Relevant bibliographies by topics / Signal processing Adaptive signal processing

Contents

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

Academic literature on the topic 'Signal processing Adaptive signal processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Signal processing Adaptive signal processing"

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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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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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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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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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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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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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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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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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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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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 "Signal processing 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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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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Krishnan, Sridhar. "Adaptive signal processing techniques for analysis of knee joint vibroarthrographic signals." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0016/NQ47897.pdf.

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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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Pazaitis, Dimitrios I. "Performance improvement in adaptive signal processing algorithms." Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368771.

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Yaminysharif, Mohammad. "Accelerated gradient techniques and adaptive signal processing." Thesis, University of Strathclyde, 1987. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=21496.

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The main objective of this thesis is to demonstrate the application of the accelerated gradient techniques to various fields of adaptive signal processing. A variety of adaptive algorithms based on the accelerated gradient techniques are developed and analysed in terms of the convergence speed, computational complexity and numerical stability. Extensive simulation results are presented to demonstrate the performance of the proposed algorithms when applied to the fields of adaptive noise cancelling, broad band adaptive array processing and narrow band adaptive spectral estimation. These results are very encouraging in terms of convergence speed and numerical stability of the developed algorithms. The proposed algorithms appear to be attractive alternatives to the conventional recursive least squares algorithms. In addition, the thesis includes a review chapter in which the conventional approaches (ranging from the least mean squares algorithm to the computationally demanding recursive least squares algorithm) to three types of minimization problems (namely unconstrained, linearly constrained and quadratically constrained) are discussed.
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Books on the topic "Signal processing Adaptive signal processing"

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

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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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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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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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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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1931-, Haykin Simon S., and Kosko Bart, eds. Intelligent signal processing. New York: IEEE Press, 2001.

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Nitzberg, Ramon. Adaptive signal processing for radar. Boston: Artech House, 1991.

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Uncini, Aurelio. Fundamentals of Adaptive Signal Processing. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-02807-1.

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Book chapters on the topic "Signal processing 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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Forrest, J. R. "Optical Signal Processing." In Adaptive Methods in Underwater Acoustics, 607–19. Dordrecht: Springer Netherlands, 1985. http://dx.doi.org/10.1007/978-94-009-5361-1_52.

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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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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, 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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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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Wax, M. "Adaptive Processing in Sensor Arrays." In Adaptive Signal Processing, 97–171. Vienna: Springer Vienna, 1991. http://dx.doi.org/10.1007/978-3-7091-2840-4_3.

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Benesty, Jacob, Yiteng Huang, and Dennis R. Morgan. "On a Class of Exponentiated Adaptive Algorithms for the Identification of Sparse Impulse Responses." In Adaptive Signal Processing, 1–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11028-7_1.

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Affes, Sofiène, and Paul Mermelstein. "Adaptive Space-Time Processing for Wireless CDMA." In Adaptive Signal Processing, 283–321. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11028-7_10.

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Conference papers on the topic "Signal processing 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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"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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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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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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"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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Li, Wei, Guang Dai, Ying Zhang, Feifei Long, and Yanru Wang. "Empirical Mode Decomposition of AE Signal Processing Based on Hilbert-Huang Transformation." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77001.

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In this paper a new signal processing method of Hilbert-Huang transform is applied to analyze AE lead-breaking signals and loaded concrete AE signals. Empirical model of decomposition (EMD) and cubic interpolation were used in AE signals analysis which was based on the local characteristic time scale. After the decomposition inherent model function (IMF) can effectively preserve non-linear and non-stationary features of signals during the processing. Meanwhile, noises of signals were eliminated by using multi-dimension filter property of EMD. The above two signal’s IMF can be clearly shown on a time-frequency vs. energy distribution spectrum and marginal spectrum by Hilbert-Huang transformation. The results show that HHT can efficiently reflecting the intrinsic properties of signals. Besides, HHT method, which is more adaptive than other methods, had a bigger superiority in adaptability and frequency concentration, and it has batter localization property and visual result of the time-frequency domain.
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Lee, Sang-Kwon, and Paul R. White. "Fault Identification for Rotating Machinery Using Adaptive Signal Processing and Time-Frequency Analysis." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-4236.

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Abstract Impulsive sound and vibration signals in rotating machinery are often associated with faults which lead to due to irregular impacting. Thus these impulsive sound and vibration signals can be used as indicators of machinery faults. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the measurement of impulsive sounds embedded in background noise, we enhance the impulsive signals using adaptive signal processing and then analyze them in time and frequency domain by using time-frequency representation. This technique is applied to the diagnosis of faults within internal combustion engine and industrial gear.
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Ling, Jun, and Jian Li. "Multistatic adaptive active sonar signal processing." In OCEANS 2011 - SPAIN. IEEE, 2011. http://dx.doi.org/10.1109/oceans-spain.2011.6003640.

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Reports on the topic "Signal processing 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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