Publications by David Shuman | EPFL Graph Search

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Unions of graph multiplier operators are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators. The proposed method features approximations of ...

One of the key challenges in the area of signal processing on graphs is to design dictionaries and transform methods to identify and exploit structure in signals on weighted graphs. To do so, we need to account for the intrinsic geometric structure of the ...

This paper introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective of the technology, supported by the NASA Earth Science Technology Off ...

A major line of work in graph signal processing [2] during the past 10 years has been to design new transform methods that account for the underlying graph structure to identify and exploit structure in data residing on a connected, weighted, undirected gr ...

We consider the transductive learning problem when the labels belong to a continuous space. Through the use of spectral graph wavelets, we explore the benefits of multiresolution analysis on a graph constructed from the labeled and unlabeled data. The spec ...

We consider the problem of designing spectral graph filters for the construction of dictionaries of atoms that can be used to efficiently represent signals residing on weighted graphs. While the filters used in previous spectral graph wavelet constructions ...

Multiscale transforms designed to process analog and discrete-time signals and images cannot be directly applied to analyze high-dimensional data residing on the vertices of a weighted graph, as they do not capture the intrinsic topology of the graph data ...

A number of new localized, multiscale transforms have recently been introduced to analyze data residing on weighted graphs. In signal processing tasks such as regularization and compression, much of the power of classical wavelets on the real line is deriv ...

In this paper, we consider the problem of monitoring soil moisture evolution using a wireless network of in situ sensors. Continuously sampling moisture levels with these sensors incurs high-maintenance and energy consumption costs, which are particularly ...

This paper considers a single source transmitting data to one or more receivers/users over a shared wireless channel. Due to random fading, the wireless channel conditions vary with time and from user to user. Each user has a buffer to store received packe ...

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties – the ability to adapt to specific signal data and a fast implementation of the dictionary. To sparsely represent signals residing on wei ...

Unions of graph Fourier multipliers are an important class of linear operators for processing signals defined on graphs. We present a novel method to efficiently distribute the application of these operators to the high-dimensional signals collected by sen ...

David Shuman

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Distributed Signal Processing via Chebyshev Polynomial Approximation

Vertex-Frequency Analysis on Graphs

A Wireless Soil Moisture Smart Sensor Web Using Physics-Based Optimal Control: Concept and Initial Demonstrations

Localized Spectral Graph Filter Frames: A Unifying Framework, Survey of Design Considerations, and Numerical Comparison

Semi-Supervised Learning with Spectral Graph Wavelets

Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames

A Multiscale Pyramid Transform for Graph Signals

On the Sparsity of Wavelet Coefficients for Signals on Graphs

Measurement Scheduling for Soil Moisture Sensing: From Physical Models to Optimal Control

Energy-Efficient Transmission Scheduling With Strict Underflow Constraints

Learning Parametric Dictionaries for Signals on Graphs

Chebyshev Polynomial Approximation for Distributed Signal Processing

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains