Relevant bibliographies by topics / Adaptive signal processing

Academic literature on the topic 'Adaptive signal processing'

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Published: 4 June 2021
Last updated: 30 July 2024

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Journal articles on the topic "Adaptive signal processing"

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Morgan, D. "Adaptive signal processing." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 4 (August 1986): 1017–18. http://dx.doi.org/10.1109/tassp.1986.1164869.

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Brewster, R. L. "Adaptive Signal Processing." Electronics and Power 32, no. 7 (1986): 545. http://dx.doi.org/10.1049/ep.1986.0314.

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Sibul, Leon H., and Teresa L. Dixon. "Environmentallly adaptive signal processing." Journal of the Acoustical Society of America 101, no. 5 (May 1997): 3157. http://dx.doi.org/10.1121/1.419091.

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Wellstead, P. E. "Book Review: Adaptive Signal Processing." International Journal of Electrical Engineering & Education 23, no. 4 (October 1986): 375–76. http://dx.doi.org/10.1177/002072098602300429.

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Lindquist, C. "Book reviews - Adaptive signal processing." IEEE Control Systems Magazine 7, no. 4 (August 1987): 51. http://dx.doi.org/10.1109/mcs.1987.1105343.

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Haykin, Simon. "Guest Editorial: Adaptive Signal Processing." Optical Engineering 31, no. 6 (1992): 1143. http://dx.doi.org/10.1117/12.60706.

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Resnikoff, Howard L. "Wavelets and adaptive signal processing." Optical Engineering 31, no. 6 (1992): 1229. http://dx.doi.org/10.1117/12.57515.

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Chakrabarti, N. B. "Transform Domain Adaptive Signal Processing." IETE Journal of Research 35, no. 2 (March 1989): 52–60. http://dx.doi.org/10.1080/03772063.1989.11436792.

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Harteneck, M., and R. W. Stewart. "Adaptive signal processing JAVA applet." IEEE Transactions on Education 44, no. 2 (May 2001): 6 pp. http://dx.doi.org/10.1109/13.925850.

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Chen, Walter Y., and Richard A. Haddad. "Dual mode adaptive signal processing." Computers & Electrical Engineering 18, no. 3-4 (May 1992): 261–75. http://dx.doi.org/10.1016/0045-7906(92)90019-a.

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Dissertations / Theses on the topic "Adaptive signal processing"

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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals." Doctoral thesis, Umeå universitet, Strålningsvetenskaper, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Electromyography is the study of muscle function through the electrical signals from the muscles. In surface electromyography the electrical signal is detected on the skin. The signal arises from ion exchanges across the muscle fibres’ membranes. The ion exchange in a motor unit, which is the smallest unit of excitation, produces a waveform that is called an action potential (AP). When a sustained contraction is performed the motor units involved in the contraction will repeatedly produce APs, which result in AP trains. A surface electromyogram (EMG) signal consists of the superposition of many AP trains generated by a large number of active motor units. The aim of this dissertation was to introduce and evaluate new methods for analysis of surface EMG signals. An important aspect is to consider where to place the electrodes during the recording so that the electrodes are not located over the zone where the neuromuscular junctions are located. A method that could estimate the location of this zone was presented in one study. The mean frequency of the EMG signal is often used to estimate muscle fatigue. For signals with low signal-to-noise ratio it is important to limit the integration intervals in the mean frequency calculations. Therefore, a method that improved the maximum frequency estimation was introduced and evaluated in comparison with existing methods. The main methodological work in this dissertation was concentrated on finding single motor unit AP trains from EMG signals recorded with several channels. In two studies single motor unit AP trains were enhanced by using filters that maximised the kurtosis of the output. The first of these studies used a spatial filter, and in the second study the technique was expanded to include filtration in time. The introduction of time filtration resulted in improved performance, and when the method was evaluated in comparison with other methods that use spatial and/or temporal filtration, it gave the best performance among them. In the last study of this dissertation this technique was used to compare AP firing rates and conduction velocities in fibromyalgia patients as compared with a control group of healthy subjects. In conclusion, this dissertation has resulted in new methods that improve the analysis of EMG signals, and as a consequence the methods can simplify physiological research projects.
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Östlund, Nils. "Adaptive signal processing of surface electromyogram signals /." Umeå : Department of Radiation Sciences, Umeå University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-743.

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Chan, M. K. "Adaptive signal processing algorithms for non-Gaussian signals." Thesis, Queen's University Belfast, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269023.

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Jahanchahi, Cyrus. "Quaternion valued adaptive signal processing." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/24165.

