2018 |
Lampros Flokas, Petros Maragos Online Wideband Spectrum Sensing Using Sparsity Journal Article IEEE Journal of Selected Topics in Signal Processing, 12 (1), pp. 35–44, 2018, ISSN: 19324553. Abstract | BibTeX | Links: [PDF] @article{349, title = {Online Wideband Spectrum Sensing Using Sparsity}, author = {Lampros Flokas and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/FlokasMaragos_OnlineWideSpectrumSensingUsingSparsity_JSTSP_preprint.pdf}, doi = {10.1109/JSTSP.2018.2797422}, issn = {19324553}, year = {2018}, date = {2018-01-01}, journal = {IEEE Journal of Selected Topics in Signal Processing}, volume = {12}, number = {1}, pages = {35--44}, abstract = {Wideband spectrum sensing is an essential part of cognitive radio systems. Exact spectrum estimation is usually inefficient as it requires sampling rates at or above the Nyquist rate. Using prior information on the structure of the signal could allow near exact reconstruction at much lower sampling rates. Sparsity of the sampled signal in the frequency domain is one of the popular priors studied for cognitive radio applications. Reconstruction of signals under sparsity assumptions has been studied rigorously by researchers in the field of Compressed Sensing (CS). CS algorithms that operate on batches of samples are known to be robust but can be computationally costly, making them unsuitable for cheap low power cognitive radio devices that require spectrum sensing in real time. On the other hand, online algorithms that are based on variations of the Least Mean Squares (LMS) algorithm have very simple updates so they are computationally efficient and can easily adapt in real time to changes of the underlying spectrum. In this paper we will present two variations of the LMS algorithm that enforce sparsity in the estimated spectrum given an upper bound on the number of non- zero coefficients. Assuming that the number of non-zero elements in the spectrum is known we show that under conditions the hard threshold operation can only reduce the error of our estimation. We will also show that we can estimate the number of non-zero elements of the spectrum at each iteration based on our online estimations. Finally, we numerically compare our algorithm with other online sparsity-inducing algorithms in the literature.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Wideband spectrum sensing is an essential part of cognitive radio systems. Exact spectrum estimation is usually inefficient as it requires sampling rates at or above the Nyquist rate. Using prior information on the structure of the signal could allow near exact reconstruction at much lower sampling rates. Sparsity of the sampled signal in the frequency domain is one of the popular priors studied for cognitive radio applications. Reconstruction of signals under sparsity assumptions has been studied rigorously by researchers in the field of Compressed Sensing (CS). CS algorithms that operate on batches of samples are known to be robust but can be computationally costly, making them unsuitable for cheap low power cognitive radio devices that require spectrum sensing in real time. On the other hand, online algorithms that are based on variations of the Least Mean Squares (LMS) algorithm have very simple updates so they are computationally efficient and can easily adapt in real time to changes of the underlying spectrum. In this paper we will present two variations of the LMS algorithm that enforce sparsity in the estimated spectrum given an upper bound on the number of non- zero coefficients. Assuming that the number of non-zero elements in the spectrum is known we show that under conditions the hard threshold operation can only reduce the error of our estimation. We will also show that we can estimate the number of non-zero elements of the spectrum at each iteration based on our online estimations. Finally, we numerically compare our algorithm with other online sparsity-inducing algorithms in the literature. |
2006 |
A Katsamanis, G Papandreou, V Pitsikalis, P Maragos Multimodal fusion by adaptive compensation for feature uncertainty with application to audiovisual speech recognition Conference Proc. 14th European Signal Processing Conference (EUSIPCO-2006), Florence, Italy, Sep. 2006, 2006, ISBN: 22195491 (ISSN). Abstract | BibTeX | Links: [PDF] @conference{225, title = {Multimodal fusion by adaptive compensation for feature uncertainty with application to audiovisual speech recognition}, author = { A Katsamanis and G Papandreou and V Pitsikalis and P Maragos}, url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84862631884&partnerID=40&md5=ccaeee023c42f0923a6dcdec81ac7fdc}, isbn = {22195491 (ISSN)}, year = {2006}, date = {2006-01-01}, booktitle = {Proc. 14th European Signal Processing Conference (EUSIPCO-2006), Florence, Italy, Sep. 2006}, abstract = {In pattern recognition one usually