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more flexible electrode configurations? #5
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Activity
pyEITdoes not constrain the stimulation pattern, for example, you can use the variableex_matfor more flexible configurations of the stimulation electrodes.ex_matis of sizeNx2, for any rowex_mat_i, theex_mat_i(0)is the current injection whileex_mat_i(1)is the sink.For a typical 16-electrodes configuration, the electrodes are at nodes:
[1, 7, 86, 176, 288, 667, ..., 981]and
ex_matmay be specified within the range[1..16]1, 3 1, 7 2, 9, 1, 16, 2, 15 ... 7, 1 8, 2You may find an example in
demo_stack_jac.pyanddemo_sensitivity2d.pyBut, as you see in the function
voltage_meter, the measuring pattern is limited toskip k, whilekis specified by the variablestep. However, the measuring sequence only affects the dynamic range in real hardware, in simulation, the adjacent measurement method suffices.Great - demo_dynamic_stack can vary A and B but is there a way to vary M and N as well?
Also, is there a way to do bipolar measurements where M and N are non-existent? Intent is to use in hardware and experiment with different configurations here.The current code only varies A and B. M and N are fixed by a skip k pattern (step=k). However, given that,
\int u_i = 0, i \in E_l, by measuring all adjacent voltage differences on the boundary electrodes, you may infer the voltage difference of any pair (exclude the A and B which have contact impedance). The way the voltage meter constructs only affect the dynamic ranges and noise performance in real hardware, in simulation, the DR and SNR (of ADC) are assumed infinity.However, pyEIT cannot handle the voltage meter while M and N are not electrodes. In this case, you may create pseudo electrodes. For example, 71 electrodes marked by
el_pos, but keep in mind which of these electrodes are those actual 16 electrodes you want to use (or even, you can use any of these 71 electrodes as A and B), and writeex_mat(A and B) accordingly. M and N are also measured in an adjacent manner, then you may infer any voltage difference of these 71 measures.Thanks for clarifying SNR assumed infinity in simulation. Currently integrating pyEIT into OpenEIT dashboard.
Reacted by liubenyuanNow electrode configurations has been merged into Protocol dataset as in #47. User could specify
meas_patternor build it if necessary.Hi, is there a reason i just cant seem to reconstruct using a different patern than adjacent? The ex_mat and meas_mat seems ok if i do skip2 but any reconstruction doesn't work even by adjusting the constant : w,lamb,n,....
Hello, If you want to try any sequence you specify you should also update the firmware to match. The firmware is also open source.
Hello, If you want to try any sequence you specify you should also update the firmware to match. The firmware is also open source.
Hi, thanks for your quick reply. I already have an updated firmware which match the excitation pattern im trying to reconstruct. Data are acquired on circular phantom
I would like to model a larger array of varied electrode configurations, from the standard tetrapolar adjacent method, opposition method, to bipolar and other user defined electrode configurations that don't always have the same step size. Currently functions seem limited to tetrapolar adjacent/opposition only. Is there a way to allow a user defined file(example)
e_config.txt
Above is an example of a 32 electrode configuration running in random order. Ideally it should acquire the same results as running the simulator but it doesn't as there are dependencies in other files on electrode ordering.