Relevant bibliographies by topics / Wiener's filters

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Wiener's filters'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Wiener's filters"

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Biradar, Nagashettappa, M. L. Dewal, and Manoj Kumar Rohit. "Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters." International Scholarly Research Notices 2014 (October 30, 2014): 1–11. http://dx.doi.org/10.1155/2014/876434.

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Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.
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Jacobsen, Bo Holm. "A case for upward continuation as a standard separation filter for potential‐field maps." GEOPHYSICS 52, no. 8 (August 1987): 1138–48. http://dx.doi.org/10.1190/1.1442378.

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Separation filtering is incomplete even under the ideal synthetic condition of known power spectra of the regional and residual fields. I have designed some Wiener filters, which minimize the inevitable separation error, from previous statistical source models of Naidu, and Spector and Grant. This formulation includes the classic separation filters of Strakhov and of Elkins as Wiener filters. A proposed generalization of Wiener filters, denoted as uniformly suboptimum filters, quantitatively supports the statement that a wide span of separation problems may be solved adequately using some convenient, small standard filter family. A uniform random‐source model without assumed vertical correlations invokes upward continuation filters. In addition to this role as a Wiener filter, the upward continuation operator is given by elementary functions in both space and wavenumber domains, is numerically stable, and is also physically comprehensible when applied to real, nonrandom anomalies. In view of these distinguishing features, I propose to use the upward continuation operator to build a convenient, standard family of separation filters.
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Linville, A. Frank. "Single‐channel digital filter design for seismic applications." GEOPHYSICS 59, no. 10 (October 1994): 1584–92. http://dx.doi.org/10.1190/1.1443547.

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A general least‐squares, time‐domain filter design methodology has been developed that is easy to use for a variety of seismic filtering applications. The 1-D finite‐impulse response frequency filter can efficiently provide the noise attenuation and selectivity needed in modern data processing. Flexibility of design allows a choice of all basic types of single‐channel filters commonly used in processing. These include low‐pass, high‐pass, band‐pass, band‐reject, and notch filters. In addition, multiple bands may be passed or rejected using a single operator design without increasing the length of the filter. The ability to reject multiple noise bands with one filter is convenient and also reduces data processing costs. The filter can be viewed as a minimum‐phase Wiener‐Levinson predictive deconvolution filter designed to reject specified frequency bands. The filter is designed from an exact mathematical description of the specified stop bands that provide an explicit expression for the required autocorrelation lags in the normal equations. The filter’s desired frequency response (transition zone width and rejection level) is simply related to two input parameters—operator length and white noise level.
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Dávalos, Antonio, Meryem Jabloun, Philippe Ravier, and Olivier Buttelli. "The Impact of Linear Filter Preprocessing in the Interpretation of Permutation Entropy." Entropy 23, no. 7 (June 22, 2021): 787. http://dx.doi.org/10.3390/e23070787.

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Permutation Entropy (PE) is a powerful tool for measuring the amount of information contained within a time series. However, this technique is rarely applied directly on raw signals. Instead, a preprocessing step, such as linear filtering, is applied in order to remove noise or to isolate specific frequency bands. In the current work, we aimed at outlining the effect of linear filter preprocessing in the final PE values. By means of the Wiener–Khinchin theorem, we theoretically characterize the linear filter’s intrinsic PE and separated its contribution from the signal’s ordinal information. We tested these results by means of simulated signals, subject to a variety of linear filters such as the moving average, Butterworth, and Chebyshev type I. The PE results from simulations closely resembled our predicted results for all tested filters, which validated our theoretical propositions. More importantly, when we applied linear filters to signals with inner correlations, we were able to theoretically decouple the signal-specific contribution from that induced by the linear filter. Therefore, by providing a proper framework of PE linear filter characterization, we improved the PE interpretation by identifying possible artifact information introduced by the preprocessing steps.
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Porsani, Milton J. "Fast algorithms to design discrete Wiener filters in lag and length coordinates." GEOPHYSICS 61, no. 3 (May 1996): 882–90. http://dx.doi.org/10.1190/1.1444013.

