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In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics.
In this paper, we take a step towards addressing this issue, by proposing a novel approach to biclustering based on factor graphs, which yields high quality solutions and scales more favorably than previous methods.
The proposed approach has been tested and compared with state-of-the-art methods on four datasets (two synthetic and two real world data), providing encouraging results with respect both to previous approaches based on factor graphs and to other state-of-the-art methods.
Therefore, we propose a TDoA positioning algorithm based on factor graphs.
Of course, the proposed framework is not unique, so we also refer the reader to an alternative version based on factor graphs [23].
Specifically, in[11], a distributed localization method is presented which is based on factor graphs and relies on cooperation and message-passing between nodes.
Similar(49)
As shown in [15 19], there exists a solid framework based on factor graph theory that dictates how the estimation and the decoding can be decoupled in a coded setup.
Generally, the BP decoder for polar codes is based on the factor graph representation obtained by the encoding graph of polar codes [4].
Our approach is based on a factor graph representation of the constraint network.
Based on the factor graph constructed, messages are derived on the two subgraphs, i.e., PHN and CFO estimation subgraph and the FTN symbol detection subgraph.
Thus, the proposed BP decoders in [4, 5] are based on the factor graph representation of the generator matrix.
More suggestions(13)
based on property graphs
based on difference graphs
based on factor loadings
based on factor prices
based on contig graphs
based on constraint graphs
based on factor analyses
based on proximity graphs
based on shape graphs
based on threshold graphs
based on string graphs
based on expander graphs
based on photo graphs
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