Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Journal on Selected Areas in Communications"
DOI: 10.1109/jsac.2022.3221950
Abstract: Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals… read more here.
Keywords: semantic information; graph signals; innovation; extraction ... See more keywords
Rate-Constrained Trellis-Coded Quantization for Large-Scale Noisy Graph Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3149814
Abstract: We consider the issue of compressing large-scale noise-corrupted graph signals under a rate constraint, to tackle the communication resource limitations, from rate-distortion perspective. To guarantee the fidelity of the overall compression system for noisy graph… read more here.
Keywords: graph signals; quantization; large scale; trellis coded ... See more keywords
An Adaptive Rate Allocation Scheme for Time-Varying Graph Signal Quantization
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Communications Letters"
DOI: 10.1109/lcomm.2022.3163468
Abstract: To address the communication resource limitations the uplink data in some distributed networks suffers from, quantization enables these graph signals to realize compression. However, the compression process is accompanied by quantization errors, which pose threat… read more here.
Keywords: time; graph signals; rate allocation; quantization ... See more keywords
A-Optimal Sampling and Robust Reconstruction for Graph Signals via Truncated Neumann Series
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2018.2818062
Abstract: Graph signal processing (GSP) studies signals that live on irregular data kernels described by graphs. One fundamental problem in GSP is sampling—from which subset of graph nodes to collect samples in order to reconstruct a… read more here.
Keywords: reconstruction; truncated neumann; neumann series; graph signals ... See more keywords
An Uncertainty Principle for Lowband Graph Signals
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2022.3152131
Abstract: In this article, we introduce a novel lower bound on the support size of lowband graph signals. This result allows the deduction of an optimality criterion for the lowband and sparse decomposition of any graph… read more here.
Keywords: graph signals; uncertainty principle; principle lowband; graph ... See more keywords
Rakeness-Based Compressed Sensing of Multiple-Graph Signals for IoT Applications
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Circuits and Systems II: Express Briefs"
DOI: 10.1109/tcsii.2018.2821241
Abstract: Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field.… read more here.
Keywords: sensing multiple; compressed sensing; rakeness based; based compressed ... See more keywords
Efficient Approximation and Denoising of Graph Signals Using the Multiscale Basis Dictionaries
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2016.2632039
Abstract: We propose methods to efficiently approximate and denoise signals sampled on the nodes of graphs using our overcomplete multiscale transforms/basis dictionaries for such graph signals: the hierarchical graph Laplacian eigen transform (HGLET) and the generalized… read more here.
Keywords: approximation denoising; basis; graph signals; basis dictionaries ... See more keywords
Sampling of Graph Signals via Randomized Local Aggregations
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2018.2869354
Abstract: Sampling of signals defined over the nodes of a graph is one of the crucial problems in graph signal processing, whereas in classical signal processing, sampling is a well-defined operation; when we consider a graph… read more here.
Keywords: via randomized; sampling graph; graph signals; randomized local ... See more keywords
Joint Graph Learning and Blind Separation of Smooth Graph Signals Using Minimization of Mutual Information and Laplacian Quadratic Forms
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Signal and Information Processing over Networks"
DOI: 10.1109/tsipn.2023.3240893
Abstract: The smoothness of graph signals has found desirable real applications for processing irregular (graph-based) signals. When the latent sources of the mixtures provided to us as observations are smooth graph signals, it is more efficient… read more here.
Keywords: information; graph signals; smooth graph; graph signal ... See more keywords
M-Channel Perfect Recovery of Coarsened Graphs and Graph Signals With Spectral Invariance and Topological Preservation
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2017.2726984
Abstract: In this paper, an M-channel perfect reconstruction filter bank based on a coarsening algorithm is proposed. Compared with most of the designs of graph filter banks that do not consider the graph reconstruction, our proposed… read more here.
Keywords: channel perfect; input graph; graph signals; proposed design ... See more keywords
Spectral Domain Sampling of Graph Signals
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2018.2839620
Abstract: Sampling methods for graph signals in the graph spectral domain are presented. Though the conventional sampling of graph signals can be regarded as sampling in the graph vertex domain, it does not have the desired… read more here.
Keywords: spectral domain; domain sampling; graph signals; graph ... See more keywords