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Compressive Sampling and Deep Neural Network (CS‐DNN)
Asoke Nandi
,
Hosameldin Ahmed
Aston Digital Futures Institute
Xi'an Jiaotong University
Brunel University
Research output
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Chapter in Book/Published conference output
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Dive into the research topics of 'Compressive Sampling and Deep Neural Network (CS‐DNN)'. Together they form a unique fingerprint.
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Keyphrases
Deep Neural Network
100%
Compressive Sampling
100%
Sparse Autoencoder
80%
Over-complete
40%
Compressive Measurement
40%
Vibration Measurement
20%
Sparse Representation
20%
Learned Features
20%
Classification Methods
20%
Transformation Approach
20%
Feature Representation
20%
Multiple Stages
20%
Representation Model
20%
Vibration Data
20%
Fault Diagnosis
20%
Feature Transformation
20%
Intelligent Fault Diagnosis
20%
Machine Condition Monitoring
20%
Nonlinear Features
20%
Unsupervised Feature Learning
20%
Sparse Time-frequency Representation
20%
Network Use
20%
Computer Science
Deep Neural Network
100%
Compressive Sampling
100%
Sparse Autoencoder
66%
Representation Learning
33%
Case Study
16%
Sparse Representation
16%
Collected Data
16%
time-frequency representation
16%
Classification Method
16%
Representation Model
16%
Condition Monitoring
16%
Engineering
Deep Neural Network
100%
Compressive Sampling
100%
Autoencoder
66%
Collected Data
16%
Frequency Representation
16%
Multiple Stage
16%
Condition Monitoring
16%
Vibration Measurement
16%
Classification Method
16%
Nonlinear Feature
16%
Chemical Engineering
Deep Neural Network
100%
Vibration Measurement
16%
Condition Monitoring
16%