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Monitoring network structure and content quality of signal processing articles on wikipedia

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Graph Based Sinogram Denoising for Tomographic Reconstructions

Pierre Vandergheynst, Nauman Shahid

Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sinogram denoising has become an essential pre-processing step for low dose Comp ...

2016

Combinations of adaptive filters: performance and convergence properties

Ali H. Sayed

Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation [1], array beamforming [2], channel equalization [3], to more recent sensor network applications in surveillance, target loc ...

IEEE2016

Signal Processing in the Workplace

Daniel Gatica-Perez

According to the U.S. Bureau of Labor Statistics, during 2013 employed Americans "worked an average of 7.6 hours on the days they worked," and "83% did some or all of their work at their workplace" [1]. Understanding processes in the workplace has been the ...

Ieee-Inst Electrical Electronics Engineers Inc2015

A Python package for audio signal processing

Sidney Barthe

A Python package for room acoustics. This package would allow us to simulate sound propagation in a closed room allowing us to predict a situation, compare it with experimental results or apply calculations for various signal processing applications. ...

2015

On spurious and real fluctuations of dynamic functional connectivity during rest

Dimitri Nestor Alice Van De Ville, Nora Leonardi

Functional brain networks reconfigure spontaneously during rest. Such network dynamics can be studied by dynamic functional connectivity (dynFC); i.e., sliding-window correlations between regional brain activity. Key parameters-such as window length and cu ...

Elsevier2015

Mitigating the Impact of Faults in Unreliable Memories For Error-Resilient Applications

Andreas Peter Burg, Adam Shmuel Teman, Georgios Karakonstantis, Shrikanth Ganapathy

Inherently error-resilient applications in areas such as signal processing, machine learning and data analytics provide opportunities for relaxing reliability requirements, and thereby reducing the overheads incurred by conventional error correction scheme ...

2015

Sequences with Minimal Time-Frequency Uncertainty

Martin Vetterli, Reza Parhizkar, Yann Barbotin

A central problem in signal processing and communications is to design signals that are compact both in time and frequency. Heisenberg's uncertainty principle states that a given function cannot be arbitrarily compact both in time and frequency, defining a ...

Elsevier2015

Laplacian Matrix Learning for Smooth Graph Signal Representation

Pascal Frossard, Pierre Vandergheynst, Xiaowen Dong

The construction of a meaningful graph plays a crucial role in the emerging field of signal processing on graphs. In this paper, we address the problem of learning graph Laplacians, which is similar to learning graph topologies, such that the input data fo ...

2015

Guided Mesh Normal Filtering

Sofien Bouaziz, Bailin Deng

The joint bilateral filter is a variant of the standard bilateral filter, where the range kernel is evaluated using a guidance signal instead of the original signal. It has been successfully applied to various image processing problems, where it provides m ...

Wiley-Blackwell2015

Near-Optimal Source Placement for Linear Physical Fields

Martin Vetterli, Juri Ranieri

In real-word applications, signal processing is often used to measure and control a physical field by means of sensors and sources, respectively. An aspect that has been often neglected is the optimization of the sources' locations. In this work, we discus ...

Ieee2014