Relevant bibliographies by topics / Adaptive filtering

Academic literature on the topic 'Adaptive filtering'

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Published: 4 June 2021
Last updated: 14 September 2024

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Journal articles on the topic "Adaptive filtering"

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Wagner, Christian, and Gabriel Wittum. "Adaptive filtering." Numerische Mathematik 78, no. 2 (December 1, 1997): 305–28. http://dx.doi.org/10.1007/s002110050314.

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Brown, S. D., and S. C. Rutan. "Adaptive Kalman Filtering." Journal of Research of the National Bureau of Standards 90, no. 6 (November 1985): 403. http://dx.doi.org/10.6028/jres.090.032.

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Troncoso-Pastoriza, Juan Ramón, and Fernando Perez-Gonzalez. "Secure Adaptive Filtering." IEEE Transactions on Information Forensics and Security 6, no. 2 (June 2011): 469–85. http://dx.doi.org/10.1109/tifs.2011.2109385.

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Shynk, J. J. "Adaptive IIR filtering." IEEE ASSP Magazine 6, no. 2 (April 1989): 4–21. http://dx.doi.org/10.1109/53.29644.

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RAUF, FAWAD, and HASSAN M. AHMED. "NONLINEAR ADAPTIVE FILTERING." International Journal of High Speed Electronics and Systems 07, no. 04 (December 1996): 491–520. http://dx.doi.org/10.1142/s0129156496000281.

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Singher, Liviu. "Adaptive multiple filtering." Optical Engineering 41, no. 1 (January 1, 2002): 55. http://dx.doi.org/10.1117/1.1425790.

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Yukawa, Masahiro. "Multikernel Adaptive Filtering." IEEE Transactions on Signal Processing 60, no. 9 (September 2012): 4672–82. http://dx.doi.org/10.1109/tsp.2012.2200889.

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Rutan, Sarah C. "Adaptive Kalman Filtering." Analytical Chemistry 63, no. 22 (November 15, 1991): 1103A—1109A. http://dx.doi.org/10.1021/ac00022a739.

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Bose, Tamal, Anand Venkatachalam, and Ratchaneekorn Thamvichai. "Multiplierless Adaptive Filtering." Digital Signal Processing 12, no. 1 (January 2002): 107–18. http://dx.doi.org/10.1006/dspr.2001.0407.

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Voskoboinikov, Yuri E. "А locally adaptive wavelet filtering algorithm for images." Analysis and data processing systems, no. 1 (March 29, 2023): 25–36. http://dx.doi.org/10.17212/2782-2001-2023-1-25-36.

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The algorithms based on the decomposition of a noisy image in an orthogonal basis of wavelet functions have been widely used to filter images (especially contrasting ones) over the past four decades. In this case, most wavelet filtering algorithms are of a threshold nature, namely: the decomposition coefficient smaller in an absolute value of a certain threshold value is reset to zero; otherwise the coefficient undergoes some (most often nonlinear) transformation. A certain (and very significant) drawback of threshold algorithms is that all coefficients of a certain decomposition level are processed with one identical threshold value (i.e., a constant value for all de-composition coefficients). This does not allow taking into account the “individual energy” of each decomposition coefficient for its more optimal processing. Therefore, we propose its own filtering factor for each coefficient, built on the basis of the optimal Wiener filtering and where a filtering parameter is introduced to compensate for incomplete a priori information on the value of the processed decomposition coefficients. In order to select a filtering parameter, a statistical approach has been proposed that makes it possible to estimate the optimal value of this parameter with acceptable accuracy. The performed computational experiment has shown the developed algorithm effectiveness for wavelet filtering of images.
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Dissertations / Theses on the topic "Adaptive filtering"

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Haglund, Leif. "Adaptive Multidimensional Filtering." Doctoral thesis, Linköpings universitet, Bildbehandling, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-54339.