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Two recursive algorithms for designing discrete Wiener filters for wavelets of different phase characteristics are the Simpson and the Manolakis recursions. Both procedures are efficient; however, both recursions work with a prefixed length filter. Two fast algorithms to design discrete Wiener filters in lag and length coordinates are presented. The recursion methods of Levinson and Manolakis are combined to generate two fast algorithms that calculate the value for the minimized total squared error (MTSE) corresponding to spiking and shaping filters. For a spiking filter of length n and a wavelet of m data points, [Formula: see text] operations are required to obtain the [Formula: see text] map of the MTSEs, (one operation is defined here as one multiplication and one addition). For a shaping filter, [Formula: see text] operations are required to obtain the corresponding m × n map. These algorithms may be seen as a Levinson recursion on two variables, length j of the filter and lag k, for the desired signal. Numerical examples for spiking and shaping filters are presented.
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Salehi, Hadi, Javad Vahidi, Thabet Abdeljawad, Aziz Khan, and Seyed Yaser Bozorgi Rad. "A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter." Remote Sensing 12, no. 15 (July 23, 2020): 2371. http://dx.doi.org/10.3390/rs12152371.

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The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.
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Jiang, Hai Bo, and Jing Zhi Cai. "Performance Analysis of Several Common Filter." Applied Mechanics and Materials 220-223 (November 2012): 1446–49. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1446.

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Denoising is the initial stage of image processing, in preparation for the subsequent processing of the image. This article describes a field of several denoising used filters include average filter, median filter, Wiener filter, Kalman filter. Combination of diagrams, will describe their filtering principle, at the end of this paper,analysis signal to noise ratio of image and other performance indicators .
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Bazine, Razika, Huayi Wu, and Kamel Boukhechba. "Spectral DWT Multilevel Decomposition with Spatial Filtering Enhancement Preprocessing-Based Approaches for Hyperspectral Imagery Classification." Remote Sensing 11, no. 24 (December 5, 2019): 2906. http://dx.doi.org/10.3390/rs11242906.

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In this paper, spectral–spatial preprocessing using discrete wavelet transform (DWT) multilevel decomposition and spatial filtering is proposed for improving the accuracy of hyperspectral imagery classification. Specifically, spectral DWT multilevel decomposition (SDWT) is performed on the hyperspectral image to separate the approximation coefficients from the detail coefficients. For each level of decomposition, only the detail coefficients are spatially filtered instead of being discarded, as is often adopted by the wavelet-based approaches. Thus, three different spatial filters are explored, including two-dimensional DWT (2D-DWT), adaptive Wiener filter (AWF), and two-dimensional discrete cosine transform (2D-DCT). After the enhancement of the spectral information by performing the spatial filter on the detail coefficients, DWT reconstruction is carried out on both the approximation and the filtered detail coefficients. The final preprocessed image is fed into a linear support vector machine (SVM) classifier. Evaluation results on three widely used real hyperspectral datasets show that the proposed framework using spectral DWT multilevel decomposition with 2D-DCT filter (SDWT-2DCT_SVM) exhibits a significant performance and outperforms many state-of-the-art methods in terms of classification accuracy, even under the constraint of small training sample size, and execution time.
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Salehi, Hadi, Javad Vahidi, and Homayun Motameni. "A Robust Hybrid Filter Based on Evolutionary Intelligence and Fuzzy Evaluation." International Journal of Image and Graphics 18, no. 04 (October 2018): 1850023. http://dx.doi.org/10.1142/s0219467818500237.

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In this paper, a novel denoising method based on wavelet, extended adaptive Wiener filter and the bilateral filter is proposed for digital images. Production of mode is accomplished by the genetic algorithm. The proposed extended adaptive Wiener filter has been developed from the adaptive Wiener filter. First, the genetic algorithm suggest some hybrid models. The attributes of images, including peak signal to noise ratio, signal to noise ratio and image quality assessment are studied. Then, in order to evaluate the model, the values of attributes are sent to the Fuzzy deduction system. Simulations and evaluations mentioned in this paper are accomplished on some standard images such as Lena, boy, fruit, mandrill, Barbara, butterfly, and boat. Next, weaker models are omitted by studying of the various models. Establishment of new generations performs in a form that a generation emendation is carried out, and final model has a more optimum quality compared to each two filters in order to obviate the noise. At the end, the results of this system are studied so that a comprehensive model with the best performance is to be found. Experiments show that the proposed method has better performance than wavelet, bilateral, Butterworth, and some other filters.
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Ramya Ravi, R., R. S. Vinod Kumar, and N. Shanila. "Artifacts Removal in Melanoma Using Various Preprocessing Filters." International Journal of Engineering & Technology 7, no. 3.27 (August 15, 2018): 104. http://dx.doi.org/10.14419/ijet.v7i3.27.17664.

