5), good(4), fair(3), poor(2), and unacceptable(1). The average noise and peak noise decreased by 15dBA and 10dBA on T2 and Q-T2 test. Also, the average value of heartbeat rate was lower in Q-T2 for 120 seconds in each test, but there was no statistical significance. The quantitative analysis showed that there was no significant difference between CNR and SIR, and there was a significant difference (p<0.05) as SNR had a lower average value on Q-T{sub 2}. According to the qualitative analysis, the overall quality image of 59 case T{sub 2} and Q-T{sub 2} was evaluated as excellent at 5 points, and 1 case was evaluated as good at 4 points due to a motion artifact. Q-T{sub 2} is a promising technique for acoustic noise reduction and improved patient comfort.

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

    KAUST Repository

    Xie, Qing; Zhang, Xiangliang; Li, Zhixu; Zhou, Xiaofang

    2016-01-01

    The problem we aim to address is the optimization of cost management for executing multiple continuous queries on data streams, where each query is defined by several filters, each of which monitors certain status of the data stream. Specially

  19. Independent component analysis based filtering for penumbral imaging

    International Nuclear Information System (INIS)

    Chen Yenwei; Han Xianhua; Nozaki, Shinya

    2004-01-01

    We propose a filtering based on independent component analysis (ICA) for Poisson noise reduction. In the proposed filtering, the image is first transformed to ICA domain and then the noise components are removed by a soft thresholding (shrinkage). The proposed filter, which is used as a preprocessing of the reconstruction, has been successfully applied to penumbral imaging. Both simulation results and experimental results show that the reconstructed image is dramatically improved in comparison to that without the noise-removing filters

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

    Science.gov (United States)

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

    2018-02-01

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

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

    Science.gov (United States)

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

    2016-07-12

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

  2. Noise Reduction of Steel Cord Conveyor Belt Defect Electromagnetic Signal by Combined Use of Improved Wavelet and EMD

    Directory of Open Access Journals (Sweden)

    Hong-Wei Ma

    2016-09-01

    Full Text Available In order to reduce the noise of a defect electromagnetic signal of the steel cord conveyor belt used in coal mines, a new signal noise reduction method by combined use of the improved threshold wavelet and Empirical Mode Decomposition (EMD is proposed. Firstly, the denoising method based on the improved threshold wavelet is applied to reduce the noise of a defect electromagnetic signal obtained by an electromagnetic testing system. Then, the EMD is used to decompose the denoised signal and then the effective Intrinsic Mode Function (IMF is extracted by the dominant eigenvalue strategy. Finally, the signal reconstruction is carried out by utilizing the obtained IMF. In order to verify the proposed noise reduction method, the experiments are carried out in two cases including the defective joint and steel wire rope break. The experimental results show that the proposed method in this paper obtains the higher Signal to Noise Ratio (SNR for the defect electromagnetic signal noise reduction of steel cord conveyor belts.

  3. Adaptive elimination of optical fiber transmission noise in fiber ocean bottom seismic system

    Science.gov (United States)

    Zhong, Qiuwen; Hu, Zhengliang; Cao, Chunyan; Dong, Hongsheng

    2017-10-01

    In this paper, a pressure and acceleration insensitive reference Interferometer is used to obtain laser and public noise introduced by transmission fiber and laser. By using direct subtraction and adaptive filtering, this paper attempts to eliminate and estimation the transmission noise of sensing probe. This paper compares the noise suppression effect of four methods, including the direct subtraction (DS), the least mean square error adaptive elimination (LMS), the normalized least mean square error adaptive elimination (NLMS) and the least square (RLS) adaptive filtering. The experimental results show that the noise reduction effect of RLS and NLMS are almost the same, better than LMS and DS, which can reach 8dB (@100Hz). But considering the workload, RLS is not conducive to the real-time operating system. When it comes to the same treatment effect, the practicability of NLMS is higher than RLS. The noise reduction effect of LMS is slightly worse than that of RLS and NLMS, about 6dB (@100Hz), but its computational complexity is small, which is beneficial to the real time system implementation. It can also be seen that the DS method has the least amount of computational complexity, but the noise suppression effect is worse than that of the adaptive filter due to the difference of the noise amplitude between the RI and the SI, only 4dB (@100Hz) can be reached. The adaptive filter can basically eliminate the influence of the transmission noise, and the simulation signal of the sensor is kept intact.

