Academic literature on the topic 'Blind decorrelation'
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Journal articles on the topic "Blind decorrelation"
Tuzlukov, V. P. "Two approaches to multiuser detection over fading channels." Doklady BGUIR 19, no. 1 (February 23, 2021): 11–20. http://dx.doi.org/10.35596/1729-7648-2021-19-1-11-20.
Full textLapini, Alessandro, Tiziano Bianchi, Fabrizio Argenti, and Luciano Alparone. "Blind Speckle Decorrelation for SAR Image Despeckling." IEEE Transactions on Geoscience and Remote Sensing 52, no. 2 (February 2014): 1044–58. http://dx.doi.org/10.1109/tgrs.2013.2246838.
Full textDouglas, S. C., and A. Cichocki. "Neural networks for blind decorrelation of signals." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2829–42. http://dx.doi.org/10.1109/78.650109.
Full textMei, Tiemin, and Fuliang Yin. "Blind separation of convolutive mixtures by decorrelation." Signal Processing 84, no. 12 (December 2004): 2297–313. http://dx.doi.org/10.1016/j.sigpro.2004.07.024.
Full textRui Wang, Jing Lu, and Yue He. "Variable Step Size Algorithm for Adaptive Blind Decorrelation." International Journal of Digital Content Technology and its Applications 7, no. 6 (March 31, 2013): 1209–16. http://dx.doi.org/10.4156/jdcta.vol7.issue6.138.
Full textYin, Fuliang, Tiemin Mei, and Jun Wang. "Blind-Source Separation Based on Decorrelation and Nonstationarity." IEEE Transactions on Circuits and Systems I: Regular Papers 54, no. 5 (May 2007): 1150–58. http://dx.doi.org/10.1109/tcsi.2007.895510.
Full textChen, Guo Jun, and Han Ying Hu. "New Blind Adaptive Channel Estimation Schemes Based on OFDM Systems." Advanced Materials Research 709 (June 2013): 370–73. http://dx.doi.org/10.4028/www.scientific.net/amr.709.370.
Full textSchobben, D. W. E., and P. W. Sommen. "A frequency domain blind signal separation method based on decorrelation." IEEE Transactions on Signal Processing 50, no. 8 (August 2002): 1855–65. http://dx.doi.org/10.1109/tsp.2002.800417.
Full textKrstić, Vladimir R. "The blind decision feedback equalizer with the entropic: Leaky decorrelation algorithm." Tehnika 70, no. 1 (2015): 105–11. http://dx.doi.org/10.5937/tehnika1501105k.
Full textLi, Jiahui. "The Study of Blind Source Separation Based on Sparsity and Decorrelation." IOP Conference Series: Materials Science and Engineering 394 (August 7, 2018): 052019. http://dx.doi.org/10.1088/1757-899x/394/5/052019.
Full textDissertations / Theses on the topic "Blind decorrelation"
Nessel, James Aaron. "Estimation of Atmospheric Phase Scintillation Via Decorrelation of Water Vapor Radiometer Signals." University of Akron / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=akron1447701180.
Full textWada, Ted S. "System approach to robust acoustic echo cancellation through semi-blind source separation based on independent component analysis." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44851.
Full textBoudjenouia, Fouad. "Restauration d’images avec critères orientés qualité." Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2031/document.
Full textThis thesis concerns the blind restoration of images (formulated as an ill-posed and illconditioned inverse problem), considering a SIMO system. Thus, a blind system identification technique in which the order of the channel is unknown (overestimated) is introduced. Firstly, a simplified version at reduced cost SCR of the cross relation (CR) method is introduced. Secondly, a robust version R-SCR based on the search for a sparse solution minimizing the CR cost function is proposed. Image restoration is then achieved by a new approach (inspired from 1D signal decoding techniques and extended here to the case of 2D images) based on an efficient tree search (Stack algorithm). Several improvements to the ‘Stack’ method have been introduced in order to reduce its complexity and to improve the restoration quality when the images are noisy. This is done using a regularization technique and an all-at-once optimization approach based on the gradient descent which refines the estimated image and improves the algorithm’s convergence towards the optimal solution. Then, image quality measurements are used as cost functions (integrated in the global criterion), in order to study their potential for improving restoration performance. In the context where the image of interest is corrupted by other interfering images, its restoration requires the use of blind sources separation techniques. In this sense, a comparative study of some separation techniques based on the property of second-order decorrelation and sparsity is performed
XU, BIN. "A Blind Space-Time Decorrelating RAKE Receiver in a DS-CDMA System in Multipath Channels." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1078193785.
Full text"Novel self-decorrelation and fractional self-decorrelation pre-processing techniques to enhance the output SINR of single-user-type DS-CDMA detectors in blind space-time RAKE receivers." 2002. http://library.cuhk.edu.hk/record=b5891142.
