@inproceedings{40766, abstract = {{Amplitude-to-amplitude interactions between neural oscillations are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetencephalography (EEG/MEG) or intracranial multichannel recordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to-noise ratio of the signals is low.}}, author = {{Dähne, S. and Nikulin, V. V. and Ramírez, D. and Schreier, P. J. and Müller, K.-R. and Haufe, S.}}, booktitle = {{Proc. Int. Work. Pattern Recognition In Neuroimaging}}, title = {{{Optimizing spatial filters for the extraction of envelope-coupled neural oscillations}}}, doi = {{10.1109/PRNI.2014.6858514}}, year = {{2014}}, } @article{40775, abstract = {{The separation of a complex mixture based solely on second-order statistics can be achieved using the Strong Uncorrelating Transform (SUT) if and only if all sources have distinct circularity coefficients. However, in most problems we do not know the circularity coefficients, and they must be estimated from observed data. In this work, we propose a detector, based on the generalized likelihood ratio test (GLRT), to test the separability of a complex Gaussian mixture using the SUT. For the separable case (distinct circularity coefficients), the maximum likelihood (ML) estimates are straightforward. On the other hand, for the non-separable case (at least one circularity coefficient has multiplicity greater than one), the ML estimates are much more difficult to obtain. To set the threshold, we exploit Wilks’ theorem, which gives the asymptotic distribution of the GLRT under the null hypothesis. Finally, numerical simulations show the good performance of the proposed detector and the accuracy of Wilks’ approximation.}}, author = {{Ramírez, D. and Schreier, P. J. and Vía, J. and Santamaría, I.}}, journal = {{Signal Process.}}, pages = {{49–57}}, title = {{{Testing blind separability of complex Gaussian mixtures}}}, doi = {{10.1016/j.sigpro.2013.08.010}}, volume = {{95}}, year = {{2014}}, } @article{40771, author = {{Manco-Vásquez, J. and Lázaro-Gredilla, M. and Ramírez, D. and Vía, J. and Santamaría, I.}}, journal = {{Signal Process.}}, pages = {{228–240}}, title = {{{A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks}}}, doi = {{10.1016/j.sigpro.2013.10.005}}, volume = {{96, Part B}}, year = {{2014}}, } @inproceedings{40770, author = {{Stein, Manuel and Lenz, Andreas and Mezghani, Amine and Nossek, Josef A.}}, booktitle = {{Proc.\ IEEE Int.\ Conf.\ Acoustics, Speech and Signal Process.}}, title = {{{Optimum analog receive filters for detection and inference under a sampling rate constraint}}}, year = {{2014}}, } @inproceedings{40772, author = {{Lameiro, Christian and Utschick, Wolfgang and Santamaría, Ignacio}}, booktitle = {{Proc. Int. ITG Work. Smart Antennas}}, title = {{{Spatial Shaping and Precoding Design for Underlay MIMO Interference Channels}}}, year = {{2014}}, } @inproceedings{40773, author = {{Stein, Manuel and Castañeda, Mario and Nossek, Josef A.}}, booktitle = {{Proc.\ ITG Int.\ Work. Smart Ant.}}, title = {{{Information-preserving spatial filtering for direction-of-arrival estimation}}}, year = {{2014}}, } @article{40776, author = {{Schreier, Peter J.}}, journal = {{ForschungsForum Paderborn}}, pages = {{24–30}}, title = {{{Neue Anwendungsgebiete für Computer Assisted Surgery (CAS)}}}, volume = {{17}}, year = {{2014}}, } @inproceedings{40774, author = {{Stein, Manuel and Nossek, Josef A.}}, booktitle = {{Proc. of IEEE/ION PLANS 2014}}, title = {{{Will the 1-bit GNSS receiver prevail?}}}, year = {{2014}}, } @inproceedings{40768, abstract = {{We derive the generalized likelihood ratio test (GLRT) for detecting cyclostationarity in scalar-valued time series. The main idea behind our approach is Gladyshev’s relationship, which states that when the scalar-valued cyclostationary sig- nal is blocked at the known cycle period it produces a vector- valued wide-sense stationary process. This result amounts to saying that the covariance matrix of the vector obtained by stacking all observations of the time series is block-Toeplitz if the signal is cyclostationary, and Toeplitz if the signal is wide- sense stationary. The derivation of the GLRT requires the maximum likelihood estimates of Toeplitz and block-Toeplitz matrices. This can be managed asymptotically (for large num- berofsamples)exploitingSzego ̈’stheoremanditsgeneraliza- tion for vector-valued processes. Simulation results show the good performance of the proposed GLRT.}}, author = {{Ramírez, D. and Scharf, L. L. and Vía, J. and Santamaría, I. and Schreier, P. J.}}, booktitle = {{Proc.\ IEEE Int.\ Conf.\ Acoustics, Speech and Signal Process.