The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.
IEEE Transactions on Signal and Information Processing over Networks
Description
Identifiers
e-ISSN | 2373-776X |
Publisher
IEEE
Additional information
Data set: ieee
Articles
IEEE Transactions on Signal and Information Processing over Networks > 2017 > 3 > 4 > 650 - 659
We introduce a measure of causality that captures the functional dependencies in dynamical systems and subsequently, define a new type of graphical model, functional dependency graph, to encode such dependencies. We study the relationship between this type of graphical model and other graphical models such as directed information graphs and linear dynamical graphs that have been proposed to capture...
IEEE Transactions on Signal and Information Processing over Networks > 2017 > 3 > 4 > 803 - 806
Presents the list of reviewers who contributed to this publication in 2017.
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.