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

    Science.gov (United States)

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

    2012-04-01

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

  5. Innovative Technique for Noise Reduction in Spacecraft Doppler Tracking for Planetary Interior Studies

    Science.gov (United States)

    Notaro, V.; Armstrong, J. W.; Asmar, S.; Di Ruscio, A.; Iess, L.; Mariani, M., Jr.

    2017-12-01

    Precise measurements of spacecraft range rate, enabled by two-way microwave links, are used in radio science experiments for planetary geodesy including the determination of planetary gravitational fields for the purpose of modeling the interior structure. The final accuracies in the estimated gravity harmonic coefficients depend almost linearly on the Doppler noise in the link. We ran simulations to evaluate the accuracy improvement attainable in the estimation of the gravity harmonic coefficients of Venus (with a representative orbiter) and Mercury (with the BepiColombo spacecraft), using our proposed innovative noise-cancellation technique. We showed how the use of an additional, smaller and stiffer, receiving-only antenna could reduce the leading noise sources in a Ka-band two-way link such as tropospheric and antenna mechanical noises. This is achieved through a suitable linear combination (LC) of Doppler observables collected at the two antennas at different times. In our simulations, we considered a two-way link either from NASA's DSS 25 antenna in California or from ESA's DSA-3 antenna in Malargüe (Argentina). Moreover, we selected the 12-m Atacama Pathfinder EXperiment (APEX) in Chile as the three-way antenna and developed its tropospheric noise model using available atmospheric data and mechanical stability specifications. For an 8-hour Venus orbiter tracking pass in Chajnantor's winter/night conditions, the accuracy of the simulated LC Doppler observable at 10-s integration time is 6 mm/s, to be compared to 23 mm/s for the two-way link. For BepiColombo, we obtained 16.5 mm/s and 35 mm/s, respectively for the LC and two-way links. The benefits are even larger at longer time scales. Numerical simulations indicate that such noise reduction would provide significant improvements in the determination of Venus's and Mercury's gravity field coefficients. If implemented, this noise-reducing technique will be valuable for planetary geodesy missions, where the

  6. Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

    Science.gov (United States)

    Lai, Ying-Hui; Tsao, Yu; Lu, Xugang; Chen, Fei; Su, Yu-Ting; Chen, Kuang-Chao; Chen, Yu-Hsuan; Chen, Li-Ching; Po-Hung Li, Lieber; Lee, Chin-Hui

    2018-01-20

    We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI) recipients. The deep learning-based NR approach used in this study consists of two modules: noise classifier (NC) and deep denoising autoencoder (DDAE), thus termed (NC + DDAE). In a series of comprehensive experiments, we conduct qualitative and quantitative analyses on the NC module and the overall NC + DDAE approach. Moreover, we evaluate the speech recognition performance of the NC + DDAE NR and classical single-microphone NR approaches for Mandarin-speaking CI recipients under different noisy conditions. The testing set contains Mandarin sentences corrupted by two types of maskers, two-talker babble noise, and a construction jackhammer noise, at 0 and 5 dB SNR levels. Two conventional NR techniques and the proposed deep learning-based approach are used to process the noisy utterances. We qualitatively compare the NR approaches by the amplitude envelope and spectrogram plots of the processed utterances. Quantitative objective measures include (1) normalized covariance measure to test the intelligibility of the utterances processed by each of the NR approaches; and (2) speech recognition tests conducted by nine Mandarin-speaking CI recipients. These nine CI recipients use their own clinical speech processors during testing. The experimental results of objective evaluation and listening test indicate that under challenging listening conditions, the proposed NC + DDAE NR approach yields higher intelligibility scores than the two compared classical NR techniques, under both matched and mismatched training-testing conditions. When compared to the two well-known conventional NR techniques under challenging listening condition, the proposed NC + DDAE NR approach has superior noise suppression capabilities and gives less distortion

  7. New image-processing and noise-reduction software reduces radiation dose during complex endovascular procedures.

    Science.gov (United States)

    Kirkwood, Melissa L; Guild, Jeffrey B; Arbique, Gary M; Tsai, Shirling; Modrall, J Gregory; Anderson, Jon A; Rectenwald, John; Timaran, Carlos

