(Downloads - 0)
For more info about our services contact : help@bestpfe.com
Table of contents
1 Introduction
1.1 Research background
1.2 Outline
2 Modelling of the forward problem
2.1 Multiple scattering method
2.2 Method of moments
2.3 Validation of the modelling
3 Sparsity constrained inversion and contrast source inversion
3.1 Sparsity constrained method
3.1.1 Results of sparsity-constrained method
3.2 Contrast source inversion
3.2.1 Results of binary-specialized contrast source inversion
3.3 CSI for reconstruction of random contrast distribution
4 Imaging by convolutional neural networks in frequency domain
4.1 CNN architecture
4.2 Loss function
4.3 Training method
4.4 Training dataset
4.5 Implementation
4.6 The binary-specialized CNN: a reference example
4.6.1 Different configurations for the test
4.6.2 Single frequency vs. multiple frequencies of operation
4.6.3 Different data noise ratios
4.6.4 Different values of contrast
4.6.5 Additional results for different numbers of missing rods and different shapes using binary-specialized CNN
4.6.6 Extension to random contrast distribution
5 Imaging by recurrent neural networks in time domain
5.1 Motivation of using RNN
5.2 LSTM structure
5.2.1 Dataset
5.2.2 Training process
5.2.3 Results
5.3 Comparison with imaging by convolutional neural networks
5.3.1 CNN architecture
5.3.2 CNN results
5.3.3 Comparison between CNN and RNN
5.4 Validation on laboratory-controlled Data
5.4.1 The configuration of experiments
5.4.2 Results on experimental data
6 Imaging by convolutional-recurrent neural networks
6.1 Architecture of proposed CRNN
6.2 Training process
6.3 Results of CRNN
7 Direct imaging method: time reversal
7.1 Time reversal for localization of source
7.2 Time Reversal for localization of missing rods
8 Conclusion
8.1 Summary of the work as completed
8.2 Potential work
