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A Blind Source Separation Method Based on Bounded Component Analysis Optimized by the Improved Beetle Antennae Search [PDF]
Sensors, 2023 Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals.
Mingyang Tang, Yafeng Wu
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Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm [PDF]
Sensors, 2022 The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the
Qingyu Xia +5 more
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Blind Source Separation for Compositional Time Series. [PDF]
Math Geosci, 2021 AbstractMany geological phenomena are regularly measured over time to follow developments and changes. For many of these phenomena, the absolute values are not of interest, but rather the relative information, which means that the data are compositional time series.
Nordhausen K, Fischer G, Filzmoser P.
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Kernel-FastICA-Based Nonlinear Blind Source Separation for Anti-Jamming Satellite Communications [PDF]
SensorsSatellite communication systems, as a core component of global information infrastructure, have undergone unprecedented development. However, the open nature of satellite channels renders them vulnerable to electromagnetic interference, making anti ...
Xiya Sun, Changqing Li, Jiong Li, Qi Su
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Spatial blind source separation [PDF]
Biometrika, 2020 SummaryRecently a blind source separation model was suggested for spatial data, along with an estimator based on the simultaneous diagonalization of two scatter matrices. The asymptotic properties of this estimator are derived here, and a new estimator based on the joint diagonalization of more than two scatter matrices is proposed.
Bachoc, François +5 more
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Multiscale blind source separation [PDF]
The Annals of Statistics, 2018 We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an $ε$-neighborhood of the mixture when the sources take only values in a known finite alphabet.
Behr, Merle, Holmes, Chris, Munk, Axel
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, 2014 Blind Source Separation intends to report the new results of the efforts on the study of Blind Source Separation (BSS). This edited book collects novel research ideas and some training in BSS, independent component analysis (ICA), artificial intelligence and signal processing applications.
Xianchuan Yu, Dan Hu, Jindong Xu
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JCCM: Joint conformer and CNN model for overlapping radio signals recognition
Electronics Letters, 2023 Overlapping radio signals recognition is attracting more attention as the development and ubiquitous application of radio technologies. The traditional blind signal separation (BSS) method is mostly not effective when both radio propagation effects and ...
Junbin Liang +5 more
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Underdetermined Blind Source Separation Algorithm for Speech Signal Based on DSKSVD Dictionary Learning [PDF]
Jisuanji gongcheng, 2018 In order to overcome the shortcoming that the traditional learning algorithm training has limited dictionary size and large amount of computation,the algorithm of underdetermined blind source separation for speech signal based on the dictionary learning ...
LI Hu,XU Yan
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Nonlinear blind source separation for sparse sources [PDF]
2016 24th European Signal Processing Conference (EUSIPCO), 2016 Blind Source Separation (BSS) is the problem of separating signals which are mixed through an unknown function from a number of observations, without any information about the mixing model. Although it has been mathematically proven that the separation can be done when the mixture is linear, there is not any proof for the separability of nonlinearly ...
Ehsandoust, Bahram +3 more
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