    2016-11-01

    A new proprietary image-processing system known as AlluraClarity, developed by Philips Healthcare (Best, The Netherlands) for radiation-based interventional procedures, claims to lower radiation dose while preserving image quality using noise-reduction algorithms. This study determined whether the surgeon and patient radiation dose during complex endovascular procedures (CEPs) is decreased after the implementation of this new operating system. Radiation dose to operators, procedure type, reference air kerma, kerma area product, and patient body mass index were recorded during CEPs on two Philips Allura FD 20 fluoroscopy systems with and without Clarity. Operator dose during CEPs was measured using optically stimulable, luminescent nanoDot (Landauer Inc, Glenwood, Ill) detectors placed outside the lead apron at the left upper chest position. nanoDots were read using a microStar ii (Landauer Inc) medical dosimetry system. For the CEPs in the Clarity group, the radiation dose to surgeons was also measured by the DoseAware (Philips Healthcare) personal dosimetry system. Side-by-side measurements of DoseAware and nanoDots allowed for cross-calibration between systems. Operator effective dose was determined using a modified Niklason algorithm. To control for patient size and case complexity, the average fluoroscopy dose rate and the dose per radiographic frame were adjusted for body mass index differences and then compared between the groups with and without Clarity by procedure. Additional factors, for example, physician practice patterns, that may have affected operator dose were inferred by comparing the ratio of the operator dose to procedural kerma area product with and without Clarity. A one-sided Wilcoxon rank sum test was used to compare groups for radiation doses, reference air kermas, and operating practices for each procedure type. The analysis included 234 CEPs; 95 performed without Clarity and 139 with Clarity. Practice patterns of operators during

  8. Directional Processing and Noise Reduction in Hearing Aids: Individual and Situational Influences on Preferred Setting.

    Science.gov (United States)

    Neher, Tobias; Wagener, Kirsten C; Fischer, Rosa-Linde

    2016-09-01

    A better understanding of individual differences in hearing aid (HA) outcome is a prerequisite for more personalized HA fittings. Currently, knowledge of how different user factors relate to response to directional processing (DIR) and noise reduction (NR) is sparse. To extend a recent study linking preference for DIR and NR to pure-tone average hearing thresholds (PTA) and cognitive factors by investigating if (1) equivalent links exist for different types of DIR and NR, (2) self-reported noise sensitivity and personality can account for additional variability in preferred DIR and NR settings, and (3) spatial target speech configuration interacts with individual DIR preference. Using a correlational study design, overall preference for different combinations of DIR and NR programmed into a commercial HA was assessed in a complex speech-in-noise situation and related to PTA, cognitive function, and different personality traits. Sixty experienced HA users aged 60-82 yr with controlled variation in PTA and working memory capacity took part in this study. All of them had participated in the earlier study, as part of which they were tested on a measure of "executive control" tapping into cognitive functions such as working memory, mental flexibility, and selective attention. Six HA settings based on unilateral (within-device) or bilateral (across-device) DIR combined with inactive, moderate, or strong single-microphone NR were programmed into a pair of behind-the-ear HAs together with individually prescribed amplification. Overall preference was assessed using a free-field simulation of a busy cafeteria situation with either a single frontal talker or two talkers at ±30° azimuth as the target speech. In addition, two questionnaires targeting noise sensitivity and the "Big Five" personality traits were administered. Data were analyzed using multiple regression analyses and repeated-measures analyses of variance with a focus on potential interactions between the HA

  9. Do hearing loss and cognitive function modulate benefit from different binaural noise-reduction settings?

    Science.gov (United States)