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Preprocessing plays an important role for artifacts removal and quality improvement. In this paper, noise and hair removal techniques for melanoma in dermoscopic images is proposed. Here the performance of four filters for noise removal namely Wiener, Mean, Median, and Gaussian Filters are studied. Of these the performance of Gaussian filter is proved to be best. In addition Morphological Filter is used for hair removal. The noise and hair removal filtering processes help to enhance the quality of the image and thus aid to improve the segmentation results. The performance of the preprocessing filters is compared using quantifying parameters like MSE, PSNR, and SSIM.
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Dissertations / Theses on the topic "Wiener's filters"

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Petrásek, Daniel. "Zpracování termálních obrazů technikou superresolution." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220622.

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Thesis deals with problematic of raising digital image spacial resolution, mainly thermal image. There are mentioned methods of interpolation, panorama and super-resolution. Main topic of this thesis is super-resolution which is detailly described during the thesis. Finally there is a description of algorithm implementation and problems that may occur during the implemetation.
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Zamazal, Zdeněk. "Číslicové zpracování signálů v reálném čase." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219215.

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This work deals with digital signal processing in the field of adaptive filtering. Fundamental basics of adaptive filtering are described and primary aim is to create executable laboratory examples, using adaptive filtering, in LabView programming language. These laboratory examples are intended to be used by students fo studying and during laboratory lessons. Objective is to connect the examples with external devices, such as microphone. A microphone is used as an user's speech input acquiring interface. In the thesis is depicted Wiener's filter and problem of adaptive filtering is discussed. Contemporary adaptive algorithms are described and their applications as well. Most mentioned is the LMS algorithm and it's forms. Laboratory examples use following concepts: Adaptive Echo Cancellation, Active Noise Control and System Identification. Each of these examples is solely executable (need for LabView or Run-time engine), consisting also of theory with diagrams. Examples therefore are usable even without manual.
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Jalůvková, Lenka. "Eliminace zkreslení obrazů duhovky." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220862.

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This master`s thesis is focused on a suppression of a distorsion in iris images. The aim of this work is to study and describe existing degradation methods (1D motion blur, uniform 2D motion blur, Gaussian blur, atmospheric turbulence blur, and out of focus blur). Furthermore, these methods are implemented and tested on a set of images. Then, we designed methods for suppression of these distorsions - inverse filtration, Wiener filtration and iterative deconvolution. All of these methods were tested and evaluated. Based on the experimental results, we can conclude that the Wiener-filter restoration is the most accurate approach from our test set. It achieves the best results in both normal and iterative mode.
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Nakashima, Giovana Yuko. "Aplicação do filtro de WIENER para tratamento de sinais eletromiográficos." Universidade de São Paulo, 2003. http://www.teses.usp.br/teses/disponiveis/82/82131/tde-11122003-151819/.

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A eletromiografia consiste no estudo do movimentos dos músculos através dos sinais elétricos emitidos pelos mesmos. Esses sinais são captados por meio de eletrodos (de surpefície ou de agulha), sendo muito suscetíveis a variações e interferências não relacionadas diretamente com o movimento muscular (ruídos). Visando obter dados qualitativamente confiáveis, o processamento digital de sinais fornece como ferramentas os filtros ótimos e adaptativos, que são utilizados quando o sinal desajado está contaminado por ruído. Com a finalidade de diminuir o ruído presente no sinal eletromiográfico, foram implementados os filtros de wiener e wiener adaptativo ao algoritmo LMS (least mean square), tendo a análise da relação sinal/ruído dos sinais obtidos demonstrado que não há diferença significativa entre os filtros. Como conclusão, no tratamento de sinais eletromiográficos, pode-se aplicar tanto o filtro de wiener como o de wiener adaptativo, observando-se que este último apresenta a vantagem de consumir menos tempo de processamento.
Electromyography is the study of muscle moviments through the electrical signal that they emanate. These signals are detected with eletrodes (surface or needle), where variations and interferences not directly related with movement are present (noises). Digital signal processing provides optimal and adaptative filters with the aim to get qualitative reliable data. The filters are used when desired signal is corrupted by noise. With the purpose of noise reduction in electromyography signal, wiener and adaptative wiener filters (the last one with least mean square algorithm) were implemented. However, signal-to-noise ratio analysis gave evidence that there is no significative difference between both the filters. As conclusion, in electromygraphy signal treatment, wiener and adaptative wiener filters could be used, with the only difference that the last one takes less processing time.
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Hiemstra, John David. "Robust Implementations of the Multistage Wiener Filter." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/26714.