}}, title = {{{An asymptotic GLRT for the detection of cyclostationary signals}}}, doi = {{10.1109/ICASSP.2014.6854234}}, year = {{2014}}, } @inproceedings{40767, abstract = {{Successive interference cancellation (SIC) has been extensively applied to estimate transmit signals in communication systems. When the channel state information (CSI) and noise statistics are imperfectly estimated, the standard SIC estimators that ignore the model mismatch may perform poorly. This paper introduces regularized SIC estimation to provide robustness against the model mismatch. Suboptimal, low-complexity implementations using (sorted) QR decomposition and approximate choice of regularization parameters are also introduced. Simulation examples demonstrate that the regularized SIC estimators can significantly outperform the standard version.}}, author = {{Tong, Jun and Guo, Qinghua and Schreier, Peter J. and Xi, Jiangtao}}, booktitle = {{Proc.\ IEEE Work.\ Stat.\ Signal Process.}}, title = {{{Regularized successive interference cancellation (SIC) under mismatched modeling}}}, doi = {{10.1109/SSP.2014.6884642}}, year = {{2014}}, } @article{40764, author = {{Stein, Manuel and Castañeda, Mario and Mezghani, Amine and Nossek, Josef A.}}, journal = {{IEEE Signal Process.\ Lett.}}, number = {{7}}, pages = {{866–870}}, title = {{{Information-preserving transformations for signal parameter estimation}}}, doi = {{10.1109/LSP.2014.2315537}}, volume = {{21}}, year = {{2014}}, } @article{40757, abstract = {{In this paper, we propose a novel mechanism for spectrum sensing that leads us to exploit the spatio-temporal correlation present in the received signal at a multi-antenna receiver. For the proposed mechanism, we formulate the spectrum sensing scheme by adopting the generalized likelihood ratio test (GLRT). However, the GLRT degenerates in the case of limited sample support. To circumvent this problem, several extensions are proposed that bring robustness to the GLRT in the case of high dimensionality and small sample size. In order to achieve these sample-efficient detection schemes, we modify the GLRT-based detector by exploiting the covariance structure and factoring the large spatio-temporal covariance matrix into spatial and temporal covariance matrices. The performance of the proposed detectors is evaluated by means of numerical simulations, showing important advantages over existing detectors.}}, author = {{Ali, S. and Ramírez, D. and Jansson, M. and Seco-Granados, G. and López-Salcedo, J. A.}}, journal = {{Eurasip\ J.\ Applied Signal Process.}}, title = {{{Multi-antenna spectrum sensing by exploiting spatio-temporal correlation}}}, doi = {{10.1186/1687-6180-2014-160}}, volume = {{160}}, year = {{2014}}, } @article{40754, author = {{Draper, Bruce and Kirby, Michael and Marks, Justin and Marrinan, Tim and Peterson, Chris}}, journal = {{Lin.\ Alg.\ Appl.}}, pages = {{15–32}}, publisher = {{Elsevier}}, title = {{{A flag representation for finite collections of subspaces of mixed dimensions}}}, doi = {{10.1016/j.laa.2014.03.022}}, volume = {{451}}, year = {{2014}}, } @inproceedings{40753, abstract = {{It is well known that input-to-state stability admits an astonishing number of equivalent characterizations. Here it is shown that for monotone systems on $\Rnp$ there are some additional characterizations that are useful for network stability analysis. These characterizations include system theoretic properties, algebraic properties, as well as the problem of finding simultaneous bounds on solutions to a collection of inequalities.}}, author = {{Rüffer, Björn S. and Sailer, Rudolf}}, booktitle = {{Proc. 21st Int. Symp. Mathematical Theory of Networks and Systems (MTNS)}}, title = {{{Input-to-State Stability for Discrete-Time Monotone Systems}}}, year = {{2014}}, } @article{40755, author = {{Huang, L. and Xiao, Y-.H. and So, H. C. and Fang, J.}}, journal = {{IEEE Transactions on Wireless Communications}}, number = {{2}}, pages = {{750–758}}, title = {{{Accurate Performance Analysis of Hadamard Ratio Test for Robust Spectrum Sensing}}}, volume = {{14}}, year = {{2014}}, } @inproceedings{40756, author = {{Marrinan, Tim and Draper, Bruce and Beveridge, J. Ross and Kirby, Michael and Peterson, Chris}}, booktitle = {{CVPR}}, pages = {{1082–1089}}, title = {{{Finding the Subspace Mean or Median to Fit Your Need}}}, doi = {{10.1109/CVPR.2014.142}}, year = {{2014}}, } @article{40758, abstract = {{Phase synchronization among neuronal oscillations within the same frequency band has been hypothesized to be a major mechanism for communication between different brain areas. On the other hand, cross-frequency com- munications are more flexible allowing interactions between oscillations with different frequencies. Among such cross-frequency interactions amplitude-to-amplitude interactions are of a special interest as they show how the strength of spatial synchronization in different neuronal populations relates to each other during a given task. While, previously, amplitude-to-amplitude correlations were studied primarily on the sensor level, we present a source separation approach using spatial filters which maximize the correlation between the envelopes of brain oscillations recorded with electro-/magnetoencephalography (EEG/MEG) or intracranial