Relevant bibliographies by topics / Adaptive equalizations

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Adaptive equalizations'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Adaptive equalizations"

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Qureshi, S. U. H. "Adaptive equalization." Proceedings of the IEEE 73, no. 9 (1985): 1349–87. http://dx.doi.org/10.1109/proc.1985.13298.

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Chen, Wei, Kohei Inoue, and Kenji Hara. "Adaptive Aggregated Histogram Equalization for Color Image Enhancement without Gamut Problem." Journal of the Institute of Industrial Applications Engineers 8, no. 2 (April 25, 2020): 56–62. http://dx.doi.org/10.12792/jiiae.8.56.

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Diana D.C, Diana D. C., Joy Vasantha Rani S. P. Joy Vasantha Rani.S.P, Nithya T. R. Nithya.T.R, and Srimukhee B. Srimukhee.B. "Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 1–4. http://dx.doi.org/10.15373/22778179/apr2014/181.

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Bergmans, Jan W. M., Maria Yu Lin, David Modrie, and Rob Otte. "Asynchronous LMS adaptive equalization." Signal Processing 85, no. 7 (July 2005): 1301–13. http://dx.doi.org/10.1016/j.sigpro.2004.12.006.

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Lim, Seung-Gag. "Performance Improvement of MCMA Equalization Algorithm Using Adaptive Modulus." Journal of the Institute of Webcasting, Internet and Telecommunication 14, no. 3 (June 30, 2014): 57–62. http://dx.doi.org/10.7236/jiibc.2014.14.3.57.

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Lim, Seung-Gag. "Performance Evaluation of H-MMA Adaptive Equalization Algorithm using Adaptive Modulus and Adaptive Step Size." Journal of the Institute of Internet Broadcasting and Communication 17, no. 1 (February 28, 2017): 83–88. http://dx.doi.org/10.7236/jiibc.2017.17.1.83.

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Sun, Lin, Ming Yan, Haisen Li, and Yanjie Xu. "Joint Time-Reversal Space-Time Block Coding and Adaptive Equalization for Filtered Multitone Underwater Acoustic Communications." Sensors 20, no. 2 (January 9, 2020): 379. http://dx.doi.org/10.3390/s20020379.

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Underwater acoustic (UWA) sensor networks demand high-rate communications with high reliability between sensor nodes for massive data transmission. Filtered multitone (FMT) is an attractive multicarrier technique used in high-rate UWA communications, and can obviously shorten the span of intersymbol interference (ISI) with high spectral efficiency and low frequency offset sensitivity by dividing the communication band into several separated wide sub-bands without guard bands. The joint receive diversity and adaptive equalization scheme is often used as a general ISI suppression technique in FMT-UWA communications, but large receive array for high diversity gain has an adverse effect on the miniaturization of UWA sensor nodes. A time-reversal space-time block coding (TR-STBC) technique specially designed for frequency-selective fading channels can replace receive diversity with transmit diversity for high diversity gain, and therefore is helpful for ISI suppression with simple receive configuration. Moreover, the spatio-temporal matched filtering (MF) in TR-STBC decoding can mitigate ISI obviously, and therefore is of benefit to lessen the complexion of adaptive equalization for post-processing. In this paper, joint TR-STBC and adaptive equalization FMT-UWA communication method is proposed based on the merit of TR-STBC. The proposed method is analyzed in theory, and its performance is assessed using simulation analysis and real experimental data collected from an indoor pool communication trial. The validity of the proposed method is proved through comparing the proposed method with the joint single-input–single-output (SISO) and adaptive equalization method and the joint single-input–multiple-output (SIMO) and adaptive equalization method. The results show that the proposed method can achieve better communication performance than the joint SISO and adaptive equalization method, and can achieve similar performance with more simpler receive configuration as the joint SIMO and adaptive equalization method.
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Peng, Na Xin, and Yu Qiang Chen. "Improved Self-Adaptive Image Histogram Equalization Algorithm." Advanced Materials Research 760-762 (September 2013): 1495–500. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1495.

