Results 31 to 40 of about 5,220,012 (288)
Multi-level Pipeline Scheduling Algorithm in Heterogeneous Signal Processing Platform [PDF]
Jisuanji gongcheng, 2018 The existing real-time task scheduling algorithm do not consider the difference of node computing power in system heterogeneity,which leads to the imbalance of task partition.Therefore,according to the characteristics of real-time task in the ...
YANG Pingping,YUE Chunsheng,HU Zeming
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Graph Regularization Methods in Soft Detector Fusion
IEEE Access, 2023 This paper presents a theoretical derivation of two new graph-based regularization methods for fusing the individual results of multiple detectors (two-class classifiers).
Addisson Salazar +3 more
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Graph Signal Processing -- Part II: Processing and Analyzing Signals on Graphs
CoRR, 2019 The focus of Part I of this monograph has been on both the fundamental properties, graph topologies, and spectral representations of graphs. Part II embarks on these concepts to address the algorithmic and practical issues centered round data/signal processing on graphs, that is, the focus is on the analysis and estimation of both deterministic and ...
Ljubisa Stankovic +5 more
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On Local Distributions in Graph Signal Processing
IEEE Transactions on Signal Processing, 2022 Graph filtering is the cornerstone operation in graph signal processing (GSP). Thus, understanding it is key in developing potent GSP methods. Graph filters are local and distributed linear operations, whose output depends only on the local neighborhood of each node.
T. Mitchell Roddenberry +3 more
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Dynamic Graph Learning: A Structure-Driven Approach
Mathematics, 2021 The purpose of this paper is to infer a dynamic graph as a global (collective) model of time-varying measurements at a set of network nodes. This model captures both pairwise as well as higher order interactions (i.e., more than two nodes) among the ...
Bo Jiang +5 more
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Learning Laplacian Matrix in Smooth Graph Signal Representations [PDF]
, 2019 The construction of a meaningful graph plays a crucial role in the success of many graph-based representations and algorithms for handling structured data, especially in the emerging field of graph signal processing.
Dong, Xiaowen +3 more
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Undirected Graphs: Is the Shift-Enabled Condition Trivial or Necessary?
IEEE Access, 2021 With the growing application of undirected graphs for signal/image processing on graphs and distributed machine learning, we demonstrate that the shift-enabled condition is as necessary for undirected graphs as it is for directed graphs.
Liyan Chen +4 more
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Partition of Unity Methods for Signal Processing on Graphs [PDF]
Journal of Fourier Analysis and Applications, 2021 Partition of unity methods (PUMs) on graphs are simple and highly adaptive auxiliary tools for graph signal processing. Based on a greedy-type metric clustering and augmentation scheme, we show how a partition of unity can be generated in an efficient way on graphs.
Roberto Cavoretto +2 more
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Node-Adaptive Regularization for Graph Signal Reconstruction
IEEE Open Journal of Signal Processing, 2021 A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang +3 more
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Graphon Filters: Graph Signal Processing in the Limit
, 2021 Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure.
Morency, M.W. (author) +3 more
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