    Neher, Tobias; Grimm, Giso; Hohmann, Volker; Kollmeier, Birger

    2014-01-01

    Although previous research indicates that cognitive skills influence benefit from different types of hearing aid algorithms, comparatively little is known about the role of, and potential interaction with, hearing loss. This holds true especially for noise reduction (NR) processing. The purpose of the present study was thus to explore whether degree of hearing loss and cognitive function modulate benefit from different binaural NR settings based on measures of speech intelligibility, listening effort, and overall preference. Forty elderly listeners with symmetrical sensorineural hearing losses in the mild to severe range participated. They were stratified into four age-matched groups (with n = 10 per group) based on their pure-tone average hearing losses and their performance on a visual measure of working memory (WM) capacity. The algorithm under consideration was a binaural coherence-based NR scheme that suppressed reverberant signal components as well as diffuse background noise at mid to high frequencies. The strength of the applied processing was varied from inactive to strong, and testing was carried out across a range of fixed signal-to-noise ratios (SNRs). Potential benefit was assessed using a dual-task paradigm combining speech recognition with a visual reaction time (VRT) task indexing listening effort. Pairwise preference judgments were also collected. All measurements were made using headphone simulations of a frontal speech target in a busy cafeteria. Test-retest data were gathered for all outcome measures. Analysis of the test-retest data showed all data sets to be reliable. Analysis of the speech scores showed that, for all groups, speech recognition was unaffected by moderate NR processing, whereas strong NR processing reduced intelligibility by about 5%. Analysis of the VRT scores revealed a similar data pattern. That is, while moderate NR did not affect VRT performance, strong NR impaired the performance of all groups slightly. Analysis of the

  10. Adaptive weighted anisotropic diffusion for computed tomography denoising

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Zhi; Silver, Michael D. [Toshiba Medical Research Institute USA, Inc., Vernon Hills, IL (United States); Noshi, Yasuhiro [Toshiba Medical System Corporation, Tokyo (Japan)

    2011-07-01

    With increasing awareness of radiation safety, dose reduction has become an important task of modern CT system development. This paper proposes an adaptive weighted anisotropic diffusion method and an adaptive weighted sharp source anisotropic diffusion method as image domain filters to potentially help dose reduction. Different from existing anisotropic diffusion methods, the proposed methods incorporate an edge-sensitive adaptive source term as part of the diffusion iteration. It provides better edge and detail preservation. Visual evaluation showed that the new methods can reduce noise substantially without apparent edge and detail loss. The quantitative evaluations also showed over 50% of noise reduction in terms of noise standard deviations, which is equivalent to over 75% of dose reduction for a normal dose image quality. (orig.)

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

    Science.gov (United States)

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

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

  12. Filter-Adapted Fluorescent In Situ Hybridization (FA-FISH) for Filtration-Enriched Circulating Tumor Cells.

    Science.gov (United States)

    Oulhen, Marianne; Pailler, Emma; Faugeroux, Vincent; Farace, Françoise

    2017-01-01

    Circulating tumor cells (CTCs) may represent an easily accessible source of tumor material to assess genetic aberrations such as gene-rearrangements or gene-amplifications and screen cancer patients eligible for targeted therapies. As the number of CTCs is a critical parameter to identify such biomarkers, we developed fluorescent in situ hybridization (FISH) for CTCs enriched on filters (filter-adapted-FISH, FA-FISH). Here, we describe the FA-FISH protocol, the combination of immunofluorescent staining (DAPI/CD45) and FA-FISH techniques, as well as the semi-automated microscopy method that we developed to improve the feasibility and reliability of FISH analyses in filtration-enriched CTC.

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

    Directory of Open Access Journals (Sweden)

    Baofeng Wang

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

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

    Science.gov (United States)

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

    2017-01-01

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

  15. Poisson noise reduction from X-ray images by region classification ...

    Indian Academy of Sciences (India)

    Thakur Kirti

    means Poisson noise filter which is one of the current state-of-the-art methods. Benefits of the proposed ... This modality is used to detect fractures in bones, tumours, cough or ..... metric peak signal to noise ratio (PSNR). It is observed from ...

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

    Digital Repository Service at National Institute of Oceanography (India)

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

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

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

    Digital Repository Service at National Institute of Oceanography (India)

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

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

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

    Directory of Open Access Journals (Sweden)

    Xin Li

    2016-02-01

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

  19. Online Reduction of Artifacts in EEG of Simultaneous EEG-fMRI Using Reference Layer Adaptive Filtering (RLAF).

    Science.gov (United States)

    Steyrl, David; Krausz, Gunther; Koschutnig, Karl; Edlinger, Günter; Müller-Putz, Gernot R

