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Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Graph signal processing (GSP) has become an important tool in many areas such as image processing, networking learning and analysis of social network data. In this paper, we propose a broader framework that not only encompasses traditional GSP as a special case, but also includes a hybrid framework of graph and classical signal processing over a continuous domain.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We consider the problem of detecting abrupt changes in the underlying stochastic structure of multivariate signals. A novel non-parametric and model-free off-line change-point detection method based on a kernel mapping is presented. This approach is sequential and alternates between two steps: a greedy detection to estimate a new breakpoint and a projection to remove its contribution to the signal. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR) for both single point source and multiple point sources, operating in an interference active environment. Unlike existing sparse design methods which either deal with structured environment-independent or non-structured environment-dependent arrays, our method is a hybrid approach and seeks a full augumentable array that optimizes beamformer performance. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Signal sampling and reconstruction is a fundamental engineering task at the heart of signal processing. The celebrated Shannon-Nyquist theorem guarantees perfect signal reconstruction from uniform samples, obtained at a rate twice the maximum frequency present in the signal. Unfortunately a large number of signals of interest are far from being band-limited. 

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We study conditions that allow accurate graphical model selection from non-stationary data. The observed data is modelled as a vector-valued zero-mean Gaussian random process whose samples are uncorrelated but have different covariance matrices. This model contains as special cases the standard setting of i.i.d. samples as well as the case of samples forming a stationary time series.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning mixture of separable dictionaries are explored and sufficient conditions for local identifiability of the underlying dictionary are derived in each case.

Section: Publications & Resources

Dr. Maria Sabrina Greco graduated in Electronic Engineering in 1993 and received a Ph.D. degree in Telecommunication Engineering in 1998, from the University of Pisa, Italy. From December 1997 to May 1998, she joined the Georgia Tech Research Institute, Atlanta, in the USA as a visiting research scholar where she carried on research activity in the field of radar detection in non-Gaussian background.

Section: Publications & Resources

Are you looking to energize signal processing students, early stage researchers, and industry practitioners in your area? Consider hosting a Seasonal School for young engineers near you!

Section: Publications & Resources

Every year the US Army spends close to 60 percent of funds allocated for a given air platform on maintenance and sustainment. Helicopters like the HH-60 Pave Hawk and the UH-60 Black Hawk undergo maintenance every at pre-determined cycles and their mechanical and electrical components have a finite life expectancy. 

Section: Publications & Resources

Recent years have witnessed the rapid development of virtual reality (VR). Above 90% of VR content is in the form of 360° video, also called omnidirectional video or panoramic video. Generally speaking, 360° video offers immersive and interactive viewing experience, as the viewers are able to freely move their heads in the range of 360° × 180° to access different viewports. Therefore, the perception of 360° video is significantly different from that of traditional video.