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Histogram equalization (HE) algorithm is wildly used method in image processing of contrast adjustment using images histogram. This method is useful in images with backgrounds and foreground that are both bright or both dark. But the performance of HE is not satisfactory to images with backgrounds and foregrounds that are both bright or both dark. To deal with the above problem, [ gives an improved histogram equalization algorithm named self-adaptive image histogram equalization (SIHE) algorithm. Its main idea is to extend the gray level of the image which firstly be processed by the classical histogram equalization algorithm. This paper gives detailed introduction to SIHE and analyzes the shortage of it, then give an improved version of SIHE named ISIHE, finally do experiments to show the performance of our algorithm.
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Guo, Ye Cai, and Kang Fan. "Blind Equalization Algorithm Based on Adaptive Genetic Algorithm and Wavelet Transform." Applied Mechanics and Materials 44-47 (December 2010): 3215–19. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3215.

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Constant Modulus Algorithm(CMA) has slow convergence speed and easily immerges in local minimum owing to lack of initialization theory. Aiming at these disadvantages, adaptive genetic parameters are introduced into constant modulus blind equalization algorithm based on Genetic Algorithm and Wavelet Transform(GAWT-CMA), and Constant Modulus blind equali- zation Algorithm based on Adaptive Genetic Algorithm and Wavelet Transform(AGAWT-CMA) was proposed. The proposed algorithm processes genetic parameters adaptively, which can not only save the excellence individual with large probability but also avoid stagnancy during the evolution process. So it is propitious to search the whole optimum solution in overall range. The performance of the proposed algorithm was verified by computer simulation with underwater acoustic channels.
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Sun, Lin, Mei Wang, Guoheng Zhang, Haisen Li, and Lan Huang. "Filtered Multitone Modulation Underwater Acoustic Communications Using Low-Complexity Channel-Estimation-Based MMSE Turbo Equalization." Sensors 19, no. 12 (June 17, 2019): 2714. http://dx.doi.org/10.3390/s19122714.

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Filtered multitone (FMT) modulation divides the communication band into several subbands to shorten the span of symbols affected by multipath in underwater acoustic (UWA) communications. However, there is still intersymbol interference (ISI) in each subband of FMT modulation degrading communication performance. Therefore, ISI suppression techniques must be applied to FMT modulation UWA communications. The suppression performance of traditional adaptive equalization methods often exploited in FMT modulation UWA communications is limited when the effect of ISI spans tens of symbols or large constellation sizes are used. Turbo equalization consisting of adaptive equalization and channel decoding can improve equalization performance through information exchanging and iterative processes. To overcome the shortcoming of traditional minimum mean square error (MMSE) equalization and effectively suppress the ISI with relatively low computation complexity, an FMT modulation UWA communication using low-complexity channel-estimation-based (CE-based) MMSE turbo equalization is proposed in this paper. In the proposed method, turbo equalization is first exploited to suppress the ISI in FMT modulation UWA communications, and the equalizer coefficients of turbo equalization are adjusted using the low-complexity CE-based MMSE algorithm. The proposed method is analyzed in theory and verified by simulation analysis and real data collected in the experiment carried out in a pool with multipath propagation. The results demonstrate that the proposed method can achieve better communication performance with a higher bit rate than the FMT modulation UWA communication using traditional MMSE adaptive equalization.
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Dissertations / Theses on the topic "Adaptive equalizations"

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Haghdad, Mehdi, and Kamilo Feher. "NON-FEEDBACK HIGH SPEED ADAPTIVE EQUALIZERS FOR FQPSK AND OTHER SPECTRAL EFFICIENCY SYSTEMS FOR LEO SATELLITE TELEMETRY SYSTEMS." International Foundation for Telemetering, 2001. http://hdl.handle.net/10150/607586.