    2018-01-01

    Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow us to study the active human brain from two perspectives concurrently. Signal processing based artifact reduction techniques are mandatory for this, however, to obtain reasonable EEG quality in simultaneous EEG-fMRI. Current artifact reduction techniques like average artifact subtraction (AAS), typically become less effective when artifact reduction has to be performed on-the-fly. We thus present and evaluate a new technique to improve EEG quality online. This technique adds up with online AAS and combines a prototype EEG-cap for reference recordings of artifacts, with online adaptive filtering and is named reference layer adaptive filtering (RLAF). We found online AAS + RLAF to be highly effective in improving EEG quality. Online AAS + RLAF outperformed online AAS and did so in particular online in terms of the chosen performance metrics, these being specifically alpha rhythm amplitude ratio between closed and opened eyes (3-45% improvement), signal-to-noise-ratio of visual evoked potentials (VEP) (25-63% improvement), and VEPs variability (16-44% improvement). Further, we found that EEG quality after online AAS + RLAF is occasionally even comparable with the offline variant of AAS at a 3T MRI scanner. In conclusion RLAF is a very effective add-on tool to enable high quality EEG in simultaneous EEG-fMRI experiments, even when online artifact reduction is necessary.

  20. Improvement of Detection of Hypoattenuation in Acute Ischemic Stroke in Unenhanced Computed Tomography Using an Adaptive Smoothing Filter

    International Nuclear Information System (INIS)

    Takahashi, N.; Lee, Y.; Tsai, D. Y.; Ishii, K.; Kinoshita, T.; Tamura, H.; K imura, M.

    2008-01-01

    Background: Much attention has been directed toward identifying early signs of cerebral ischemia on computed tomography (CT) images. Hypoattenuation of ischemic brain parenchyma has been found to be the most frequent early sign. Purpose: To evaluate the effect of a previously proposed adaptive smoothing filter for improving detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images. Material and Methods: Twenty-six patients with parenchymal hypoattenuation and 49 control subjects without hypoattenuation were retrospectively selected in this study. The adaptive partial median filter (APMF) designed for improving detectability of hypoattenuation areas on unenhanced CT images was applied. Seven radiologists, including four certified radiologists and three radiology residents, indicated their confidence level regarding the presence (or absence) of hypoattenuation on CT images, first without and then with the APMF processed images. Their performances without and with the APMF processed images were evaluated by receiver operating characteristic (ROC) analysis. Results: The mean areas under the ROC curves (AUC) for all observers increased from 0.875 to 0.929 (P=0.002) when the radiologists observed with the APMF processed images. The mean sensitivity in the detection of hypoattenuation significantly improved, from 69% (126 of 182 observations) to 89% (151 of 182 observations), when employing the APMF (P=0.012). The specificity, however, was unaffected by the APMF (P=0.41). Conclusion: The APMF has the potential to improve the detection of parenchymal hypoattenuation of acute ischemic stroke on unenhanced CT images

  1. A FPGA-based Fast Converging Digital Adaptive Filter for Real-time RFI Mitigation on Ground Based Radio Telescopes

    Science.gov (United States)

    Finger, R.; Curotto, F.; Fuentes, R.; Duan, R.; Bronfman, L.; Li, D.

    2018-02-01

    Radio Frequency Interference (RFI) is a growing concern in the radio astronomy community. Single-dish telescopes are particularly susceptible to RFI. Several methods have been developed to cope with RF-polluted environments, based on flagging, excision, and real-time blanking, among others. All these methods produce some degree of data loss or require assumptions to be made on the astronomical signal. We report the development of a real-time, digital adaptive filter implemented on a Field Programmable Gate Array (FPGA) capable of processing 4096 spectral channels in a 1 GHz of instantaneous bandwidth. The filter is able to cancel a broad range of interference signals and quickly adapt to changes on the RFI source, minimizing the data loss without any assumption on the astronomical or interfering signal properties. The speed of convergence (for a decrease to a 1%) was measured to be 208.1 μs for a broadband noise-like RFI signal and 125.5 μs for a multiple-carrier RFI signal recorded at the FAST radio telescope.

  2. Feedforward compensation control of rotor imbalance for high-speed magnetically suspended centrifugal compressors using a novel adaptive notch filter

    Science.gov (United States)

    Zheng, Shiqiang; Feng, Rui

    2016-03-01

    This paper introduces a feedforward control strategy combined with a novel adaptive notch filter to solve the problem of rotor imbalance in high-speed Magnetically Suspended Centrifugal Compressors (MSCCs). Unbalance vibration force of rotor in MSCC is mainly composed of current stiffness force and displacement stiffness force. In this paper, the mathematical model of the unbalance vibration with the proportional-integral-derivative (PID) control laws is presented. In order to reduce the unbalance vibration, a novel adaptive notch filter is proposed to identify the synchronous frequency displacement of the rotor as a compensation signal to eliminate the current stiffness force. In addition, a feedforward channel from position component to control output is introduced to compensate displacement stiffness force to achieve a better performance. A simplified inverse model of power amplifier is included in the feedforward channel to reject the degrade performance caused by its low-pass characteristic. Simulation and experimental results on a MSCC demonstrate a significant effect on the synchronous vibration suppression of the magnetically suspended rotor at a high speed.

