Results 221 to 230 of about 27,877 (267)
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IEEE Signal Processing Letters, 2004
The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. Though so-called "tap-leakage" modifications of LMS have been proposed to mitigate this drift, they inevitably introduce ...
Brian D. Rigling, Philip Schniter
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The least mean squared (LMS) adaptive filtering algorithm may experience uncontrolled parameter drift when its input signal is not persistently exciting, leading to serious consequences when implemented with finite word-length. Though so-called "tap-leakage" modifications of LMS have been proposed to mitigate this drift, they inevitably introduce ...
Brian D. Rigling, Philip Schniter
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2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018
In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity.
Paulo S. R. Diniz +2 more
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In recent years, there is a growing effort in the learning algorithms area to propose new strategies to detect and exploit sparsity in the model parameters. In many situations, the sparsity is hidden in the relations among these coefficients so that some suitable tools are required to reveal the potential sparsity.
Paulo S. R. Diniz +2 more
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2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts
Markus Rupp, Stefan Schwarz
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Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts
Markus Rupp, Stefan Schwarz
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IEEE Transactions on Signal Processing, 1993
Adaptive microstatistic filters are developed for applications in which the second-order statistics of the thresholded signals are not known or may be nonstationary. A multilevel threshold decomposition such that real-valued stochastic processes can be filtered is used, and the computational complexity of the algorithm can be arbitrarily specified by ...
Shoupu Chen, Gonzalo R. Arce
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Adaptive microstatistic filters are developed for applications in which the second-order statistics of the thresholded signals are not known or may be nonstationary. A multilevel threshold decomposition such that real-valued stochastic processes can be filtered is used, and the computational complexity of the algorithm can be arbitrarily specified by ...
Shoupu Chen, Gonzalo R. Arce
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Proceedings of the 35th annual ACM SIGUCCS fall conference, 2007
With the emergence of strong open source contenders in the Learning Management System (LMS) arena, many schools are evaluating whether to stay with one of the commercial LMS products such as Blackboard/WebCT or moving to one of the open source solutions which are free to use, but offer no corporate support. There are many factors contributing to such a
Kelly Wainwright +3 more
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With the emergence of strong open source contenders in the Learning Management System (LMS) arena, many schools are evaluating whether to stay with one of the commercial LMS products such as Blackboard/WebCT or moving to one of the open source solutions which are free to use, but offer no corporate support. There are many factors contributing to such a
Kelly Wainwright +3 more
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Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 2, 2010
We present LM, a tool for mining scenario-based specifications in the form of Live Sequence Charts, a visual language that extends sequence diagrams with modalities. LM comes with a project management component, a wizard-like interface to the mining algorithm, a set of pre- and post-processing extensions, and a visualization module.
Tuan-Anh Doan +3 more
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We present LM, a tool for mining scenario-based specifications in the form of Live Sequence Charts, a visual language that extends sequence diagrams with modalities. LM comes with a project management component, a wizard-like interface to the mining algorithm, a set of pre- and post-processing extensions, and a visualization module.
Tuan-Anh Doan +3 more
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IEEE Transactions on Signal Processing, 1995
The least mean square (LMS) algorithm is known to converge in the mean and in the mean square. However, during short time periods, the error sequence can blow up and cause severe disturbances, especially for non-Gaussian processes. The paper discusses potential short time unstable behavior of the LMS algorithm for spherically invariant random processes
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The least mean square (LMS) algorithm is known to converge in the mean and in the mean square. However, during short time periods, the error sequence can blow up and cause severe disturbances, especially for non-Gaussian processes. The paper discusses potential short time unstable behavior of the LMS algorithm for spherically invariant random processes
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IEEE Communications Letters, 2000
Pilot symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients, such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used.
Ying-Chang Liang, Francois P. S. Chin
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Pilot symbol-assisted adaptive algorithms provide coherent detection for communication systems when the filtering coefficients, such as beamforming weights or equalizer coefficients, are converged. This property can be exploited to speed up the convergence of adaptive algorithms used.
Ying-Chang Liang, Francois P. S. Chin
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Data-selective LMS-Newton and LMS-Quasi-Newton Algorithms
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019The huge volume of data that are available today requires data-selective processing approaches that avoid the costs in computational complexity via appropriately treating the non-innovative data. In this paper, extensions of the well-known adaptive filtering LMS-Newton and LMS-Quasi-Newton Algorithms are developed that enable data selection while also ...
Christos G. Tsinos, Paulo S. R. Diniz
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IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989
A realization-oriented analysis is given of the gradient noise misadjustment and lag misadjustment performance of the LMS (least-mean-square) algorithm. New formulas are given for both of these components of excess mean-square error. It is shown that the traditional formula for lag misadjustment needs to be modified by adding further terms due to ...
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A realization-oriented analysis is given of the gradient noise misadjustment and lag misadjustment performance of the LMS (least-mean-square) algorithm. New formulas are given for both of these components of excess mean-square error. It is shown that the traditional formula for lag misadjustment needs to be modified by adding further terms due to ...
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