Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the ...

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

The graph below shows the total number of publications each year in Respiratory Rate Estimation Using Photoplethysmography and Electrocardiogram Signals.

Graph signal processing (GSP) uses a shift operator to define a Fourier basis for the set of graph signals. The shift operator is often chosen to capture the graph topology. However, in many ...

graph-algorithms signal-processing image-compression wavelets-on-graphs wedgelets geometric-wavelets Updated 5 days ago MATLAB ...

EEGLAB is an open source signal processing environment for electrophysiological signals running on Matlab and developed at the SCCN/UCSD ...

Videos in this product Graph Learning from Multi-Attribute Smooth Signals 0:12:59 0 views ...

Bacteria engineered to detect pollution or nutrients could help farmers monitor their fields.

By learning the relevant features of clinical images along with the relationships between them, the neural network can ...

The brain doesn't merely register time—it structures it, according to new research from the Kavli Institute for Systems Neuroscience published in Science.

Milton Feng has been working with Foxconn Interconnect Technologies (FIT) on a series of Center for Networked Intelligent ...

A new method turns noise into valuable data to enhance understanding of chemical reactions and material properties with unprecedented detail at the atomic level. The results of this research are now ...