  3. Potentials and limitations of adaptive plasticity in filtering screen morphology of Daphnia (Crustacea: Cladocera)

    Czech Academy of Sciences Publication Activity Database

    Macháček, Jiří; Seďa, Jaromír

    2016-01-01

    Roč. 38, č. 5 (2016), s. 1269-1280 ISSN 0142-7873 R&D Projects: GA ČR(CZ) GA206/09/1325; GA ČR(CZ) GA15-24309S Institutional support: RVO:60077344 Keywords : Daphnia * filtering screen morphology * transgenerational plasticity * ontogenetic plasticity * taxonspecific trait Subject RIV: EH - Ecology, Behaviour Impact factor: 1.983, year: 2016

  4. The impact of a noise reduction quality improvement project upon sound levels in the open-unit-design neonatal intensive care unit.

    Science.gov (United States)

    Liu, W F

    2010-07-01

    To decrease measured sound levels in the neonatal intensive care unit through implementation of human factor and minor design modification strategies. Prospective time series. Two open-unit-design neonatal centers. Implementation of a coordinated program of noise reduction strategies did not result in any measurable improvement in levels of loudness or quiet. Two centers, using primarily human behavior noise reduction strategies, were unable to demonstrate measurable improvements in sound levels within the occupied open-unit-design neonatal intensive care unit.

  5. Adaptive Mean and Trend Removal of Heart Rate Variability Using Kalman Filtering

    National Research Council Canada - National Science Library

    Schloegl, A

    2001-01-01

    Analysis of heart rate van ability requires the calculation of the mean heart rate, Adaptive methods are important for online and real-time parameter estimation, In this paper we demonstrate the use...

  6. Signal Enhancement with Variable Span Linear Filters

    DEFF Research Database (Denmark)

    Benesty, Jacob; Christensen, Mads Græsbøll; Jensen, Jesper Rindom

    . Variable span filters combine the ideas of optimal linear filters with those of subspace methods, as they involve the joint diagonalization of the correlation matrices of the desired signal and the noise. The book shows how some well-known filter designs, e.g. the minimum distortion, maximum signal...... the time and STFT domains, and, lastly, in time-domain binaural enhancement. In these contexts, the properties of these filters are analyzed in terms of their noise reduction capabilities and desired signal distortion, and the analyses are validated and further explored in simulations....

  7. A Two-Microphone Noise Reduction System for Cochlear Implant Users with Nearby Microphones—Part II: Performance Evaluation

    Directory of Open Access Journals (Sweden)

    Rudolf Häusler

    2008-06-01

    Full Text Available Users of cochlear implants (auditory aids, which stimulate the auditory nerve electrically at the inner ear often suffer from poor speech understanding in noise. We evaluate a small (intermicrophone distance 7 mm and computationally inexpensive adaptive noise reduction system suitable for behind-the-ear cochlear implant speech processors. The system is evaluated in simulated and real, anechoic and reverberant environments. Results from simulations show improvements of 3.4 to 9.3 dB in signal to noise ratio for rooms with realistic reverberation and more than 18 dB under anechoic conditions. Speech understanding in noise is measured in 6 adult cochlear implant users in a reverberant room, showing average improvements of 7.9–9.6 dB, when compared to a single omnidirectional microphone or 1.3–5.6 dB, when compared to a simple directional two-microphone device. Subjective evaluation in a cafeteria at lunchtime shows a preference of the cochlear implant users for the evaluated device in terms of speech understanding and sound quality.