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International Telemetering Conference Proceedings / October 22-25, 2001 / Riviera Hotel and Convention Center, Las Vegas, Nevada
A non-feedback adaptive equalizer based on Feher Equalizer (FE) is presented and its performance is evaluated. By artificially adding notch/notches to the corrupted spectrum resulted from selectively faded LEO environments, an artificial symmetry is created and as a result the BER/BLER is improved. The location and the depth of artificial additive notches are based on the shape of the spectrum of the corrupted signal. By measuring the power in narrow bands around certain frequencies the existence of notches around those frequencies are predicted. Based on this information notches with proper depths are added to the main spectrum which results in more symmetry in the spectrum. The selection process of artificial notch/notches are based on the shape of the signal spectrum, which means that this equalizer unlike most conventional equalizer does not need any feedback. The nonfeedback nature of this equalizer improves the adaptation time over that of alternative equalizers The results presented in this paper are based on both MatLab simulations and laboratory hardware measurements.
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Kvapil, Jiří. "Adaptivní ekvalizace histogramu digitálních obrazů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2009. http://www.nusl.cz/ntk/nusl-228687.

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The diploma thesis is focused on histogram equalization method and his extension by the adaptive boundary. This thesis contains explanations of basic notions on that histogram equalization method was created. Next part is described the human vision and priciples of his imitation. In practical part of this thesis was created software that makes it possible to use methods of adaptive histogram equalization on real images. At the end is showed some results that was reached.
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Morton, John M. "Adaptive Equalization for Indoor Channels." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/36963.

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This thesis describes the use of adaptive equalization techniques to compensate for the intersymbol interference (ISI) that results when digital data is transmitted over a multipath radio channel. The equalization structures covered in this work are the linear transversal equalizer (LTE), the fractionally spaced equalizer (FSE), the decision-feedback equalizer (DFE), and the maximum-likelihood sequence estimation (MLSE) equalizer. This work also covers adaptive algorithms for equalization including both the least mean squares (LMS) and the recursive least squares (RLS) algorithm. All these equalizer structures and algorithms will be modeled using various simulation modules. Equalization for both stationary and mobile radio channels is considered. Stationary channels are modeled with a simple exponentially decaying profile. The mobile radio channel is represented using a two-ray Rayleigh fading model for an outdoor environment. The SIRCIM channel modeling tool is used to create channel profiles for an indoor mobile radio channel. Adaptive arrays and their similarities to linear equalizers are also studied in this thesis. The properties and performance of simple adaptive array systems using the LMS and RLS algorithms are examined through simulation. This thesis concludes with an in-depth study of the use of adaptive equalization for high-speed data systems operating in an indoor environment. Both stationary and slowly varying radio channels are examined. Simulations of DFE and MLSE equalizers operating in such a system show that both equalizer structures provide better BER performance over a system with no equalization. These simulation results also show that the MLSE equalizer provides better performance than the DFE in almost all cases, but requires a great deal more computations.
Master of Science
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ABASCAL, CARLOS G. "ADAPTIVE EQUALIZATION OF A RADIO FREQUENCY AMPLIFIER." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin983391684.

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Tam, Hing Sang Derek. "Adaptive equalization for serial digital interface." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0010/MQ34140.pdf.

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Gurrapu, Omprakash. "Adaptive filter algorithms for channel equalization." Thesis, Högskolan i Borås, Institutionen Ingenjörshögskolan, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-19219.

