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Asilomar Conf. on Signals, Systems, Computers","citation":{"ama":"Hasija T, Gölz M, Muma M, Schreier PJ, Zoubir AM. Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks. In: <i>Proc. Asilomar Conf. on Signals, Systems, Computers</i>. ; 2019.","chicago":"Hasija, Tanuj, Martin Gölz, Michael Muma, Peter J Schreier, and Abdelhak M Zoubir. “Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks.” In <i>Proc. Asilomar Conf. on Signals, Systems, Computers</i>, 2019.","ieee":"T. Hasija, M. Gölz, M. Muma, P. J. Schreier, and A. M. Zoubir, “Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks,” 2019.","apa":"Hasija, T., Gölz, M., Muma, M., Schreier, P. J., &#38; Zoubir, A. M. (2019). Source Enumeration and Robust Voice Activity Detection in Wireless Acoustic Sensor Networks. <i>Proc. Asilomar Conf. on Signals, Systems, Computers</i>.","short":"T. Hasija, M. Gölz, M. Muma, P.J. 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Lameiro, T. Hasija, T. Marrinan, and P. J. Schreier, “Estimating the Number of Correlated Components Based on Random Projections,” in <i>ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>, 2019, pp. 5152–5156.","chicago":"Lameiro, Christian, Tanuj Hasija, Tim Marrinan, and Peter J Schreier. “Estimating the Number of Correlated Components Based on Random Projections.” In <i>ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)</i>, 5152–5156, 2019.","ama":"Lameiro C, Hasija T, Marrinan T, Schreier PJ. Estimating the Number of Correlated Components Based on Random Projections. 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Vieluf, T. Hasija, R. Jakobsmeyer, P. Schreier, C. Reinsberger, Front Physiol 10 (2019) 240.","mla":"Vieluf, S., et al. “Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.” <i>Front Physiol</i>, vol. 10, 2019, p. 240.","bibtex":"@article{Vieluf_Hasija_Jakobsmeyer_Schreier_Reinsberger_2019, title={Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.}, volume={10}, journal={Front Physiol}, author={Vieluf, S and Hasija, Tanuj and Jakobsmeyer, Rasmus and Schreier, PJ and Reinsberger, Claus}, year={2019}, pages={240} }","apa":"Vieluf, S., Hasija, T., Jakobsmeyer, R., Schreier, P., &#38; Reinsberger, C. (2019). Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network. <i>Front Physiol</i>, <i>10</i>, 240.","ama":"Vieluf S, Hasija T, Jakobsmeyer R, Schreier P, Reinsberger C. Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network. <i>Front Physiol</i>. 2019;10:240.","ieee":"S. Vieluf, T. Hasija, R. Jakobsmeyer, P. Schreier, and C. Reinsberger, “Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.,” <i>Front Physiol</i>, vol. 10, p. 240, 2019.","chicago":"Vieluf, S, Tanuj Hasija, Rasmus Jakobsmeyer, PJ Schreier, and Claus Reinsberger. “Exercise-Induced Changes of Multimodal Interactions Within the Autonomic Nervous Network.” <i>Front Physiol</i> 10 (2019): 240."},"intvolume":"        10","page":"240","year":"2019"},{"status":"public","publication":"Proc. European Signal Process. Conf. (EUSIPCO)","type":"conference","language":[{"iso":"eng"}],"_id":"40702","department":[{"_id":"263"}],"user_id":"43497","year":"2018","page":"1082–1086","citation":{"short":"T. Marrinan, T. Hasija, C. Lameiro, P.J. Schreier, in: Proc. European Signal Process. Conf. (EUSIPCO), 2018, pp. 1082–1086.","bibtex":"@inproceedings{Marrinan_Hasija_Lameiro_Schreier_2018, title={Complete Model Selection in Multiset Canonical Correlation Analysis}, booktitle={Proc. European Signal Process. Conf. (EUSIPCO)}, author={Marrinan, T. and Hasija, Tanuj and Lameiro, C. and Schreier, P. J.