multichannel re- cordings. Our approach, which is called canonical source power correlation analysis (cSPoC), is thereby capable of extracting genuine brain oscillations solely based on their assumed coupling behavior even when the signal-to- noise ratio of the signals is low. In addition to using cSPoC for the analysis of cross-frequency interactions in the same subject, we show that it can also be utilized for studying amplitude dynamics of neuronal oscillations across subjects. We assess the performance of cSPoC in simulations as well as in three distinctively different analysis sce- narios of real EEG data, each involving several subjects. In the simulations, cSPoC outperforms unsupervised state-of-the-art approaches. In the analysis of real EEG recordings, we demonstrate excellent unsupervised dis- covery of meaningful power-to-power couplings, within as well as across subjects and frequency bands.}}, author = {{Dähne, S. and Nikulin, V. V. and Ramírez, D. and Schreier, P. J. and Müller, K.-R. and Haufe, S.}}, journal = {{NeuroImage}}, pages = {{334–348}}, title = {{{Finding brain oscillations with power dependencies in neuroimaging data}}}, doi = {{10.1016/j.neuroimage.2014.03.075}}, volume = {{96}}, year = {{2014}}, } @article{40762, abstract = {{Detecting and analyzing directional structures in images is important in many applications since one-dimensional patterns often correspond to important features such as object contours or trajectories. Classifying a structure as directional or non-directional requires a measure to quantify the degree of directionality and a threshold, which needs to be chosen based on the statistics of the image. In order to do this, we model the image as a random field. So far, little research has been performed on analyzing directionality in random fields. In this paper, we propose a measure to quantify the degree of directionality based on the random monogenic signal, which enables a unique decomposition of a 2D signal into local amplitude, local orientation, and local phase. We investigate the second-order statistical properties of the monogenic signal for isotropic, anisotropic, and unidirectional random fields. We analyze our measure of directionality for finite-size sample images, and determine a threshold to distinguish between unidirectional and non-unidirectional random fields, which allows the automatic classification of images.}}, author = {{Olhede, S. C. and Ramírez, D. and Schreier, P. J.}}, journal = {{IEEE Trans.\ Inform.\ Theory}}, number = {{10}}, pages = {{6491–6510}}, title = {{{Detecting Directionality in Random Fields Using the Monogenic Signal}}}, doi = {{10.1109/TIT.2014.2342734}}, volume = {{60}}, year = {{2014}}, } @article{40760, abstract = {{Alternating minimization and steepest descent are commonly used strategies to obtain interference alignment (IA) solutions in the $K$-user multiple-input multiple-output (MIMO) interference channel (IC). Although these algorithms are shown to converge monotonically, they experience a poor convergence rate, requiring an enormous amount of iterations which substantially increases with the size of the scenario. To alleviate this drawback, in this letter we resort to the Gauss-Newton (GN) method, which is well-known to experience quadratic convergence when the iterates are sufficiently close to the optimum. We discuss the convergence properties of the proposed GN algorithm and provide several numerical examples showing that it always converges to the optimum with quadratic rate, reducing dramatically the required computation time in comparison to other algorithms, hence paving a new way for the design of IA algorithms.}}, author = {{Lameiro, Christian and Santamaría, Ignacio}}, journal = {{IEEE Signal Process. Lett.}}, pages = {{1423–1427}}, title = {{{A Quadratically Convergent Method for Interference Alignment in MIMO Interference Channels}}}, doi = {{10.1109/LSP.2014.2338132}}, volume = {{21}}, year = {{2014}}, } @inproceedings{40761, abstract = {{This paper derives the interference-temperature (IT) limit for a multi-antenna primary user (PU) with a rate constraint. While in the case of a single-antenna PU there is a one-to-one mapping between IT and achievable rate, this correspondence does not hold anymore when a multiple-input multiple-output (MIMO) system is considered. In such cases, the spatial distribution of the interference must be taken into account, since it strongly affects the PU performance. To this end, we derive a closed-form expression for the maximum IT that can be tolerated by identifying the worst-case interference covariance matrix, which results in a multilevel waterfilling problem.}}, author = {{Lameiro, Christian and Utschick, Wolfgang and Santamaría, Ignacio}}, booktitle = {{Proc.\ Asilomar Conf.\ Signals Syst.\ Computers}}, title = {{{Interference-Temperature Limit for Cognitive Radio Networks with MIMO Primary Users}}}, doi = {{10.1109/ACSSC.2014.7094625}}, year = {{2014}}, }