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Equalization techniques compensate for the time dispersion introduced bycommunication channels and combat the resulting inter-symbol interference (ISI) effect.Given a channel of unknown impulse response, the purpose of an adaptive equalizer is tooperate on the channel output such that the cascade connection of the channel and theequalizer provides an approximation to an ideal transmission medium. Typically,adaptive equalizers used in digital communications require an initial training period,during which a known data sequence is transmitted. A replica of this sequence is madeavailable at the receiver in proper synchronism with the transmitter, thereby making itpossible for adjustments to be made to the equalizer coefficients in accordance with theadaptive filtering algorithm employed in the equalizer design. This type of equalization isknown as Non-Blind equalization. However, in practical situations, it would be highlydesirable to achieve complete adaptation without access to a desired response. Clearly,some form of Blind equalization has to be built into the receiver design. Blind equalizerssimultaneously estimate the transmitted signal and the channel parameters, which mayeven be time-varying. The aim of the project is to study the performance of variousadaptive filter algorithms for blind channel equalization through computer simulations.
Uppsatsnivå: D
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Wesel, Richard Dale. "Adaptive equalization for modem constellation identification." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/29857.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1989.
Includes bibliographical references (leaves 77-78).
by Richard Dale Wesel.
M.S.
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Kurak, Charles W. Jr. "Adaptive Histogram Equalization, a Parallel Implementation." UNF Digital Commons, 1990. http://digitalcommons.unf.edu/etd/260.

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Adaptive Histogram Equalization (AHE) has been recognized as a valid method of contrast enhancement. The main advantage of AHE is that it can provide better contrast in local areas than that achievable utilizing traditional histogram equalization methods. Whereas traditional methods consider the entire image, AHE utilizes a local contextual region. However, AHE is computationally expensive, and therefore time-consuming. In this work two areas of computer science, image processing and parallel processing, are combined to produce an efficient algorithm. In particular, the AHE algorithm is implemented with a Multiple-Instruction-Multiple-Data (MIMD) parallel architecture. It is proposed that, as MIMD machines become more powerful and prevalent, this methodology can be applied to not only this particular algorithm, but also to many others in its class.
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Shukla, Parveen Kumar. "Adaptive equalization of fading radio channels." Thesis, Imperial College London, 1989. http://hdl.handle.net/10044/1/47660.

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Yakoubian, Jeffrey Scott. "Adaptive histogram equalization for mammographic image processing." Thesis, Georgia Institute of Technology, 1993. http://hdl.handle.net/1853/16387.

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Books on the topic "Adaptive equalizations"

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Tam, Hing Sang Derek. Adaptive equalization for serial digital interface. Ottawa: National Library of Canada, 1998.

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Cheng, Jasmine Sai-Ying. Adaptive equalization system for data transmission over coaxial cables. Ottawa: National Library of Canada, 1998.

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Wong, Joyce Cheuk Wai. Circuits for adaptive receiver equalization in high-speed chip-to-chip signaling. Ottawa: National Library of Canada, 2003.

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Wiedmann, Ralf. An adaptive all-pass filter for decision feedback equalization. 1997.

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Wiedmann, Ralf. An adaptive all-pass filter for decision feedback equalization. 1997.

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Inclusive and Adaptive Teaching. Routledge, 2018.

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Westwood, Peter. Inclusive and Adaptive Teaching: Meeting the Challenge of Diversity in the Classroom. Taylor & Francis Group, 2013.

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Westwood, Peter. Inclusive and Adaptive Teaching: Meeting the Challenge of Diversity in the Classroom. Taylor & Francis Group, 2018.

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Inclusive and Adaptive Teaching: Meeting the Challenge of Diversity in the Classroom. Taylor & Francis Group, 2013.

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Gao, Hairong. Design of high-speed summing circuitry and comparator for adaptive parallel multi-level decision feedback equalization. 1997.

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Book chapters on the topic "Adaptive equalizations"

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Regalia, Philip A. "Turbo Equalization." In Adaptive Signal Processing, 143–210. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470575758.ch3.

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Lee, Edward A., and David G. Messerschmitt. "Adaptive Equalization." In Digital Communication, 517–54. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4684-0004-5_11.

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Lee, Edward A., David G. Messerschmitt, and Robert Gallager. "Adaptive Equalization." In Digital Communication, 371–407. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-0044-1_9.

