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Section: Community & Involvement

ScopeThough the IEEE Signal Processing (SPS) and Communications (COMSOC) Societies have evolved somewhat independently, the technical areas covered by the two societies are tightly and undeniably intertwined. In fact, in most problems, it is impossible to tell where the signal processing ends and the communications technology begins, and vice versa.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Nonlinear static multiple-input multiple-output (MIMO) systems are analyzed. The matrix formulation of Bussgang's theorem for complex Gaussian signals is rederived and put in the context of the multivariate cumulant series expansion. The attenuation matrix is a function of the input signals’ covariance and the covariance of the input and output signals.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We address a robust detection problem for MIMO radars in Gaussian noise with unknown covariance matrix, for the mismatched case where the nominal transmit (or receive) steering vector may not be aligned with the true transmit (or receive) steering vector. Subspace models are adopted for taking into account these mismatches.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influences, SEMs assert that directed dependencies arise due to instantaneous effects...

Section: Community & Involvement

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The Collaborative Research Centre SFB 1330 Hearing acoustics: Perceptual principles, Algorithms and Applications (HAPPAA) at the Carl von Ossietzky Universität Oldenburg is seeking to fill the position of a Research Associate (m/f/d)

The Collaborative Research Centre SFB 1330 Hearing acoustics: Perceptual principles, Algorithms and Applications (HAPPAA) at the Carl von Ossietzky Universität Oldenburg is seeking to fill the position of a Research Scientist / PhD Student in Acoustical Signal Processing (m/f/d)

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements.

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Two-directional two-dimensional canonical correlation analysis ((2D) 2 CCA) directly seeks linear relationship between different image data sets without reshaping images into vectors. However, it fails in finding the nonlinear correlation.