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This thesis contains a presentation and an analysis of adaptive filtering strategies for multidimensional data. The size, shape and orientation of the flter are signal controlled and thus adapted locally to each neighbourhood according to a predefined model. The filter is constructed as a linear weighting of fixed oriented bandpass filters having the same shape but different orientations. The adaptive filtering methods have been tested on both real data and synthesized test data in 2D, e.g. still images, 3D, e.g. image sequences or volumes, with good results. In 4D, e.g. volume sequences, the algorithm is given in its mathematical form. The weighting coefficients are given by the inner products of a tensor representing the local structure of the data and the tensors representing the orientation of the filters. The procedure and lter design in estimating the representation tensor are described. In 2D, the tensor contains information about the local energy, the optimal orientation and a certainty of the orientation. In 3D, the information in the tensor is the energy, the normal to the best ftting local plane and the tangent to the best fitting line, and certainties of these orientations. In the case of time sequences, a quantitative comparison of the proposed method and other (optical flow) algorithms is presented. The estimation of control information is made in different scales. There are two main reasons for this. A single filter has a particular limited pass band which may or may not be tuned to the different sized objects to describe. Second, size or scale is a descriptive feature in its own right. All of this requires the integration of measurements from different scales. The increasing interest in wavelet theory supports the idea that a multiresolution approach is necessary. Hence the resulting adaptive filter will adapt also in size and to different orientations in different scales.
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Adriannse, Robert. "Adaptive local statistics filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq21530.pdf.

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Chambers, Brian D. "Adaptive Bayesian information filtering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ45945.pdf.

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Rangarao, Kaluri Venkata. "Adaptive digital notch filtering." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26345.

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Xie, Bei. "Partial Update Adaptive Filtering." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/26670.

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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 and have lower steady-state mean square error (MSE) than LMS. However, their high computational complexity ($O(N^2)$) makes them unsuitable for many real-time applications. A well-known approach to controlling computational complexity is applying partial update (PU) method to adaptive filters. A partial update method can reduce the adaptive algorithm complexity by updating part of the weight vector instead of the entire vector or by updating part of the time. An analysis for different PU adaptive filter algorithms is necessary and meaningful. The deficient-length adaptive filter addresses a situation in system identification where the length of the estimated filter is shorter than the length of the actual unknown system. It is related to the partial update adaptive filter, but has different performance. It can be viewed as a PU adaptive filter, in that the deficient-length adaptive filter also updates part of the weight vector. However, it updates the same part of the weight vector for each iteration, while the partial update adaptive filter updates a different part of the weight vector for each iteration. In this work, basic PU methods are applied to the adaptive filter algorithms which have not been fully addressed in the literature, including CG, EDS, and Constant Modulus Algorithm (CMA) based algorithms. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical analysis is shown including convergence condition, steady-state performance, and tracking performance. Computer simulation with proper examples is also shown to further help study the performance. The performance is compared among different PU methods or among different adaptive filtering algorithms. Computational complexity is calculated for each PU method and each adaptive filter algorithm. The deficient-length RLS and EDS are also analyzed and compared to the performance of the PU adaptive filter. In this dissertation, basic partial-update methods are applied to adaptive filter algorithms including CMA1-2, NCMA, Least Squares CMA (LSCMA), EDS, and CG. A new PU method, the selective-sequential method, is developed for LSCMA. Mathematical derivation and performance analysis are provided including convergence condition, steady-state mean and mean-square performance for a time-invariant system. The steady-state mean and mean-square performance are also presented for a time-varying system. Computational complexity is calculated for each adaptive filter algorithm. Numerical examples are shown to compare the computational complexity of the PU adaptive filters with the full-update filters. Computer simulation examples, including system identification and channel equalization, are used to demonstrate the mathematical analysis and show the performance of PU adaptive filter algorithms. They also show the convergence performance of PU adaptive filters. The performance is compared between the original adaptive filter algorithms and different partial-update methods. The performance is also compared among similar PU least-squares adaptive filter algorithms, such as PU RLS, PU CG, and PU EDS. Deficient-length RLS and EDS are studied. The performance of the deficient-length filter is also compared with the partial update filter. In addition to the generic applications of system identification and channel equalization, two special applications of using partial update adaptive filters are also presented. One application is using PU adaptive filters to detect Global System for Mobile Communication (GSM) signals in a local GSM system using the Open Base Transceiver Station (OpenBTS) and Asterisk Private Branch Exchange (PBX). The other application is using PU adaptive filters to do image compression in a system combining hyperspectral image compression and classification. Overall, the PU adaptive filters can usually achieve comparable performance to the full-update filters while reducing the computational complexity significantly. The PU adaptive filters can achieve similar steady-state MSE to the full-update filters. Among different PU methods, the MMax method has a convergence rate very close to the full-update method. The sequential and stochastic methods converge slower than the MMax method. However, the MMax method does not always perform well with the LSCMA algorithm. The sequential LSCMA has the best performance among the PU LSCMA algorithms. The PU CMA may perform better than the full-update CMA in tracking a time-varying system. The MMax EDS can converge faster than the MMax RLS and CG. It can converge to the same steady-state MSE as the MMax RLS and CG, while having a lower computational complexity. The PU LMS and PU EDS can also perform a little better in a system combining hyperspectral image compression and classification.
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Kshonze, Kristopher. "Adaptive filtering with systolic arrays." Thesis, University of Ottawa (Canada), 1988. http://hdl.handle.net/10393/5456.