  8. Brain MR Image Restoration Using an Automatic Trilateral Filter With GPU-Based Acceleration.

    Science.gov (United States)

    Chang, Herng-Hua; Li, Cheng-Yuan; Gallogly, Audrey Haihong

    2018-02-01

    Noise reduction in brain magnetic resonance (MR) images has been a challenging and demanding task. This study develops a new trilateral filter that aims to achieve robust and efficient image restoration. Extended from the bilateral filter, the proposed algorithm contains one additional intensity similarity funct-ion, which compensates for the unique characteristics of noise in brain MR images. An entropy function adaptive to intensity variations is introduced to regulate the contributions of the weighting components. To hasten the computation, parallel computing based on the graphics processing unit (GPU) strategy is explored with emphasis on memory allocations and thread distributions. To automate the filtration, image texture feature analysis associated with machine learning is investigated. Among the 98 candidate features, the sequential forward floating selection scheme is employed to acquire the optimal texture features for regularization. Subsequently, a two-stage classifier that consists of support vector machines and artificial neural networks is established to predict the filter parameters for automation. A speedup gain of 757 was reached to process an entire MR image volume of 256 × 256 × 256 pixels, which completed within 0.5 s. Automatic restoration results revealed high accuracy with an ensemble average relative error of 0.53 ± 0.85% in terms of the peak signal-to-noise ratio. This self-regulating trilateral filter outperformed many state-of-the-art noise reduction methods both qualitatively and quantitatively. We believe that this new image restoration algorithm is of potential in many brain MR image processing applications that require expedition and automation.

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

    Science.gov (United States)

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

    2018-07-15

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

  10. Fluctuations and information filtering in coupled populations of spiking neurons with adaptation.

    Science.gov (United States)

    Deger, Moritz; Schwalger, Tilo; Naud, Richard; Gerstner, Wulfram

    2014-12-01

    Finite-sized populations of spiking elements are fundamental to brain function but also are used in many areas of physics. Here we present a theory of the dynamics of finite-sized populations of spiking units, based on a quasirenewal description of neurons with adaptation. We derive an integral equation with colored noise that governs the stochastic dynamics of the population activity in response to time-dependent stimulation and calculate the spectral density in the asynchronous state. We show that systems of coupled populations with adaptation can generate a frequency band in which sensory information is preferentially encoded. The theory is applicable to fully as well as randomly connected networks and to leaky integrate-and-fire as well as to generalized spiking neurons with adaptation on multiple time scales.

  11. Noise reduction by support vector regression with a Ricker wavelet kernel

    International Nuclear Information System (INIS)

    Deng, Xiaoying; Yang, Dinghui; Xie, Jing

    2009-01-01

    We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR

  12. Noise reduction by support vector regression with a Ricker wavelet kernel

    Science.gov (United States)

    Deng, Xiaoying; Yang, Dinghui; Xie, Jing

    2009-06-01

    We propose a noise filtering technology based on the least-squares support vector regression (LS-SVR), to improve the signal-to-noise ratio (SNR) of seismic data. We modified it by using an admissible support vector (SV) kernel, namely the Ricker wavelet kernel, to replace the conventional radial basis function (RBF) kernel in seismic data processing. We investigated the selection of the regularization parameter for the LS-SVR and derived a concise selecting formula directly from the noisy data. We used the proposed method for choosing the regularization parameter which not only had the advantage of high speed but could also obtain almost the same effectiveness as an optimal parameter method. We conducted experiments using synthetic data corrupted by the random noise of different types and levels, and found that our method was superior to the wavelet transform-based approach and the Wiener filtering. We also applied the method to two field seismic data sets and concluded that it was able to effectively suppress the random noise and improve the data quality in terms of SNR.

  13. Simulation of modified hybrid noise reduction algorithm to enhance the speech quality

    International Nuclear Information System (INIS)

    Waqas, A.; Muhammad, T.; Jamal, H.

    2013-01-01

    Speech is the most essential method of correspondence of humankind. Cell telephony, portable hearing assistants and, hands free are specific provisions in this respect. The performance of these communication devices could be affected because of distortions which might augment them. There are two essential sorts of distortions that might be recognized, specifically: convolutive and additive noises. These mutilations contaminate the clean speech and make it unsatisfactory to human audiences i.e. perceptual value and intelligibility of speech signal diminishes. The objective of speech upgrade systems is to enhance the quality and understandability of speech to make it more satisfactory to audiences. This paper recommends a modified hybrid approach for single channel devices to process the noisy signals considering only the effect of background noises. It is a mixture of pre-processing relative spectral amplitude (RASTA) filter, which is approximated by a straight forward 4th order band-pass filter, and conventional minimum mean square error short time spectral amplitude (MMSE STSA85) estimator. To analyze the performance of the algorithm an objective parameter called Perceptual estimation of speech quality (PESQ) is measured. The results show that the modified algorithm performs well to remove the background noises. SIMULINK implementation is also performed and its profile report has been generated to observe the execution time. (author)