relies on measuring a set of informative features to perform tasks such as classification. While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact has received relatively little attention to date. In this work we explicitly take into account uncertainty in feature measurements and we show in a rigorous probabilistic framework how the models used for classification should be adjusted to compensate for this effect. Our approach proves to be particularly fruitful in multimodal fusion scenarios, such as audio-visual speech recognition, where multiple streams of complementary features are integrated. For such applications, provided that an estimate of the measurement noise uncertainty for each feature stream is available, we show that the proposed framework leads to highly adaptive multimodal fusion rules which are widely applicable and easy to implement. We further show that previous multimodal fusion methods relying on stream weights fall under our scheme if certain assumptions hold; this provides novel insights into their applicability for various tasks and suggests new practical ways for estimating the stream weights adaptively. Preliminary experimental results in audio-visual speech recognition demonstrate the potential of our approach.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In pattern recognition one usually relies on measuring a set of informative features to perform tasks such as classification. While the accuracy of feature measurements heavily depends on changing environmental conditions, studying the consequences of this fact has received relatively little attention to date. In this work we explicitly take into account uncertainty in feature measurements and we show in a rigorous probabilistic framework how the models used for classification should be adjusted to compensate for this effect. Our approach proves to be particularly fruitful in multimodal fusion scenarios, such as audio-visual speech recognition, where multiple streams of complementary features are integrated. For such applications, provided that an estimate of the measurement noise uncertainty for each feature stream is available, we show that the proposed framework leads to highly adaptive multimodal fusion rules which are widely applicable and easy to implement. We further show that previous multimodal fusion methods relying on stream weights fall under our scheme if certain assumptions hold; this provides novel insights into their applicability for various tasks and suggests new practical ways for estimating the stream weights adaptively. Preliminary experimental results in audio-visual speech recognition demonstrate the potential of our approach. |
1997 |
B. Santhanam, P. Maragos Demodulation of discrete multicomponent AM-FM signals using periodic algebraic separation and energy demodulation Conference 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 3 , 1997, ISSN: 1520-6149. Abstract | BibTeX | Links: [PDF] @conference{271, title = {Demodulation of discrete multicomponent AM-FM signals using periodic algebraic separation and energy demodulation}, author = { B. Santhanam and P. Maragos}, url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=599542}, doi = {10.1109/ICASSP.1997.599542}, issn = {1520-6149}, year = {1997}, date = {1997-01-01}, booktitle = {1997 IEEE International Conference on Acoustics, Speech, and Signal Processing}, volume = {3}, pages = {2409--2412}, abstract = {Existing multicomponent AM-FM demodulation algorithms either assume spectrally distinct components or components separable via linear filtering and break down when the components overlap spectrally or if one of the components is stronger than the other. In this paper, we present a nonlinear algorithm for multicomponent AM-FM demodulation which avoids the above shortcomings and works well even for extremely small spectral separation of the components. The proposed algorithm separates the multicomponent demodulation problem into two tasks: periodicity-based algebraic separation of the components and then monocomponent demodulation via energy-based methods}, keywords = {}, pubstate = {published}, tppubtype = {conference} } Existing multicomponent AM-FM demodulation algorithms either assume spectrally distinct components or components separable via linear filtering and break down when the components overlap spectrally or if one of the components is stronger than the other. In this paper, we present a nonlinear algorithm for multicomponent AM-FM demodulation which avoids the above shortcomings and works well even for extremely small spectral separation of the components. The proposed algorithm separates the multicomponent demodulation problem into two tasks: periodicity-based algebraic separation of the components and then monocomponent demodulation via energy-based methods |
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