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Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Incorporating graphs in the analysis of multivariate signals is becoming a standard way to understand the interdependency of activity recorded at different sites. The new research frontier in this direction includes the important problem of how to assess dynamic changes of signal activity. We address this problem in a novel way by defining the graph-variate signal alongside methods for its analysis.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Various signal processing applications can be expressed as large-scale optimization problems with a composite objective structure, where the Lipschitz constant of the smooth part gradient is either not known, or its local values may only be a fraction of the global value. The smooth part may be strongly convex as well. The algorithms capable of addressing this problem class in its entirety are black-box accelerated first-order methods, related to either Nesterov's Fast Gradient Method or the Accelerated Multistep Gradient Scheme, which were developed and analyzed using the estimate sequence mathematical framework.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Recently, many sparse arrays have been proposed to increase the number of degrees of freedom for direction of arrival (DoA) estimation especially for circular signals. Though many practical signals are noncircular, still the properties of noncircularity are hardly exploited in the design of sparse linear arrays. A new array geometry for noncircular signals, which significantly increases the aperture of the virtual array, has been proposed in this paper. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Linear regression models contaminated by Gaussian noise (inlier) and possibly unbounded sparse outliers are common in many signal processing applications. Sparse recovery inspired robust regression (SRIRR) techniques are shown to deliver high-quality estimation performance in such regression models. Unfortunately, most SRIRR techniques assume a priori knowledge of noise statistics like inlier noise variance or outlier statistics like number of outliers.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Distributed estimation fusion is concerned with the combination of local estimates from multiple distributed sensors to produce a fused result. In this paper, we characterize local estimates as posterior probability densities, and assume that they all belong to a parametric family. Our starting point is to consider this family as a Riemannian manifold by introducing the Fisher information metric.

Section: Publications & Resources

The IEEE SPS congratulates the following SPS members who will receive the Society’s prestigious awards during ICASSP 2019 in Brighton, UK.

Section: Publications & Resources

The IEEE Signal Processing Society (SPS) Publications group is pleased to announce the launch of the IEEE SPS Author Survey. This automated survey is being delivered to the corresponding authors when their articles are final and published on IEEE Xplore®. 

Section: Publications & Resources

This year the Signal Processing Society identified 16 Chapters who exhibited excellence in many facets of chapter management, member recruitment, and activities.

Section: Publications & Resources

This dissertation focuses on statistical signal processing theory and its applications to radar, complex-valued signal processing and model selection.

Section: Publications & Resources

In recent years, the complexity of designing embedded signal processing systems for wireless communications has increased significantly based on the need to support increasing levels of operational flexibility and adaptivity, while also supporting increasing data rates and bandwidths.