}, year={2018}, pages={1082–1086} }","mla":"Marrinan, T., et al. “Complete Model Selection in Multiset Canonical Correlation Analysis.” <i>Proc. European Signal Process. Conf. (EUSIPCO)</i>, 2018, pp. 1082–1086.","apa":"Marrinan, T., Hasija, T., Lameiro, C., &#38; Schreier, P. J. (2018). Complete Model Selection in Multiset Canonical Correlation Analysis. <i>Proc. European Signal Process. Conf. (EUSIPCO)</i>, 1082–1086.","ama":"Marrinan T, Hasija T, Lameiro C, Schreier PJ. Complete Model Selection in Multiset Canonical Correlation Analysis. In: <i>Proc. European Signal Process. Conf. (EUSIPCO)</i>. ; 2018:1082–1086.","ieee":"T. Marrinan, T. Hasija, C. Lameiro, and P. J. Schreier, “Complete Model Selection in Multiset Canonical Correlation Analysis,” in <i>Proc. European Signal Process. Conf. (EUSIPCO)</i>, 2018, pp. 1082–1086.","chicago":"Marrinan, T., Tanuj Hasija, C. Lameiro, and P. J. Schreier. “Complete Model Selection in Multiset Canonical Correlation Analysis.” In <i>Proc. European Signal Process. Conf. (EUSIPCO)</i>, 1082–1086, 2018."},"title":"Complete Model Selection in Multiset Canonical Correlation Analysis","date_updated":"2023-01-30T12:07:14Z","author":[{"first_name":"T.","last_name":"Marrinan","full_name":"Marrinan, T."},{"first_name":"Tanuj","id":"43497","full_name":"Hasija, Tanuj","last_name":"Hasija"},{"last_name":"Lameiro","full_name":"Lameiro, C.","first_name":"C."},{"full_name":"Schreier, P. J.","last_name":"Schreier","first_name":"P. J."}],"date_created":"2023-01-30T11:51:39Z"},{"intvolume":"        24","page":"1606–1610","citation":{"short":"T. Hasija, C. Lameiro, P.J. Schreier, IEEE Signal Process. Lett. 24 (2017) 1606–1610.","mla":"Hasija, Tanuj, et al. “Determining the Dimension of the Improper Signal Subspace in Complex-Valued Data.” <i>IEEE Signal Process. Lett.</i>, vol. 24, 2017, pp. 1606–1610.","bibtex":"@article{Hasija_Lameiro_Schreier_2017, title={Determining the dimension of the improper signal subspace in complex-valued data}, volume={24}, journal={IEEE Signal Process. Lett.}, author={Hasija, Tanuj and Lameiro, Christian and Schreier, Peter J.}, year={2017}, pages={1606–1610} }","apa":"Hasija, T., Lameiro, C., &#38; Schreier, P. J. (2017). Determining the dimension of the improper signal subspace in complex-valued data. <i>IEEE Signal Process. Lett.</i>, <i>24</i>, 1606–1610.","ama":"Hasija T, Lameiro C, Schreier PJ. Determining the dimension of the improper signal subspace in complex-valued data. <i>IEEE Signal Process Lett</i>. 2017;24:1606–1610.","ieee":"T. Hasija, C. Lameiro, and P. J. Schreier, “Determining the dimension of the improper signal subspace in complex-valued data,” <i>IEEE Signal Process. Lett.</i>, vol. 24, pp. 1606–1610, 2017.","chicago":"Hasija, Tanuj, Christian Lameiro, and Peter J. Schreier. “Determining the Dimension of the Improper Signal Subspace in Complex-Valued Data.” <i>IEEE Signal Process. Lett.</i> 24 (2017): 1606–1610."},"year":"2017","title":"Determining the dimension of the improper signal subspace in complex-valued data","volume":24,"author":[{"full_name":"Hasija, Tanuj","id":"43497","last_name":"Hasija","first_name":"Tanuj"},{"first_name":"Christian","full_name":"Lameiro, Christian","last_name":"Lameiro"},{"full_name":"Schreier, Peter J.","last_name":"Schreier","first_name":"Peter J."}],"date_created":"2023-01-30T11:51:41Z","date_updated":"2023-01-30T12:08:06Z","status":"public","abstract":[{"lang":"eng","text":"Abstract: A complex-valued signal is improper if it is correlated with its complex conjugate. The dimension of the improper signal subspace, i.e., the number of improper components in a complex-valued measurement, is an important parameter and is unknown in most of the applications. In this letter, we introduce two approaches to estimate this dimension: one based on an information-theoretic criterion and the other based on hypothesis testing. We also present reduced-rank versions of these approaches that work for scenarios where the number of observations is comparable to or even smaller than the dimension of the data. Unlike other techniques for determining model orders, our techniques also work in the presence of additive colored noise."}],"publication":"IEEE Signal Process. Lett.","type":"journal_article","language":[{"iso":"eng"}],"department":[{"_id":"263"}],"user_id":"43497","_id":"40711"},{"volume":128,"date_created":"2023-01-30T11:51:42Z","author":[{"full_name":"Song, Yang","last_name":"Song","first_name":"Yang"},{"full_name":"Schreier, Peter J.","last_name":"Schreier","first_name":"Peter J."