Skip to main navigation
Skip to search
Skip to main content
Aston Research Explorer Home
Help & FAQ
Home
Research units
Profiles
Research Outputs
Datasets
Student theses
Activities
Press/Media
Prizes
Equipment
Search by expertise, name or affiliation
Compressive Sampling and Deep Neural Network (CS‐DNN)
Asoke Nandi,
Hosameldin Ahmed
Aston Digital Futures Institute
Research output
:
Chapter in Book/Published conference output
›
Chapter
Overview
Fingerprint
Research Outputs
(1)
Fingerprint
Dive into the research topics of 'Compressive Sampling and Deep Neural Network (CS‐DNN)'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Classification Methods
20%
Compressive Measurement
40%
Compressive Sampling
100%
Deep Neural Network
100%
Fault Diagnosis
20%
Feature Representation
20%
Feature Transformation
20%
Intelligent Fault Diagnosis
20%
Learned Features
20%
Machine Condition Monitoring
20%
Multiple Stages
20%
Network Use
20%
Nonlinear Features
20%
Over-complete
40%
Representation Model
20%
Sparse Autoencoder
80%
Sparse Representation
20%
Sparse Time-frequency Representation
20%
Transformation Approach
20%
Unsupervised Feature Learning
20%
Vibration Data
20%
Vibration Measurement
20%
Computer Science
Case Study
16%
Classification Method
16%
Collected Data
16%
Compressive Sampling
100%
Condition Monitoring
16%
Deep Neural Network
100%
Representation Learning
33%
Representation Model
16%
Sparse Autoencoder
66%
Sparse Representation
16%
time-frequency representation
16%
Engineering
Autoencoder
66%
Classification Method
16%
Collected Data
16%
Compressive Sampling
100%
Condition Monitoring
16%
Deep Neural Network
100%
Frequency Representation
16%
Multiple Stage
16%
Nonlinear Feature
16%
Vibration Measurement
16%
Chemical Engineering
Condition Monitoring
16%
Deep Neural Network
100%
Vibration Measurement
16%