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Figures of merit chosen for position optimization NN training_
| Feature | Explanation |
|---|---|
| J homogeneity | |
| J condition number | |
| ∆Vr measurement homogeneity |
Overview of the FEM model component properties_
| Part | Conductivity | Characteristics |
|---|---|---|
| BG | 6.62 × 10−1 S·m−1 | 9 × 9 × 5 cm |
| Clot | 6.62 × 10−2 S·m−1 | Spherical targets |
| SG | 1 ×10−6 S·m−1 | Rod diameter 0.4 cm |
Overview of measurement-selection methods and counts_
| Maximization | Number of measurements |
|---|---|
| Parallelotope volume | 144 |
| L1-norm | 32 |
| L2-norm | 32 |
Overview of layers for an electrode position optimization NN_
| # | Layer type | Layer information |
| 1 | input layer | 3-element vector |
| 2 | FC layer | 254 neurons |
| 3 | ReLU layer | activation layer |
| 4 | FC layer | 203 neurons |
| 5 | ReLU layer | activation layer |
| 6 | FC layer | 48 neurons |
| 7 | regression layer | determine positions |
Overview of layers for a thrombus detection NN_
| # | Layer type | Layer information |
|---|---|---|
| 1 | input layer | 208-element vector |
| 2 | FC layer | 200 neurons |
| 3 | ReLU layer | activation layer |
| 4 | FC layer | 100 neurons |
| 5 | ReLU layer | activation layer |
| 6 | FC layer | 2 neurons |
| 7 | softmax layer | to probabilities |
| 8 | classification layer | more probable class |