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The research in this dissertation addresses reduced rank adaptive signal processing, with specific emphasis on the multistage Wiener filter (MWF). The MWF is a generalization of the classical Wiener filter that performs a stage-by-stage decomposition based on orthogonal projections. Truncation of this decomposition produces a reduced rank filter with many benefits, for example, improved performance. This dissertation extends knowledge of the MWF in four areas. The first area is rank and sample support compression. This dissertation examines, under a wide variety of conditions, the size of the adaptive subspace required by the MWF (i.e., the rank) as well as the required number of training samples. Comparisons are made with other algorithms such as the eigenvector-based principal components algorithm. The second area investigated in this dissertation concerns â soft stopsâ , i.e., the insertion of diagonal loading into the MWF. Several methods for inserting loading into the MWF are described, as well as methods for choosing the amount of loading. The next area investigated is MWF rank selection. The MWF will outperform the classical Wiener filter when the rank is properly chosen. This dissertation presents six approaches for selecting MWF rank. The algorithms are compared to one another and an overall design space taxonomy is presented. Finally, as digital modelling capabilities become more sophisticated there is emerging interest in augmenting adaptive processing algorithms to incorporate prior knowledge. This dissertation presents two methods for augmenting the MWF, one based on linear constraints and a second based on non-zero weight vector initialization. Both approaches are evaluated under ideal and perturbed conditions. Together the research described in this dissertation increases the utility and robustness of the multistage Wiener filter. The analysis is presented in the context of adaptive array processing, both spatial array processing and space-time adaptive processing for airborne radar. The results, however, are applicable across the entire spectrum of adaptive signal processing applications.
Ph. D.
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Kayrish, Matthew Greco. "Robust GM Wiener Filter in the Complex Domain." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/19230.

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Space-Time Adaptive Processing is a signal processing technique that uses an adaptive array to help remove nonhomogeneous data points from a dataset. Since the early 1970s, STAP has been used in radar systems for their ability to "filter clutter, interference and jamming signals. One major flaw with early STAP radar systems is the reliance on non-robust estimators to estimate the noise condition. When even a single outlier is present, the earliest STAP radar systems would break down, causing the target to be missed. Many algorithms have been developed to successfully estimate the noise condition of the dataset when outliers are present. As recently as 2007, a STAP radar processing system based on Adaptive Complex Projection Statistics has been proposed and successfully"filters out the noise condition even when outliers are present. However, this algorithm requires the data to be entirely real. Radar data, which consists of amplitude and phase, is complex valued. Therefore, it must be converted into its rectangular components before processing can commence. This introduces many additional processing steps which significantly increase the computing time. The STAP radar algorithm of this thesis overcomes the problems with early radar systems. It is based on the Complex GM Wiener Filter (CGMWF) with the Minimum Covariance Determinant (MCD) for outlier detection. The robustness of the conventional Wiener "lter is enhanced by robust Huber Estimator, and using the MCD enables processing entirely in the complex domain. This results in a STAP radar algorithm with a breakdown point of nearly 35% and that enables processing entirely in the complex domain for fewer computing steps.
Master of Science
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Wang, Jingbo. "Multi-channel Wiener filters and their application to seismic array data." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608809.

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Tan, Sovira. "A pattern recognition Wiener filter for realistic clutter backgrounds." Thesis, University of Sussex, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390826.

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Bowman, Kevin W. "Application of wavelets to adaptive optics and multiresolution wiener filtering." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/14920.

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Warner, Carl Michael 1952. "ESTIMATION OF NONSTATIONARY SIGNALS IN NOISE (PROCESSING, ADAPTIVE, WIENER FILTERS, ESTIMATION, DIGITAL)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291297.

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Books on the topic "Wiener's filters"

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Fontes, Natanael Ruiz. An analysis of the IIR an FIR Wiener filters with applications to underwater acoustics. Monterey, Calif: Naval Postgraduate School, 1997.