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Recent developments in sensor technology, human centered computing and robotics have brought to light new classes of multidimensional data which are naturally represented as three- or four-dimensional vector-valued processes. Such signals are readily modeled as real vectors in R3 and R4, however, it has become apparent that there are advantages in processing such data in division algebras - the quaternion domain. The progress in the statistics of quaternion variable, particularly augmented statistics and widely linear modeling, has opened up a new front of research in vector sensor modeling, however, there are several key problems that need to be addressed in order to exploit the full power of quaternions in statistical signal processing. The principal problem lies in the lack of a mathematical framework, such as the CR-calculus in the complex domain, for the differentiation of non-holomorphic functions. Since most functions (including typical cost functions) in the quaternion domain are non-holomorphic, as defined by the Cauchy-Riemann-Fueter (CRF) condition, this presents a severe obstacle to solving optimisation problems and developing adaptive filtering algorithms in the quaternion domain. To this end, we develop the HR-calculus, an extension of the CR-calculus, allowing the differentiation of non-holomorphic functions. This is followed by the introduction of the I-gradient, enabling for generic extensions of complex valued algorithms to be derived. Using this unified framework we introduce the quaternion least mean square (QLMS), quaternion recursive least squares (QRLS), quaternion affine projection algorithm (QAPA) and quaternion Kalman filter. These estimators are made optimal for the processing of noncircular data, by proposing widely linear extensions of their standard versions. Convergence and steady state properties of these adaptive estimators are analysed and validated experimentally via simulations on both synthetic and real world signals.
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CARINI, ALBERTO. "ADAPTIVE AND NONLINEAR SIGNAL PROCESSING." Doctoral thesis, Università degli studi di Trieste, 1997. http://thesis2.sba.units.it/store/handle/item/13000.

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Testoni, Nicola <1980&gt. "Adaptive multiscale biological signal processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/1122/1/Tesi_Testoni_Nicola.pdf.

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Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.
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Testoni, Nicola <1980&gt. "Adaptive multiscale biological signal processing." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2008. http://amsdottorato.unibo.it/1122/.

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Biological processes are very complex mechanisms, most of them being accompanied by or manifested as signals that reflect their essential characteristics and qualities. The development of diagnostic techniques based on signal and image acquisition from the human body is commonly retained as one of the propelling factors in the advancements in medicine and biosciences recorded in the recent past. It is a fact that the instruments used for biological signal and image recording, like any other acquisition system, are affected by non-idealities which, by different degrees, negatively impact on the accuracy of the recording. This work discusses how it is possible to attenuate, and ideally to remove, these effects, with a particular attention toward ultrasound imaging and extracellular recordings. Original algorithms developed during the Ph.D. research activity will be examined and compared to ones in literature tackling the same problems; results will be drawn on the base of comparative tests on both synthetic and in-vivo acquisitions, evaluating standard metrics in the respective field of application. All the developed algorithms share an adaptive approach to signal analysis, meaning that their behavior is not dependent only on designer choices, but driven by input signal characteristics too. Performance comparisons following the state of the art concerning image quality assessment, contrast gain estimation and resolution gain quantification as well as visual inspection highlighted very good results featured by the proposed ultrasound image deconvolution and restoring algorithms: axial resolution up to 5 times better than algorithms in literature are possible. Concerning extracellular recordings, the results of the proposed denoising technique compared to other signal processing algorithms pointed out an improvement of the state of the art of almost 4 dB.
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Figueroa, Toro Miguel E. "Adaptive signal processing and correlational learning in mixed-signal VLSI /." Thesis, Connect to this title online; UW restricted, 2005. http://hdl.handle.net/1773/6856.

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Wyrsch, Sigisbert. "Adaptive subband signal processing for hearing instruments /." Zürich, 2000. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=13577.

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Hermand, Jean-Pierre. "Environmentally-Adaptive Signal Processing in Ocean Acoustics." Doctoral thesis, Universite Libre de Bruxelles, 1993. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/212734.

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Books on the topic "Adaptive signal processing"

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Benesty, Jacob, and Yiteng Huang, eds. Adaptive Signal Processing. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11028-7.

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Adali, Tülay, and Simon Haykin, eds. Adaptive Signal Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470575758.

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Davisson, L. D., and G. Longo, eds. Adaptive Signal Processing. Vienna: Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-2840-4.

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Alexander, S. Thomas. Adaptive Signal Processing. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4612-4978-8.

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H, Sibul Leon, ed. Adaptive signal processing. New York: IEEE Press, 1987.

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D, Stearns Samuel, ed. Adaptive signal processing. Englewood Cliffs, N.J: Prentice-Hall, 1985.

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1931-, Haykin Simon S., ed. Adaptive radar signal processing. Hoboken, NJ: John Wiley & Sons, 2007.

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Haykin, Simon, ed. Adaptive Radar Signal Processing. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2006. http://dx.doi.org/10.1002/0470069120.

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Clarkson, Peter M. Optimal and adaptive signal processing. Boca Raton: CRC Press, 1993.

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Widrow, Bernard. Adaptive signal processing and adaptive neural networks. Piscataway, NJ: The Institute of Electrical and Electronics Engineers, 1992.

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Book chapters on the topic "Adaptive signal processing"

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Casey, Stephen D. "Adaptive Signal Processing." In Excursions in Harmonic Analysis, Volume 4, 261–90. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20188-7_11.

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Richter, Michael M., Sheuli Paul, Veton Këpuska, and Marius Silaghi. "Adaptive Signal Processing." In Signal Processing and Machine Learning with Applications, 131–50. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-45372-9_6.

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Esakkirajan, S., T. Veerakumar, and Badri N. Subudhi. "Adaptive Signal Processing." In Digital Signal Processing, 443–68. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6752-0_11.

