@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}}, } @inproceedings{40759, abstract = {{We derive an estimator of the cycle period of a univariate cyclostationary process based on an information- theoretic criterion. Transforming the univariate cyclostationary process into a vector-valued wide-sense stationary process allows us to obtain the structure of the covariance matrix, which is block-Toeplitz, and its block size depends on the unknown cycle period. Therefore, we sweep the block size and obtain the ML estimate of the covariance matrix, required for the information- theoretic criterion. Since there are no closed-form ML estimates of block-Toeplitz matrices, we asymptotically approximate them as block-circulant. Finally, some numerical examples show the good performance of the proposed estimator.}}, author = {{Ramírez, D. and Schreier, P. J. and Vía, J. and Santamaría, I. and Scharf, L. L.}}, booktitle = {{Proc.\ Asilomar Conf.\ Signals Syst.\ Computers}}, title = {{{A Regularized Maximum Likelihood Estimator for the Period of a Cyclostationary Process}}}, year = {{2014}}, } @article{40763, abstract = {{Complex-valued signals occur in many areas of science and engineering and are thus of fundamental interest. When developing signal processing methods in the complex domain, there are two key issues: making use of the full statistical information and optimization. In this article, we review the necessary tools to address these two key issues and provide examples in filtering and blind source separation (BSS) that utilize these tools.}}, author = {{Adali, Tulay and Schreier, Peter J.}}, journal = {{IEEE Signal Processing Magazine}}, number = {{5}}, pages = {{112–128}}, title = {{{Optimization and estimation of complex-valued signals}}}, doi = {{10.1109/MSP.2013.2287951}}, volume = {{31}}, year = {{2014}}, } @inproceedings{40769, author = {{Lameiro, Christian and Santamaría, Ignacio and Utschick, Wolfgang}}, booktitle = {{Proc.\ IEEE Int.\ Conf.\ Acoustics, Speech and Signal Process.}}, title = {{{Interference Shaping Constraints for Underlay MIMO Interference Channels}}}, doi = {{10.1109/ICASSP.2014.6855020}}, year = {{2014}}, } @article{40765, author = {{Stein, Manuel and Mezghani, Amine and Nossek, Josef A.}}, journal = {{IEEE Signal Process.\ Lett.}}, number = {{7}}, pages = {{796–799}}, title = {{{A lower bound for the Fisher information measure}}}, doi = {{10.1109/LSP.2014.2316008}}, volume = {{21}}, year = {{2014}}, } @article{8171, abstract = {{The polynomial hierarchy plays a central role in classical complexity theory. Here, we define a quantum generalization of the polynomial hierarchy, and initiate its study. We show that not only are there natural complete problems for the second level of this quantum hierarchy, but that these problems are in fact hard to approximate. Using the same techniques, we also obtain hardness of approximation for the class QCMA. Our approach is based on the use of dispersers, and is inspired by the classical results of Umans regarding hardness of approximation for the second level of the classical polynomial hierarchy [Umans, FOCS 1999]. The problems for which we prove hardness of approximation for include, among others, a quantum version of the Succinct Set Cover problem, and a variant of the local Hamiltonian problem with hybrid classical-quantum ground states.}}, author = {{Gharibian, Sevag and Kempe, Julia}}, journal = {{Quantum Information & Computation}}, keywords = {{Hardness of approximation, polynomial time hierarchy, succinct set cover, quantum complexity}}, number = {{5-6}}, pages = {{517--540}}, title = {{{Hardness of approximation for quantum problems}}}, volume = {{14}}, year = {{2014}}, } @article{8172, abstract = {{We show how to efficiently simulate continuous-time quantum query algorithms that run in time T in a manner that preserves the query complexity (within a polylogarithmic factor) while also incurring a small overhead cost in the total number of gates between queries. By small overhead, we mean T within a factor that is polylogarithmic in terms of T and a cost measure that reflects the cost of computing the driving Hamiltonian. This permits any continuous-time quantum algorithm based on an efficiently computable driving Hamiltonian to be converted into a gate-efficient algorithm with similar running time.}}, author = {{W. Berry, Dominic and Cleve, Richard and Gharibian, Sevag}}, journal = {{Quantum Information & Computation}}, number = {{1-2}}, pages = {{1--30}}, title = {{{Gate-efficient discrete simulations of continuous-time quantum query algorithms}}}, volume = {{14}}, year = {{2014}}, } @article{34845, abstract = {{Computational Galois theory, in particular the problem of computing the Galois group of a given polynomial, is a very old problem. Currently, the best algorithmic solution is Stauduhar’s method. Computationally, one of the key challenges in the application of Stauduhar’s method is to find, for a given pair of groups H