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Fertig, Louis B. "Dual forms for constrained adaptive filtering." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15642.

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Baykal, Buyurman. "Underdetermined recursive least-squares adaptive filtering." Thesis, Imperial College London, 1995. http://hdl.handle.net/10044/1/7790.

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Faghih, Farshad. "Adaptive wavelet-based noise filtering techniques." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38627.pdf.

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Wilstrup, Steven L. "Adaptive algorithms for two dimensional filtering." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/22855.

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Books on the topic "Adaptive filtering"

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Diniz, Paulo S. R. Adaptive Filtering. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-68606-6.

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Diniz, Paulo S. R. Adaptive Filtering. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-29057-3.

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Diniz, Paulo S. R. Adaptive Filtering. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4614-4106-9.

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Diniz, Paulo Sergio Ramirez. Adaptive Filtering. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3.

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Ramirez, Paulo Sergio. Adaptive Filtering. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4757-3637-3.

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Lee, Kong-Aik, Woon-Seng Gan, and Sen M. Kuo. Subband Adaptive Filtering. Chichester, UK: John Wiley & Sons, Ltd, 2009. http://dx.doi.org/10.1002/9780470745977.

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1931-, Haykin Simon S., ed. Unsupervised adaptive filtering. New York: Wiley, 2000.

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Rangarao, Kaluri Venkata. Adaptive digital notch filtering. Monterey, Calif: Naval Postgraduate School, 1991.

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Chambers, Brian D. Adaptive bayesian information filtering. Toronto: Univsity of Toronto, Dept. of Computer Science, 1999.

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Gustafsson, Fredrik. Adaptive Filtering and Change Detection. Chichester, UK: John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/0470841613.

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Book chapters on the topic "Adaptive filtering"

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Diniz, Paulo Sergio Ramirez. "Introduction to Adaptive Filtering." In Adaptive Filtering, 1–14. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_1.

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Diniz, Paulo Sergio Ramirez. "Fundamentals of Adaptive Filtering." In Adaptive Filtering, 15–69. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_2.

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Diniz, Paulo Sergio Ramirez. "The Least-Mean-Square (LMS) Algorithm." In Adaptive Filtering, 71–131. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_3.

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Diniz, Paulo Sergio Ramirez. "LMS-Based Algorithms." In Adaptive Filtering, 133–81. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_4.

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Diniz, Paulo Sergio Ramirez. "Conventional RLS Adaptive Filter." In Adaptive Filtering, 183–236. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_5.

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Diniz, Paulo Sergio Ramirez. "Adaptive Lattice-Based RLS Algorithms." In Adaptive Filtering, 237–87. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_6.

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Diniz, Paulo Sergio Ramirez. "Fast Transversal RLS Algorithms." In Adaptive Filtering, 289–309. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_7.

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Diniz, Paulo Sergio Ramirez. "QR-Decomposition-Based RLS Filters." In Adaptive Filtering, 311–76. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_8.

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Diniz, Paulo Sergio Ramirez. "Adaptive IIR Filters." In Adaptive Filtering, 377–436. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8660-3_9.

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Diniz, Paulo S. R. "Introduction to Adaptive Filtering." In Adaptive Filtering, 1–8. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29057-3_1.

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Conference papers on the topic "Adaptive filtering"

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Lang, Eric, and Tsu-Wei Chou. "Modal filtering using lineal sensors." In Adaptive Structures Forum. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1994. http://dx.doi.org/10.2514/6.1994-1741.

