Mohammed Mobien - Academia.edu (original) (raw)
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Papers by Mohammed Mobien
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
Applied Computational Electromagnetics Society Journal
QRD-RLS Adaptive Filtering, 2008
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
IEEE Photonics Technology Letters, 2015
EURASIP Journal on Wireless Communications and Networking, 2011
2010 7th International Symposium on Wireless Communication Systems, 2010
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
2006 IEEE International Symposium on Circuits and Systems, 2006
2013 Saudi International Electronics, Communications and Photonics Conference, 2013
ABSTRACT
Multichannel fast QR decomposition recursive least-squares (MC- FQRD-RLS) algorithms are well kno... more Multichannel fast QR decomposition recursive least-squares (MC- FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. However, these al- gorithms have been restricted to problems seeking an estimate of the output error signal. This is because their transversal weights are embedded in the algorithm variables and are not explicitly available. In this paper we present a novel technique that can extract the filter weights associated with the MC-FQRD-RLS algorithm at any time instant. As a consequence, the range of applications is extended to include problems where explicit knowledge of the filter weights is required. The proposed weight extraction technique is used to identify the beampattern of a broadband adaptive beamformer im- plemented with an MC-FQRD-RLS algorithm. The results confirm that the extracted coefficients of the MC-FQRD-RLS algorithm are identical to those obtained by any RLS algorithm such as the inverse QRD-RLS algor...
IEEE Transactions on Signal Processing, 2000
IEEE Signal Processing Letters, 2000
EURASIP Journal on Advances in Signal Processing, 2012
Fast QR-decomposition based recursive least-squares (FQRD- RLS) algorithms are known for their go... more Fast QR-decomposition based recursive least-squares (FQRD- RLS) algorithms are known for their good numerical properties and low computational complexity. However, they have so far not been used in active noise control (ANC) because the general implementa- tion would require multiple-input multiple-output (MIMO) FQRD- RLS algorithms, which are currently not available in the technical literature. Another reason is the lack of an explicit weight vector update equation in the FQRD-RLS algorithms, which prevents their use in structures where a copy of the coefficients is filtering a dif- ferent input sequence than that of the adaptive filter, e.g., the modi- fied filtered-x ANC (MFX-ANC) structure. In this paper, we der ive a MIMO-FQRD-RLS algorithm based on backward prediction er- ror updates. The proposed algorithm is applied to a multichannel MFX-ANC structure. We show how to avoid the explicit use of the weight vector in the MFX-ANC structure by reproducing the filtered-x signal fro...
2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
The particle swarm approach provides a low complexity solution to the optimization problem among ... more The particle swarm approach provides a low complexity solution to the optimization problem among various existing heuristic algorithms. Recent advances in the algorithm resulted in improved performance at the cost of increased computational complexity, which is undesirable. Literature shows that the particle swarm optimization algorithm based on comprehensive learning provides the best complexity-performance trade-off. We show how to reduce the complexity of this algorithm further, with a slight but acceptable performance loss. This enhancement allows the application of the algorithm in time critical applications, such as, real-time tracking, equalization etc.
Applied Computational Electromagnetics Society Journal
QRD-RLS Adaptive Filtering, 2008
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
IEEE Photonics Technology Letters, 2015
EURASIP Journal on Wireless Communications and Networking, 2011
2010 7th International Symposium on Wireless Communication Systems, 2010
The 10th IEEE International Symposium on Signal Processing and Information Technology, 2010
2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings, 2006
2006 IEEE International Symposium on Circuits and Systems, 2006
2013 Saudi International Electronics, Communications and Photonics Conference, 2013
ABSTRACT
Multichannel fast QR decomposition recursive least-squares (MC- FQRD-RLS) algorithms are well kno... more Multichannel fast QR decomposition recursive least-squares (MC- FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. However, these al- gorithms have been restricted to problems seeking an estimate of the output error signal. This is because their transversal weights are embedded in the algorithm variables and are not explicitly available. In this paper we present a novel technique that can extract the filter weights associated with the MC-FQRD-RLS algorithm at any time instant. As a consequence, the range of applications is extended to include problems where explicit knowledge of the filter weights is required. The proposed weight extraction technique is used to identify the beampattern of a broadband adaptive beamformer im- plemented with an MC-FQRD-RLS algorithm. The results confirm that the extracted coefficients of the MC-FQRD-RLS algorithm are identical to those obtained by any RLS algorithm such as the inverse QRD-RLS algor...
IEEE Transactions on Signal Processing, 2000
IEEE Signal Processing Letters, 2000
EURASIP Journal on Advances in Signal Processing, 2012
Fast QR-decomposition based recursive least-squares (FQRD- RLS) algorithms are known for their go... more Fast QR-decomposition based recursive least-squares (FQRD- RLS) algorithms are known for their good numerical properties and low computational complexity. However, they have so far not been used in active noise control (ANC) because the general implementa- tion would require multiple-input multiple-output (MIMO) FQRD- RLS algorithms, which are currently not available in the technical literature. Another reason is the lack of an explicit weight vector update equation in the FQRD-RLS algorithms, which prevents their use in structures where a copy of the coefficients is filtering a dif- ferent input sequence than that of the adaptive filter, e.g., the modi- fied filtered-x ANC (MFX-ANC) structure. In this paper, we der ive a MIMO-FQRD-RLS algorithm based on backward prediction er- ror updates. The proposed algorithm is applied to a multichannel MFX-ANC structure. We show how to avoid the explicit use of the weight vector in the MFX-ANC structure by reproducing the filtered-x signal fro...