IEEE Transactions on Signal and Information Processing over Networks

Issue 3 • Sept. 2015

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  • Table of Contents

    Publication Year: 2015, Page(s): C1
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  • IEEE Transactions on Signal and Information Processing over Networks publication information

    Publication Year: 2015, Page(s): C2
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  • Error Propagation in Gossip-Based Distributed Particle Filters

    Publication Year: 2015, Page(s):148 - 163
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (554 KB) | HTML iconHTML

    This paper examines the impact of the gossip procedure on distributed particle filters that employ averaging to estimate the global likelihood function. We consider a model where a gossip-driven algorithm leads to the use of a slightly distorted version of the likelihood function, in lieu of its true value. Under standard regularity conditions, and a mild assumption on the true likelihood function... View full abstract»

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  • Distributed Widely Linear Multiple-Model Adaptive Estimation

    Publication Year: 2015, Page(s):164 - 179
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1603 KB) | HTML iconHTML

    The paper considers the problem of estimating the state of a complex-valued stochastic hybrid system observed distributively using an agent/sensor network (AN/SN) with complex-valued (possibly noncircular) observations. In several distributed estimation problems, a suitable model to describe the underlying system is unknown a priori, i.e., distributed state estimation with structural uncertainty. ... View full abstract»

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  • Ensemble of distributed learners for online classification of dynamic data streams

    Publication Year: 2015, Page(s):180 - 194
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1031 KB) | HTML iconHTML

    We present a distributed online learning scheme to classify data captured from distributed and dynamic data sources. Our scheme consists of multiple distributed local learners, which analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each le... View full abstract»

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  • Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing

    Publication Year: 2015, Page(s):195 - 208
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2534 KB) | HTML iconHTML

    Mobile crowdsensing takes advantage of pervasive mobile devices to collect and process data for a variety of applications (e.g., traffic monitoring and spectrum sensing). In this study, a socially-aware crowdsensing system is advocated in which a cloud-based platform incentivizes mobile users to participate in sensing tasks by leveraging social trust among users, upon receiving sensing requests. F... View full abstract»

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  • No-Reference Transmission Distortion Modelling for H.264/AVC-Coded Video

    Publication Year: 2015, Page(s):209 - 221
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1730 KB) | HTML iconHTML

    In this paper, a low-complexity No-reference algorithm for real-time estimation of the channel induced distortion is proposed. The algorithm is capable of providing video quality evaluation for the network service provider perspective to the end-user. An analytical model has been proposed to estimate the mean square error (mse) distortion at the MB, frame, and sequence level. The algorithm takes i... View full abstract»

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  • IEEE Transactions on Signal and Information Processing over Networks Edics

    Publication Year: 2015, Page(s): 222
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  • IEEE Transactions on Signal and Information Processing over Networks information for authors

    Publication Year: 2015, Page(s):223 - 224
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  • IEEE Signal Processing Society Information

    Publication Year: 2015, Page(s): C3
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Aims & Scope

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.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Petar Djuric
Stony Brook University, Electrical & Computer Engineering
Light Engineering, Room 245
Stony Brook, NY
11794-2350
USA
+1 631-632-8423
Fax: +1 631-632-8494
petar.djuric@stonybrook.edu