- Page 1 and 2: Enhancements in Electrical Impedanc
- Page 3 and 4: Acknowledgements I would like to de
- Page 5: 3.5 3D Considerations . . . . . . .
- Page 9 and 10: 4.8 GCV curves for different priors
- Page 11 and 12: 7.15 Four layer tank used for 3D re
- Page 13 and 14: Discrete Variables • I is the mat
- Page 15 and 16: as opposed to anatomical imaging. W
- Page 17 and 18: to know how various alternative con
- Page 19 and 20: to noise however both algorithms pr
- Page 21 and 22: Chapter 2 Forward Problem 2.1 Descr
- Page 23 and 24: there are two primary types in EIT.
- Page 25 and 26: impedance, σ(�x,t) + jω(�x,t)
- Page 27 and 28: elying on a variational statement.
- Page 29 and 30: with Y11 = −Y12 − Y13, Y22 =
- Page 31 and 32: 2.3.1.3 Derivation of Linear Interp
- Page 33 and 34: with Ci being the following column
- Page 35 and 36: Substitution of 2.24 into 2.21 yiel
- Page 37 and 38: where the superscript identifies th
- Page 39 and 40: 2.3.3.4 Numerical Implementation of
- Page 41 and 42: is selected, measurements are taken
- Page 43 and 44: measurements of which only 104 are
- Page 45 and 46: Chapter 3 Reconstruction The proces
- Page 47 and 48: where the Jacobian is � H = T −
- Page 49 and 50: The condition number of a matrix is
- Page 51 and 52: 2. As λ goes to zero, the un-regul
- Page 53 and 54: 6. Evaluate a stopping rule. For ex
- Page 55 and 56: Finally Adler and Guardo define the
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3.5 3D Considerations In EIT it is
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Chapter 4 Objective Selection of Hy
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initial conductivity x = ∆σ/σ0.
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λ=0.0008 λ=0.0302 λ=0.0616 λ=6.
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4.3.3 Generalized Cross-Validation
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1. simulated data, generated using
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GCV 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0
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a hyperparameter selection method,
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1. Heuristic selections of hyperpar
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human lung data. Keywords: regulari
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fields when reconstructing in 2D [1
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i and integrating across element j
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5.2.5 Nodal Gaussian Filter The Gau
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Quantitative figures of merit are r
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(a) Rdiag BR=.236, SNR=.332 (b) Fil
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plane located halfway between the e
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models that are difficult or imposs
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6.1 Introduction EIT attempts to ca
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6.2.1 Image Reconstruction In order
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Aligned, fig 6.2(a) Planar Zigzag Z
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of reconstructions were calculated
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case normalized to the first and la
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Resolution (BR) Image Radial Positi
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configuration gave the worse overal
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Planar and Planar-offset configurat
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Chapter 7 Total Variation Regulariz
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on the conductivity vector, σ, whi
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a regularization penalty term a muc
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etween primal and dual optimal poin
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The optimization problem min x 1 2
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PD-IPM Algorithm ψ(σ) = 1 2 �F(
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(a) First step, TV solution. BRTV =
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Iteration 1 (a) Iteration 4 (d) Ite
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(a) L 2 solution with 2.5% AWGN, fi
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(a) i=1 (b) i=2 (c) i=3 (d) i=4 (e)
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Figure 7.15: Four layer tank used f
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can be solved with the algorithm. T
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Appendix: Variability in EIT Images
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where H is the Jacobian or sensitiv
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(a) AσˆL = 33% (b) Aσ ˆL = 51%
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Figure A.4: Reconstructions with ho
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Figure A.7: Reconstructions with no
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[15] Barber DC, Brown BH, Applied p
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[49] Frerichs I, Hahn G, Hellige G,
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[83] Kunst PWA, Vonk Noordegraaf A,
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[116] Vauhkonen M, Lionheart WRB, H