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Lee, Edward A., and David G. Messerschmitt. "Adaptive Equalization." In Digital Communication, 371–407. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1303-5_9.

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Barry, John R., Edward A. Lee, and David G. Messerschmitt. "Adaptive Equalization." In Digital Communication, 423–60. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4615-0227-2_9.

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Weik, Martin H. "adaptive equalization." In Computer Science and Communications Dictionary, 23. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/1-4020-0613-6_295.

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Gitlin, Richard D., Jeremiah F. Hayes, and Stephen B. Weinstein. "Automatic and Adaptive Equalization." In Data Communications Principles, 517–605. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3292-7_8.

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Rupp, Markus, and Andreas Burg. "Algorithms for Adaptive Equalization in Wireless Applications." In Adaptive Signal Processing, 249–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-11028-7_9.

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Proakis, John G. "Adaptive Algorithms for Blind Channel Equalization." In Linear Algebra for Signal Processing, 139–51. New York, NY: Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-4228-4_8.

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Austin, John D., and Stephen M. Pizer. "A Multiprocessor Adaptive Histogram Equalization Machine." In Information Processing in Medical Imaging, 375–92. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4615-7263-3_25.

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Conference papers on the topic "Adaptive equalizations"

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Amin, Moeness G., and Lin He. "A hybrid adaptive technique for dynamic channel equalizations." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745189.

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Amin and He. "A hybrid adaptive technique for dynamic channel equalizations." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1005227.

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He, Lin, and Moeness G. Amin. "Performance comparison of dual and combined mode adaptive techniques for dynamic channel equalizations." In AeroSense 2002, edited by Raghuveer M. Rao, Soheil A. Dianat, and Michael D. Zoltowski. SPIE, 2002. http://dx.doi.org/10.1117/12.472951.

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"5 - Adaptive Equalization." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628581.

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Yilmaz, Baki B., and Alper T. Erdogan. "Compressed training adaptive equalization." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472613.

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Zhifeng Wu, Houkuan Huang, Xiong Zhang, Bei Yang, and Hongbin Dong. "Adaptive equalization using differential evolution." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631057.

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Neto, Mario Uliani, Leandro de C. T. Gomes, Joao Marcos T. Romano, and Madeleine Bonn. "Adaptive equalization based on watermarking." In ITS '07. 2007 International Telecommunications Symposium. IEEE, 2006. http://dx.doi.org/10.1109/its.2006.4433371.

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Pizer, Stephen M., John D. Austin, Robert Cromartie, Ari Geselowitz, Bart t. H. Romeny, John B. Zimmerman, and Karel Zuiderveld. "Algorithms For Adaptive Histogram Equalization." In Physics and Engineering of Computerized Multidimensional Imaging and Processing, edited by Thomas F. Budinger, Zang-Hee Cho, and Orhan Nalcioglu. SPIE, 1986. http://dx.doi.org/10.1117/12.966688.

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Yilmaz, Baki B., and Alper T. Erdogan. "Compressed Training Adaptive MIMO equalization." In 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2016. http://dx.doi.org/10.1109/spawc.2016.7536758.

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Chow, J. T., and D. A. Lawrence. "Adaptive equalization for disk drives." In Proceedings of American Control Conference. IEEE, 2001. http://dx.doi.org/10.1109/acc.2001.946238.

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Reports on the topic "Adaptive equalizations"

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Beaujean, Pierre-Philippe, and Steven Schock. Smart Acoustic Network Using Combined Fsk-Psk, Adaptive Beamforming and Equalization. Fort Belvoir, VA: Defense Technical Information Center, September 2003. http://dx.doi.org/10.21236/ada629527.

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LeBlanc, Lester R., and Pierre-Philippe J. Beaujean. Smart Acoustic Network Using Combined FSK-PSK, Adaptive, Beamforming and Equalization. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada628285.

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