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Planas, C. Applied time series analysis: Modelling, forecasting, unobserved components analysis and the Wiener-Kolmogorov filter. Luxembourg: Office for Official Publications of the European Communities, 1997.

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A Wiener filtering approach to the design of tracking algorithms: With applications in mobile radio communications. Uppsala: Uppsala University, 1995.

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An Analysis of the IIR and FIR Wiener Filters with Applications to Underwater Acoustics. Storming Media, 1997.

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Performance Analysis of the Wiener Filter with Applications to Underwater Acoustic Signals. Storming Media, 1997.

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Sayed, Ali H., Thomas Kailath, and Babak Hassibi. Linear Estimation. Prentice Hall, 2000.

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Sayed, Ali H., Thomas Kailath, and Babak Hassibi. Linear Estimation. Prentice Hall, 2000.

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Book chapters on the topic "Wiener's filters"

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Vaseghi, Saeed V. "Wiener Filters." In Advanced Signal Processing and Digital Noise Reduction, 140–63. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-92773-6_5.

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Kroschel, Kristian. "Wiener-Filter." In Statistische Nachrichtentheorie, 288–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/978-3-662-10044-8_10.

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Kroschel, Kristian. "Wiener-Filter." In Statistische Nachrichtentheorie, 16–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-662-10046-2_2.

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Kroschel, Kristian. "Wiener-Filter." In Statistische Informationstechnik, 189–221. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-10041-7_8.

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Kroschel, Kristian, Gerhard Rigoll, and Björn Schuller. "Wiener-Filter." In Statistische Informationstechnik, 279–317. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15954-1_8.

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Benesty, Jacob, Yiteng Huang, and Jingdong Chen. "Wiener and Adaptive Filters." In Springer Handbook of Speech Processing, 103–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-49127-9_6.

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Gan, Woon Siong. "Wiener Filter and Kalman Filter." In Signal Processing and Image Processing for Acoustical Imaging, 31–36. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-10-5550-8_7.

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Kamen, E. W., and J. K. Su. "The Wiener Filter." In Advanced Textbooks in Control and Signal Processing, 101–47. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0417-9_4.

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Principe, Jose C., Yadunandana N. Rao, and Deniz Erdogmus. "Error Whitening Wiener Filters: Theory and Algorithms." In Least-Mean-Square Adaptive Filters, 445–89. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471461288.ch10.

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Lange, Jörg, and Tatjana Lange. "Optimales Empfangsfilter (Wiener Filter)." In essentials, 49–54. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-24850-5_9.

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Conference papers on the topic "Wiener's filters"

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Sebastian, Anish, Parmod Kumar, Madhavi Anugolu, Marco P. Schoen, Alex Urfer, and D. Subbaram Naidu. "Optimization of Bayesian Filters and Hammerstein-Wiener Models for EMG-Force Signals Using Genetic Algorithm." In ASME 2009 Dynamic Systems and Control Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/dscc2009-2658.

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Processing electromyographic (EMG) signals for force estimation has many unknown variables that can influence the outcome or interpretation of the recorded EMG signal significantly. An array of filtering methods have been proposed over the past few years with the objective to classify motion for use in prosthetic hands. In this paper, we explore the optimal parameter settings of a set of Bayesian based EMG filters with the objective to use the filtered EMG data for system identification. System identification is utilized to establish a relationship between the measured EMG data and the generated force developed by fingers in a human hand. The proposed system identification is based on nonlinear Hammerstein-Wiener models. Optimization is also applied to find the optimal parameter settings for these nonlinear models. Genetic Algorithm (GA) is used to conduct the optimization for both, the optimal parameter settings for the Bayesian filters as well as the Hammerstein-Wiener model. The experimental results and optimization analysis indicate that the optimization can yield significant improvement in data accuracy and interpretation.
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Albu, Felix. "The Modified Filtered-X Multichannel Wiener Filter." In 2018 14th IEEE International Conference on Signal Processing (ICSP). IEEE, 2018. http://dx.doi.org/10.1109/icsp.2018.8652327.

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Huang, Chi-Feng, and Ming-Chyuan Lu. "Study of Microphone Array for Noise Reduction in Sound Based Micro-Tool Wear Monitoring." In ASME 2011 International Manufacturing Science and Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/msec2011-50099.