Full textThesis (M.Phil.)--Chinese University of Hong Kong, 2002.
Includes bibliographical references (leaves 80-83).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 1.1 --- The Problem --- p.1
Chapter 1.2 --- Overview of CDMA --- p.2
Chapter 1.3 --- Problems Encountered in Direct-Sequence (DS)CDMA --- p.3
Chapter 1.3.1 --- Multipath Fading Scenario in DS-CDMA Cellular Mo- bile Communication --- p.3
Chapter 1.3.2 --- Near-Far Problem --- p.4
Chapter 1.4 --- Delimitation and Significance of the Thesis --- p.5
Chapter 1.5 --- Summary --- p.7
Chapter 1.6 --- Scope of the Thesis --- p.8
Chapter 2 --- Literature Review of Blind Space-Time Processing in a wire- less CDMA Receiver --- p.9
Chapter 2.1 --- General Background Information --- p.9
Chapter 2.1.1 --- Time Model of K-User Chip-Synchronous CDMA --- p.9
Chapter 2.1.2 --- Dispersive Channel Modelling --- p.10
Chapter 2.1.3 --- Combination of K-user CDMA Time Model with the Slow Frequency-Selective Fading Channel Model to form a completed Chip-Synchronous CDMA Time Model --- p.13
Chapter 2.1.4 --- Spatial Channel Model with Antenna Array [9] --- p.15
Chapter 2.1.5 --- Joint Space-Time Channel Model in Chip-Synchronous CDMA --- p.19
Chapter 2.1.6 --- Challenges to Blind Space-Time Processing in a base- station CDMA Receiver --- p.23
Chapter 2.2 --- Literature Review of Single-User-Type Detectors used in Blind Space-Time DS-CDMA RAKE Receivers --- p.25
Chapter 2.2.1 --- A Common Problem among the Signal Processing Schemes --- p.28
Chapter 3 --- "Novel ""Self-Decorrelation"" Technique" --- p.29
Chapter 3.1 --- "Problem with ""Blind"" Space-Time RAKE Processing Using Single- User-Type Detectors" --- p.29
Chapter 3.2 --- "Review of Zoltowski & Ramos[10,11,12] Maximum-SINR Single- User-Type CDMA Blind RAKE Receiver Schemes" --- p.31
Chapter 3.2.1 --- Space-Time Data Model --- p.31
Chapter 3.2.2 --- The Blind Element-Space-Only (ESO) RAKE Receiver with Self-Decorrelation Pre-processing Applied --- p.32
Chapter 3.3 --- Physical Meaning of Self-Decorrelation Pre-processing --- p.35
Chapter 3.4 --- Simulation Results --- p.38
Chapter 4 --- """Fractional Self-Decorrelation"" Pre-processing" --- p.45
Chapter 4.1 --- The Blind Maximum-SINR RAKE Receivers in Chen et. al.[l] and Wong et. al.[2] --- p.45
Chapter 4.2 --- Fractional Self-Decorrelation Pre-processing --- p.47
Chapter 4.3 --- The Blind Element-Space-Only (ESO) RAKE Receiver with Fractional Self-Decorrelation Pre-processing Applied --- p.50
Chapter 4.4 --- Physical Meaning of Fractional Self-Decorrelation Pre-processing --- p.54
Chapter 4.5 --- Simulation Results --- p.55
Chapter 5 --- Complexity Analysis and Schematics of Proposed Techniques --- p.64
Chapter 5.1 --- Computational Complexity --- p.64
Chapter 5.1.1 --- Self-Decorrelation Applied in Element-Space-Only (ESO) RAKE Receiver --- p.64
Chapter 5.1.2 --- Fractional Self-Decorrelation Applied in Element-Space- Only (ESO) RAKE Receiver --- p.67
Chapter 5.2 --- Schematics of the Two Proposed Techniques --- p.69
Chapter 6 --- Summary and Conclusion --- p.74
Chapter 6.1 --- Summary of the Thesis --- p.74
Chapter 6.1.1 --- The Self-Decorrelation Pre-processing Technique --- p.75
Chapter 6.1.2 --- The Fractional Self-Decorrelation Pre-processing Tech- nique --- p.76
Chapter 6.2 --- Conclusion --- p.78
Chapter 6.3 --- Future Work --- p.78
Bibliography --- p.80
Chapter A --- Generalized Eigenvalue Problem --- p.84
Chapter A.1 --- Standard Eigenvalue Problem --- p.84
Chapter A.2 --- Generalized Eigenvalue Problem --- p.84
LAPINI, ALESSANDRO. "Advanced multiresolution bayesian methods and sar image modelling for speckle removal." Doctoral thesis, 2014. http://hdl.handle.net/2158/843707.
Full textLee, Chih-Chien, and 李志堅. "Blind energy estimation for decorrelating decision-feedback CDMA multiuser detection using learning type stochastic approximations." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/05499243399646261757.
