i am doing my final year proj in fingerprint authentication. i 've completed binarization, thinning, minutiae extraction. i am doing it in matlab. i am stuck with the verification. i don't know how to perform verification. so please help me out to solve this problem. i would be grateful to ppl who help .. thanks in advance. please help me out with code for verification or with algorithm atleast... thanks a lot...
The usual way is to use some form of statistical classifier. The most simple is Bayesian (which I believe won't do for fingerprints). In biometry most popular ones are GMM (Gaussian Mixture Model) or HMM (Hidden Markov Model). Another approach is to use neural network as a classifier.
I can't help you with writing your own verification, but i did fingerprint verfication in the past with Griaule Fingerprint SDK. I hope this helps a bit.
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I got a solution for my model, how could I know whether it is feasible in CBC?
Thank you very much!
Just a general question here, no reproducible example but thought this might be the right place anyway since its very software specific.
I am building a model which I want to convert to .tflite. It relies on tf.hub.text_embedding_collumn() for feature generation. When I convert to .tflite will this be captured such that the resulting model will take raw text as input rather than a sparse vector representation?
Would be good to know just generally before I invest too much time in this approach. Thanks in advance!
Currently I don't imagine this would work, as we do not support enough string ops to implement that. One approach would be to do this handling through a custom op, but implementing this custom op would require domain knowledge and mitigate the ease-of-use advance of using tf hub in the first place.
There is some interest in defining a set of hub operators that are verified to work well with tflite, but this is not yet ready.
I'm facing a problem with modelling diffusion in Dymola.
I want to have two seprate volumes (filled with air), which can be joined and thus exchange heat via diffusion.
My approach was using the Modelica.Fluid library and connect two ClosedVolumes with a Valve.
But as I found out, this library doesn't regard difussion.
What would be the best way to accomplish such a model?
This limitation is due to the use of stream connector in the Modelica.Fluid library.
One way to solve this is to develop a fluid connector which do not rely on stream connector but only on potential and flow variables. Unfortunately in this case you'll have to solve yourself numerical problems for solving flow reversal and zero-flow singularity.
One example is described in the paper "A physical solution for solving the zero-flow singularity in static thermal-hydraulics mixing models" presenting in the Modelica conference 2014. Basically, adding diffusion helps to solve zero-flow singularity and they use a regularized step function to solve flow reversal. Other regularization functions can be found in Modelica.Fluid.Utilities.
Hope this help,
Best regards.
I am a university student and trying to write a program to solve 1-D Schrodinger's equation with some kinda of potential equations.
I am not a CS major so really have no clue to start.
I did research online but didn't find thing that is suitable for entry level :( The only thing I understand now is that I probably need to use some algorithm solving differential equations
Could anyone give me some suggestions or references on how I could start? For example, how to transform the physical problem into computer science program and what kinda algorithm I should look for?
I am interested in computational so trying on this HARD problem ;)
Thank you all!
go to www.google.com
search for :how can i solve Schrodinger in matlab
go to here
Return stackoverflow
I'd be very much interested hearing any tips on how
a simple algorithm can be implemented to overcome
lost packets of a binary stream.
I was considering doing something like
ABCDE,BCDEF,CDEFG,DEFGH,.....
or may be with 3 bytes instead of 5, i.e.
ABC,BCE,CDE,DEF,...
Though I'm not too sure of this.
I understand the basic principles behind
error correcting theory, but it doesn't
help a lot .. any suggestions ?
I think I have found what I was looking for - it's the Convolutional
Coding that need to use in this sort of application. It looks like
the most suitable coding technique for a low-power application.
For those who interested in updates on this, I'll write more on my
project's wiki
There is an article on Wikipedia whcih has a nice diagram.