  14. Noise Reduction Effect of Multiple-Sampling-Based Signal-Readout Circuits for Ultra-Low Noise CMOS Image Sensors

    Directory of Open Access Journals (Sweden)

    Shoji Kawahito

    2016-11-01

    Full Text Available This paper discusses the noise reduction effect of multiple-sampling-based signal readout circuits for implementing ultra-low-noise image sensors. The correlated multiple sampling (CMS technique has recently become an important technology for high-gain column readout circuits in low-noise CMOS image sensors (CISs. This paper reveals how the column CMS circuits, together with a pixel having a high-conversion-gain charge detector and low-noise transistor, realizes deep sub-electron read noise levels based on the analysis of noise components in the signal readout chain from a pixel to the column analog-to-digital converter (ADC. The noise measurement results of experimental CISs are compared with the noise analysis and the effect of noise reduction to the sampling number is discussed at the deep sub-electron level. Images taken with three CMS gains of two, 16, and 128 show distinct advantage of image contrast for the gain of 128 (noise(median: 0.29 e−rms when compared with the CMS gain of two (2.4 e−rms, or 16 (1.1 e−rms.

  15. Elastomeric Structural Attachment Concepts for Aircraft Flap Noise Reduction - Challenges and Approaches to Hyperelastic Structural Modeling and Analysis

    Science.gov (United States)

    Sreekantamurthy, Thammaiah; Turner, Travis L.; Moore, James B.; Su, Ji

    2014-01-01

    Airframe noise is a significant part of the overall noise of transport aircraft during the approach and landing phases of flight. Airframe noise reduction is currently emphasized under the Environmentally Responsible Aviation (ERA) and Fixed Wing (FW) Project goals of NASA. A promising concept for trailing-edge-flap noise reduction is a flexible structural element or link that connects the side edges of the deployable flap to the adjacent main-wing structure. The proposed solution is distinguished by minimization of the span-wise extent of the structural link, thereby minimizing the aerodynamic load on the link structure at the expense of increased deformation requirement. Development of such a flexible structural link necessitated application of hyperelastic materials, atypical structural configurations and novel interface hardware. The resulting highly-deformable structural concept was termed the FLEXible Side Edge Link (FLEXSEL) concept. Prediction of atypical elastomeric deformation responses from detailed structural analysis was essential for evaluating feasible concepts that met the design constraints. The focus of this paper is to describe the many challenges encountered with hyperelastic finite element modeling and the nonlinear structural analysis of evolving FLEXSEL concepts. Detailed herein is the nonlinear analysis of FLEXSEL concepts that emerged during the project which include solid-section, foamcore, hollow, extended-span and pre-stressed concepts. Coupon-level analysis performed on elastomeric interface joints, which form a part of the FLEXSEL topology development, are also presented.

  16. Automatic identification and removal of ocular artifacts in EEG--improved adaptive predictor filtering for portable applications.

    Science.gov (United States)

    Zhao, Qinglin; Hu, Bin; Shi, Yujun; Li, Yang; Moore, Philip; Sun, Minghou; Peng, Hong

    2014-06-01

    Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

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

    Science.gov (United States)

    Feng, Yibo; Li, Xisheng; Zhang, Xiaojuan

    2015-05-13

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

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

    Directory of Open Access Journals (Sweden)

    Yibo Feng

    2015-05-01

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

  19. Improved prediction error filters for adaptive feedback cancellation in hearing aids

    DEFF Research Database (Denmark)

    Ngo, Kim; van Waterschoot, Toon; Christensen, Mads Græsbøll

    2013-01-01

    feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal...

  20. Detection of User Independent Single Trial ERPs in Brain Computer Interfaces: An Adaptive Spatial Filtering Approach

    DEFF Research Database (Denmark)

    Leza, Cristina; Puthusserypady, Sadasivan

    2017-01-01

    Brain Computer Interfaces (BCIs) use brain signals to communicate with the external world. The main challenges to address are speed, accuracy and adaptability. Here, a novel algorithm for P300 based BCI spelling system is presented, specifically suited for single-trial detection of Event...