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Mahafza, Bassem R. "Adaptive Signal Processing." In Radar Systems Analysis and Design Using MATLAB®, 541–66. 4th ed. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003051282-16.

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Mulgrew, Bernard, Peter Grant, and John Thompson. "Adaptive filters." In Digital Signal Processing, 206–39. London: Macmillan Education UK, 1999. http://dx.doi.org/10.1007/978-1-349-14944-5_8.

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Chonavel, Thierry. "Adaptive Estimation." In Statistical Signal Processing, 231–48. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0139-0_16.

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Mulgrew, Bernard. "Adaptive filters." In Digital Signal Processing, 213–45. London: Macmillan Education UK, 2003. http://dx.doi.org/10.1057/978-1-137-44655-8_8.

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Pedersen, Thorkild Find. "Adaptive Processing." In Handbook of Signal Processing in Acoustics, 125–29. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-30441-0_8.

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Adali, Tülay, and Hualiang Li. "Complex-Valued Adaptive Signal Processing." In Adaptive Signal Processing, 1–85. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470575758.ch1.

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Delmas, Jean Pierre. "Subspace Tracking for Signal Processing." In Adaptive Signal Processing, 211–70. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470575758.ch4.

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Conference papers on the topic "Adaptive signal processing"

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"Adaptive antennas, signal processing." In 2005 5th International Conference on Antenna Theory and Techniques. IEEE, 2005. http://dx.doi.org/10.1109/icatt.2005.1496940.

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Hong, John, and Demetri Psaltis. "Acoustooptic Adaptive Signal Processing." In 1985 Technical Symposium East, edited by Jacques E. Ludman. SPIE, 1986. http://dx.doi.org/10.1117/12.949003.

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"Session MP3 Adaptive Signal Processing." In Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004. IEEE, 2004. http://dx.doi.org/10.1109/acssc.2004.1399125.

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Resnikoff, Howard L. "Wavelets and adaptive signal processing." In San Diego, '91, San Diego, CA, edited by Simon Haykin. SPIE, 1991. http://dx.doi.org/10.1117/12.49792.

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Klionskiy, Dmitry M., Dmitry I. Kaplun, and Sergei A. Romanov. "Adaptive techniques of signal processing." In 2017 IEEE VI Forum on Strategic Partnership of Universities and Enterprises of Hi-Tech Branches - Science, Education, Innovations (SPUE). IEEE, 2017. http://dx.doi.org/10.1109/ivforum.2017.8246097.

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"ISETC 2018 Adaptive Signal Processing and Digital Signal Processing Applications." In 2018 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2018. http://dx.doi.org/10.1109/isetc.2018.8584030.

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Johnson, Eric G., and Mustafa A. G. Abushagur. "Genetic algorithms in adaptive signal processing." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.thdd.2.

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Bolstad, Gregory D., and Kenneth B. Neeld. "CORDIC-based digital signal processing (DSP) element for adaptive signal processing." In Critical Review Collection. SPIE, 1995. http://dx.doi.org/10.1117/12.204206.

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Yang, Jie, Xiaoming Zhu, Gerald E. Sobelman, and Keshab K. Parhi. "Sparseness-Controlled Adaptive Tap algorithms for partial update adaptive filters." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397686.

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"Session FAM2-3: Adaptive signal processing." In 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. IEEE, 2009. http://dx.doi.org/10.1109/ecticon.2009.5137114.

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Reports on the topic "Adaptive signal processing"

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Albert, T. R. Adaptive Signal Processing at NOSC. Fort Belvoir, VA: Defense Technical Information Center, March 1992. http://dx.doi.org/10.21236/ada250245.

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Tufts, Donald W. Adaptive, Robust, High-Resolution Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 1990. http://dx.doi.org/10.21236/ada223728.

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Brady, David J., Mark A. Neifeld, and Travis Blalock. Adaptive Multiplexed Wavelength and Spatial Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 2005. http://dx.doi.org/10.21236/ada449523.

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Shamma, Shihab A., and P. S. Krishnaprasad. Signal Processing and Recognition in Adaptive Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, July 1991. http://dx.doi.org/10.21236/ada250505.

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Honig, Michael L. Adaptive Signal Processing Techniques for Robust, High Capacity Spread- Spectrum Multiple Access. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada422622.

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Preisig, James C. High-Frequency Acoustic Propagation and Adaptive Signal Processing: An Integrated Research Program. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada625506.

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Lesser, Victor R., Hamid Nawab, and Donald Weiner. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 1. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada300901.

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Casey, Stephen D. New Techniques in Time-Frequency Analysis: Adaptive Band, Ultra-Wide Band and Multi-Rate Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, March 2016. http://dx.doi.org/10.21236/ad1005007.

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Frantzeskakis, E. N., and J. J. Liu. A Class of Square Root and Division Free Algorithms and Architectures for QRD-Based Adaptive Signal Processing. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada452710.

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Shah, Rajiv R. High-Level Adaptive Signal Processing Architecture with Applications to Radar Non-Gaussian Clutter. Volume 2. A New Technique for Distribution Approximation of Random Data. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada300902.

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