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Kesjindatanawaj, Wachirapong, Surachai Ongkittikul, and Sanun Srisuk. "Adaptive boundary filtering." In TENCON 2013 - 2013 IEEE Region 10 Conference. IEEE, 2013. http://dx.doi.org/10.1109/tencon.2013.6718896.

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Le Meur, D., N. Benjamin, R. Cole, and M. Al Harthy. "Adaptive Groundroll Filtering." In 70th EAGE Conference and Exhibition - Workshops and Fieldtrips. European Association of Geoscientists & Engineers, 2008. http://dx.doi.org/10.3997/2214-4609.20147745.

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Stevens, Mark R., Dan Gutchess, Neal Checka, and Magnús Snorrason. "Adaptive particle filtering." In Defense and Security Symposium, edited by Kevin L. Priddy and Emre Ertin. SPIE, 2006. http://dx.doi.org/10.1117/12.665955.

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Weimer, Markus, Alexandros Karatzoglou, and Alex Smola. "Adaptive collaborative filtering." In the 2008 ACM conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454050.

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Lanquillon, Carsten, and Ingrid Renz. "Adaptive information filtering." In the eighth international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319950.320061.

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Boughanem, M., and M. Tmar. "Incremental adaptive filtering." In the 2002 ACM symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/508791.508915.

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Price, Michael G., Alan E. Craig, Joseph C. Harsanyi, and John N. Lee. "Adaptive Optical Filtering Architecture." In Optical Computing. Washington, D.C.: Optica Publishing Group, 1989. http://dx.doi.org/10.1364/optcomp.1989.mc3.

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This paper discusses aspects of adaptive filtering applied to narrowband interference rejection for wideband receiver systems. A time/space integrating optical architecture using a spatial light modulator is described.
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Gallieni, D., and D. Bonaccini. "Digital Filtering Techniques and Centroid Predictors In Ao Servo-Systems." In Adaptive Optics. Washington, D.C.: Optica Publishing Group, 1996. http://dx.doi.org/10.1364/adop.1996.athc.26.

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Adaptive Optics systems need a wavefront sensor (WFS) for the servo-loop. The WFS integration time has to be selected considering SNR requirements. This requires to extend the integration period as much as possible, especially when dealing with faint sources. On the other hand, WFS sampling period imposes the discretization time-step to the entire servo-system, thus influencing its close loop bandwidth. The latter is also affected by additional time delays caused by WFS readout/reconstructor computing time.
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Presencia, J. Anit, and A. Benitto Bella. "Adaptive filtering algorithms: Survey." In 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE). IEEE, 2017. http://dx.doi.org/10.1109/iceice.2017.8191850.

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Reports on the topic "Adaptive filtering"

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Zhang, Yi. Adaptive Information Filtering. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada563638.

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Brockett, R. W. Adaptive Filtering and Control. Fort Belvoir, VA: Defense Technical Information Center, November 1989. http://dx.doi.org/10.21236/ada219556.

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Meeson, Reginald N. HHT Sifting and Adaptive Filtering. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada421124.

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Gardner, William A. Programmable Blind Adaptive Multivariate Filtering. Fort Belvoir, VA: Defense Technical Information Center, July 1998. http://dx.doi.org/10.21236/ada358093.

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Poyneer, L. Robust Wiener filtering for Adaptive Optics. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/15014295.

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Gibson, Steve. Adaptive Filtering, Identification, and Control with Applications to Adaptive Optics. Fort Belvoir, VA: Defense Technical Information Center, December 2003. http://dx.doi.org/10.21236/ada419508.

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Pados, Dimitiris A. Adaptive Digital Signature Design and Short-Data-Record Adaptive Filtering. Fort Belvoir, VA: Defense Technical Information Center, April 2008. http://dx.doi.org/10.21236/ada481007.

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Weinberger, Norman M. Cortical Adaptive Filtering in Bioacoustic Signal Classification. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada266229.

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Chance, Frances S., and Christina E. Warrender. Implementing Neural Adaptive Filtering in Engineered Detection Systems. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1474262.

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Zhang, Yi, and Jamie Callan. The Bias Problem and Language Models in Adaptive Filtering. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada456239.

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