Full text國立交通大學
電信工程研究所
84
This paper investigates the application of linear reinforcement learning stochastic approximation to the blind adaptive energy estimation for a decorrelating decision-feedback (DDF) multiuser detector over synchronous CDMA radio channel in the presence of multiple access interference (MAI) and additive Gaussian noise. The decision feedback incorporated into the structure of linear decorrelating detector is able to significantly improve the weaker users' performance by cancelling the MAI from the stronger users. However, the DDF receiver requires knowledge of the received energies. In this paper, a new novel blind estimation mechanism is proposed to estimate all the users' energies using a stochastic approximation algorithm without training data. In order to increase the convergence speed of the energy estimation, a linear reinforcement learning technique is conducted to accelerate the stochastic approximation algorithms. Results show that our blind adaptation mechanism is able to accurately estimate all the users' energies even the users of DDF detector are not ranked properly. After performing the blind energy estimation and then re-ordering the users in a nonincreasing order, numerical simulations show that the DDF detector for the weakest user performs closely to the maximum likelihood detector, whose complexity grows exponentically with the number of users.
Book chapters on the topic "Blind decorrelation"
Wang, Fuxiang, and Jun Zhang. "Convolutive Blind Speech Separation by Decorrelation." In Advances in Neuro-Information Processing, 737–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_90.
Full textHosseini, Shahram, and Yannick Deville. "Blind Separation of Nonstationary Sources by Spectral Decorrelation." In Independent Component Analysis and Blind Signal Separation, 279–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_36.
Full textKarvanen, Juha, and Toshihisa Tanaka. "Temporal Decorrelation as Preprocessing for Linear and Post-nonlinear ICA." In Independent Component Analysis and Blind Signal Separation, 774–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30110-3_98.
Full textWang, Rui, and Jing Lu. "A Gradient-based Variable Step Size Algorithm Based on Blind Decorrelation." In Lecture Notes in Electrical Engineering, 63–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40633-1_9.
Full textSaylani, Hicham, Shahram Hosseini, and Yannick Deville. "Blind Separation of Noisy Mixtures of Non-stationary Sources Using Spectral Decorrelation." In Independent Component Analysis and Signal Separation, 322–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00599-2_41.
Full textOno, Nobutaka, and Shigeki Sagayam. "Crystal-like Symmetric Sensor Arrangements for Blind Decorrelation of Isotropic Wavefield." In Signal Processing. InTech, 2010. http://dx.doi.org/10.5772/8524.
Full textConference papers on the topic "Blind decorrelation"
Ou, Shifeng, Xiaohui Zhao, and Ying Gao. "Variable Step Size Technique for Adaptive Blind Decorrelation." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.196.
Full textOu, Shifeng, Xiaohui Zhao, and Ying Gao. "Variable Step Size Technique for Adaptive Blind Decorrelation." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.564.
Full textWang, Yongchuan, and Zili Chen. "Blind fractionally spaced equalizer based on output decorrelation." In Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic technology, and Artificial Intelligence, edited by Jiancheng Fang and Zhongyu Wang. SPIE, 2006. http://dx.doi.org/10.1117/12.716941.
Full textSica, F., L. Alparone, F. Argenti, G. Fornaro, A. Lapini, and D. Reale. "Benefits of blind speckle decorrelation for InSAR processing." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, and Nazzareno Pierdicca. SPIE, 2014. http://dx.doi.org/10.1117/12.2068717.
Full textYang, Jun-an, Xuefan He, and Yunxiao Jiang. "A Fast Blind Deconvolution Algorithm Using Decorrelation and Block Matrix." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.21.
Full textSun, Lijun, and Ying He. "Novel Blind Channel Equalization Iterative Algorithm Based on Decorrelation Method." In 2007 International Conference on Wireless Communications, Networking and Mobile Computing. IEEE, 2007. http://dx.doi.org/10.1109/wicom.2007.333.
Full textOno, Nobutaka, Nobutaka Ito, and Shigeki Sagayama. "Five classes of crystal arrays for blind decorrelation of diffuse noise." In 2008 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2008. http://dx.doi.org/10.1109/sam.2008.4606844.
Full textFeng, Fangchen, and Matthieu Kowalski. "An unified approach for blind source separation using sparsity and decorrelation." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362681.
Full textCavalcanti, F. R. P., J. M. T. Romano, and A. L. Brandao. "Least-squares CMA with decorrelation for fast blind multiuser signal separation." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.760645.
Full textLi, Zhijun, Jianping An, Lei Sun, and Miao Yang. "A Blind Source Separation Algorithm Based on Whitening and Non-linear Decorrelation." In 2010 Second International Conference on Computer Modeling and Simulation (ICCMS). IEEE, 2010. http://dx.doi.org/10.1109/iccms.2010.123.
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