Big Data Dimensionality Reduction at IoT Edge Through Optical Graph Signal Processing

2020 ◽  
Author(s):  
Alireza Khodaei ◽  
Jitender Deogun ◽  
Dennis Alexander
Keyword(s):  
Signal Processing ◽  
Big Data ◽  
Dimensionality Reduction ◽  
Graph Signal Processing ◽  
Data Dimensionality Reduction

Big Data Dimensionality Reduction for Wireless Sensor Networks Using Stacked Autoencoders

2019 ◽  
pp. 391-400
Author(s):  
Muneeba Sirshar ◽  
Sajid Saleem ◽  
Muhammad U. Ilyas ◽  
Muhammad Murtaza Khan ◽  
Mohammed Saeed Alkatheiri ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Sensor Networks ◽  
Dimensionality Reduction ◽  
Wireless Sensor ◽  
Stacked Autoencoders ◽  
Data Dimensionality Reduction

scholarly journals Graphon Filters: Graph Signal Processing in the Limit

2021 ◽  
Vol 69 ◽  
pp. 1740-1754
Author(s):  
Matthew W. Morency ◽  
Geert Leus
Keyword(s):  
Signal Processing ◽  
Graph Signal Processing

A novel intrinsic time–scale decomposition–graph signal processing–based characteristic extraction method for rotor–stator rubbing of aeroengine

2021 ◽  
pp. 107754632098596
Author(s):  
Mingyue Yu
Keyword(s):  
Signal Processing ◽  
Autocorrelation Function ◽  
Time Scale ◽  
Energy Index ◽  
Signal Processing Algorithm ◽  
Intrinsic Time ◽  
Graph Signal Processing ◽  
Laplacian Energy ◽  
Rotational Components ◽  
Time Scale Decomposition

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

scholarly journals Community-Aware Graph Signal Processing: Modularity Defines New Ways of Processing Graph Signals

2020 ◽  
Vol 37 (6) ◽  
pp. 150-159
Author(s):  
Miljan Petrovic ◽  
Raphael Liegeois ◽  
Thomas A.W. Bolton ◽  
Dimitri Van De Ville
Keyword(s):  
Signal Processing ◽  
Graph Signal Processing
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