},{"first_name":"David","full_name":"Ramírez, David","last_name":"Ramírez"},{"full_name":"Hasija, Tanuj","id":"43497","last_name":"Hasija","first_name":"Tanuj"}],"date_updated":"2023-01-30T12:07:39Z","title":"Canonical correlation analysis of high-dimensional data with very small sample support","intvolume":"       128","page":"449–458","citation":{"chicago":"Song, Yang, Peter J. Schreier, David Ramírez, and Tanuj Hasija. “Canonical Correlation Analysis of High-Dimensional Data with Very Small Sample Support.” <i>Signal Process.</i> 128 (2016): 449–458.","ieee":"Y. Song, P. J. Schreier, D. Ramírez, and T. Hasija, “Canonical correlation analysis of high-dimensional data with very small sample support,” <i>Signal Process.</i>, vol. 128, pp. 449–458, 2016.","ama":"Song Y, Schreier PJ, Ramírez D, Hasija T. Canonical correlation analysis of high-dimensional data with very small sample support. <i>Signal Process</i>. 2016;128:449–458.","short":"Y. Song, P.J. Schreier, D. Ramírez, T. Hasija, Signal Process. 128 (2016) 449–458.","bibtex":"@article{Song_Schreier_Ramírez_Hasija_2016, title={Canonical correlation analysis of high-dimensional data with very small sample support}, volume={128}, journal={Signal Process.}, author={Song, Yang and Schreier, Peter J. and Ramírez, David and Hasija, Tanuj}, year={2016}, pages={449–458} }","mla":"Song, Yang, et al. “Canonical Correlation Analysis of High-Dimensional Data with Very Small Sample Support.” <i>Signal Process.</i>, vol. 128, 2016, pp. 449–458.","apa":"Song, Y., Schreier, P. J., Ramírez, D., &#38; Hasija, T. (2016). Canonical correlation analysis of high-dimensional data with very small sample support. <i>Signal Process.</i>, <i>128</i>, 449–458."},"year":"2016","department":[{"_id":"263"}],"user_id":"43497","_id":"40720","language":[{"iso":"eng"}],"publication":"Signal Process.","type":"journal_article","status":"public"},{"status":"public","publication":"Proc.\\ IEEE Work.\\ Stat.\\ Signal Process.","type":"conference","language":[{"iso":"eng"}],"department":[{"_id":"263"}],"user_id":"43497","_id":"40723","citation":{"mla":"Hasija, Tanuj, et al. “Detecting the Dimension of the Subspace Correlated across Multiple Data Sets in the Sample Poor Regime.” <i>Proc.\\ IEEE Work.\\ Stat.\\ Signal Process.</i>, 2016.","short":"T. Hasija, Y. Song, P.J. Schreier, D. Ramírez, in: Proc.\\ IEEE Work.\\ Stat.\\ Signal Process., Palma de Mallorca, Spain, 2016.","bibtex":"@inproceedings{Hasija_Song_Schreier_Ramírez_2016, place={Palma de Mallorca, Spain}, title={Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime}, booktitle={Proc.\\ IEEE Work.\\ Stat.\\ Signal Process.}, author={Hasija, Tanuj and Song, Yang and Schreier, Peter J. and Ramírez, David}, year={2016} }","apa":"Hasija, T., Song, Y., Schreier, P. J., &#38; Ramírez, D. (2016). Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime. <i>Proc.\\ IEEE Work.\\ Stat.\\ Signal Process.</i>","ama":"Hasija T, Song Y, Schreier PJ, Ramírez D. Detecting the dimension of the subspace correlated across multiple data sets in the sample poor regime. In: <i>Proc.\\ IEEE Work.\\ Stat.\\ Signal Process.</i> ; 2016.","chicago":"Hasija, Tanuj, Yang Song, Peter J. 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Determining the number of signals correlated across multiple data sets for small sample support. <i>Proc.\\ Eur.\\ Signal Process.\\ Conf.</i>","bibtex":"@inproceedings{Song_Hasija_Schreier_Ramírez_2016, place={Budapest, Hungary}, title={Determining the number of signals correlated across multiple data sets for small sample support}, booktitle={Proc.\\ Eur.\\ Signal Process.\\ Conf.}, author={Song, Yang and Hasija, Tanuj and Schreier, Peter J. and Ramírez, David}, year={2016} }","short":"Y. Song, T. Hasija, P.J. Schreier, D. Ramírez, in: Proc.\\ Eur.\\ Signal Process.\\ Conf., Budapest, Hungary, 2016.","mla":"Song, Yang, et al. “Determining the Number of Signals Correlated across Multiple Data Sets for Small Sample Support.” <i>Proc.\\ Eur.\\ Signal Process.\\ Conf.</i>, 2016."},"language":[{"iso":"eng"}],"_id":"40721","department":[{"_id":"263"}],"user_id":"43497","status":"public","publication":"Proc.\\ Eur.\\ Signal Process.\\ Conf.","type":"conference"}]