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An experienced technician can usually identify the cutting condition by hearing the sound generated during the cutting and the sound signal can be expected to detect the features closely related to the tool condition. However, the background noise always contaminates the signal obtained by microphone system during cutting and reduces the chance of applying the sound based micro tool condition monitoring system in industry. In order to reduce the noise effect and improve the system performance, a microphone array integrated with Wiener filter was designed and implemented in this study to enhance the noise reduction capability for monitoring system. The experimental results show that the microphone array integrated with Wiener filter provides a better solution than single microphone integrated with Wiener filter or the microphone array without the post filter design in reducing the broadband background noise.
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Du, Fei, Jun Hong, Baotong Li, Zeng Luo, Shaofeng Wang, and Ying Xu. "A Noise Evaluation Based Wiener Deconvolution Method for Contact Parameters Measurement Using Ultrasound." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-86445.

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Ultrasound method is an extremely useful tool in contact parameters identification for joint surface widely used in engineering product. Wiener filter is commonly used to ultrasonic reflection map to reduce the influences of the blurring effect introduced by ultrasonic beam size on the measurement accuracy, but the results are not good enough because it is difficult to estimate the signal-to-noise ratio of reflection map. In this paper, a noise evaluation based Wiener deconvolution approach is developed to handle this problem. The noise power spectrum of reflection map is estimated through the steel-air contact area of the joint surface, and an evaluation of the ratio of noise power spectrum and signal power spectrum is obtained based on the noise variance and the experimental reflection map. To verify the efficiency of the developed approach, a sphere-plate contact interface is selected as a concrete case in which different Wiener deconvolution approaches are applied for evaluation of contact parameters. The results are compared with simulation results produced by a finite element model (FEM). The comparisons show that the contact parameters obtained by the proposed approach are more accurate than that obtained by commonly used Wiener filter approaches.
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Sazonov, V. V., M. A. Shcherbakov, and V. A. Vasilyev. "Modified Wiener filter." In 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON). IEEE, 2015. http://dx.doi.org/10.1109/sibircon.2015.7361881.

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Arora, Raman, and Harish Parthasarathy. "Spherical Wiener filter." In 2008 15th IEEE International Conference on Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icip.2008.4711813.

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Wang, Xinying, James G. Mainprize, Gang Wu, and Martin J. Yaffe. "Wiener filter for filtered back projection in digital breast tomosynthesis." In SPIE Medical Imaging, edited by Norbert J. Pelc, Robert M. Nishikawa, and Bruce R. Whiting. SPIE, 2012. http://dx.doi.org/10.1117/12.911545.

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De Paula, Alesi Augusto, Víctor Costa da Silva Campos, Guilherme Vianna Raffo, and Bruno Otávio Soares Teixeira. "Novel Gaussian State Estimator based on H2 Norm and Steady-State Variance." In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1259.

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This paper proposes a novel state estimator for discrete-time linear systems with Gaussian noise. The proposed algorithm is a fixed-gain filter, whose observer structure is more general than Kalman one for linear time-invariant systems. Therefore, the steady-state variance of the estimation error is minimized. For white noise stochastic processes, this performance criterion is reduced to the square H2 norm of a given linear time-invariant system. Then, the proposed algorithm is called observer H2 filter (OH2F). This is the standard Wiener-Hopf or Kalman-Bucy filtering problem. As the Kalman predictor and Kalman filter are well-known solutions for such a problem, they are revisited.
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Hung, Kwok-Wai, and Wan-Chi Siu. "Hybrid DCT-Wiener-based interpolation via learnt Wiener filter." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6637885.

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Almajai, Ibrahim, Ben Milner, Jonathan Darch, and Saeed Vaseghi. "Visually-Derived Wiener Filters for Speech Enhancement." In 2007 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/icassp.2007.366980.

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Reports on the topic "Wiener's filters"

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Fontes, N. R., and C. W. Therrien. Performance Analysis of the Wiener Filter with Applications to Underwater Acoustic Signals. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada330083.

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Scharf, Louis. Reduced Rank Wiener Filters in Optimized Coordinates for Partially Adaptive Filtering in Passive and Active Sonar Arrays. Fort Belvoir, VA: Defense Technical Information Center, June 2004. http://dx.doi.org/10.21236/ada424321.

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