I got this question from some peer,
he told that there can be 2 bit error detection algos and that is ECC, but, he asked me read up on net but, where can i find it?
For example, if I have data- 10010011, then analyse data with ECC for 1-bit error detection and also for 2 bit error detection.
How should I approach this probllem?
Please, help me guide.
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The problem statement is kind of vague but i am looking for directions because of privacy policy i can't share exact details. so please help out.
We have a problem at hand where we need to increase the efficiency of equipment or in other words decide on which values across multiple parameters should the machines operate to produce optimal outputs.
My query is whether it is possible to come up with such numbers using Linear Regression or Multinomial Logistic Regression algorithms, if no then can you please specify which algorithms will be more suitable. Also can you please point me to some active research done on this kind of problem that is available in public domain.
Does the type of problem i am asking suggestions for comes in the area of Machine Learning ?
Lots of unknowns here but I’ll make some assumptions.
What you are attempting to do could probably be achieved with multiple linear regression. I have zero familiarity with the Amazon service (I didn’t even know it existed until you brought this up, it’s not available in Europe). However, a read of the documentation suggests that the Amazon service would be capable of doing this for you. The problem you will perhaps have is that it’s geared to people unfamiliar with this field and a lot of its functionality might be removed or clumped together to prevent confusion. I am under the impression that you have turned to this service because you too are somewhat unfamiliar with this field.
Something that may suit your needs better is Response Surface Methodology (RSM), which I have applied to industrial optimisation problems that I think are similar to what you suggest. RSM works best if you can obtain your data through an experimental design such as a Central Composite Design or Box-Behnken design. I suggest you spend some time Googling these terms to get your head around them, I don’t think it’s an unmanageable burden to learn how to apply these with no prior experience in this area. Because your question is vague, only you can determine if this really is suitable. If you already have the data in an unstructured format, you can still generate an RSM but it is less robust. There are plenty of open-access articles using these techniques but Science Direct is conveniently down at the moment!
Minitab is a software package that will do all the regression and RSM for you. Its strength is that it has a robust GUI and partially reflects Excel so it is far less daunting to get into than something like R. It also has plenty of guides online. They offer a 30 day free trial so it might be worth doing some background reading, collecting the tutorials you need and develop a plan of action before downloading the trial.
Hope that is some help.
I'm looking an algorithm that analyzes a portrait-photo of a person and outputs a descriptive text like "young man, rather long nose, green eyes".
It doesn't matter if the output is very precise or not; it is for an art installation. But it should be possible to do it automatic.
I found this one: https://code.google.com/p/deep-learning-faces/, but it is impossible for me to fulfill the hardware and software requirements (NVIDIA Fermi GPUs & matlab)
Do you know of anything more accessible?
There are a few free face analyser APIs that are fairly easy to use:
Rekognition, by Orbeus
MP Face Analyzer SDK (evaluation) by MotionPortrait
Faceplusplus (linked above)
You might have to take measurements of an "average face" to make interpretations like "long nose". ToonifyMe is an app that caricatures faces using this approach.
Some of these API's can actually work on a Pi. Recognition does the analysis in the cloud, so that should be doable.
This is one of the hardest problems in computer vision. I'd recommend you watch the ted talk by Fei-Fei Li to get an understanding of it:
https://www.ted.com/talks/fei_fei_li_how_we_re_teaching_computers_to_understand_pictures
In short: If you want to use any of the state-of-the-art methods you will need a lot of processing power. A lot more than just a single high-end graphics card, I'm talking about super computing here.
And unless you're really lucky and find a research group that has released their implementation, this also requires a huge amount of engineering.
I found this online service that describes faces: http://www.faceplusplus.com/
It has a very well documented API and seems to be free of charge. Or at least I didn't find any information about pricing.
all the pattern-recognition related posts here are dealing with face-, hand-, characters-recognition.
I wonder, is someone has successivly used OpenCV for recognizing cells on a picture from a microscope.
What I am currently able to do, is counting cells using threshold and shape-detection (change threshold, count shapes, discard shapes with invalide size).
My next task is recognizing among about 20 types of cells. It would be very interesting to exchange the experience. May be, the OpenCV is not the proper/overweighted tool for this?
Regards,
Valentin Heinitz
EDIT
An up-vote has drown my attention to this old question again. I completed the task finally with OpenCV. It works good and the tool was approved by FDA last year as a software part of a diagnostic device :-)
Now I think OpenCV was the perfect tool for this, despite I had to implement Haralick-Features myself.
your problem is a machine learning problem. OpenCV offer a few tools for that, SVM would be a good fit for what you are trying to do. I have experience with Kohonen neural networks, which would be a good idea too, if you have a good database of your cells. However, I do not know how good you are in C++, but it is always a good idea to start with matlab, get your algorithm to work, and then rewrite it in C++. To test your ideas quickly, matlab is definitely a better tool, with a lot of ML functions availables.
I'm doing some research on machine learning algorithms that would be useful for processing image data and using them for recognition purposes. I've stumbled across SpikeNET and thought it had potential. However their example code is very confusing (the comments are in French) and being on a Windows box I cannot compile the project without fiddling around in Cygwin too much.
If anyone has any further information on the Spiking Neuron technology or any other highly researched machine learning techniques that yield good results, I would be highly interested.
Thanks in advance.
Well, for object detection the more "standard" state of art approaches are haar cascades and SIFT features.
As for "working code" have you spent any time at all poking around OpenCV? this is a very complete computer vision library that can help you along the way. Perhaps start here?
I'm new to image processing and I want to do a project in object detection. So help me by suggesting a step-by-step procedure to this project. Thanx.
Object detection is a very complex problem that includes some real hardcore math and long tuning of parameters to the computation methods involved. Your best bet is to use some freely available library for that - Google will help.
There are lot of algorithms about the theme and no one is the best of all. It's usually a mixture of them what makes the best solution to the solution.
For example, for object movement detection you could look at frame differencing and misture of gaussians.
Also, it's very dependent of your application, the environment (i.e. noise, signal quality), the processing capacity you may have available, the allowable error margin...
Besides, for it to work, most of time it's first necessary to do some kind of image processing to the input data like median filter, sobel filter, contrast enhancement and a large so on.
I think you should start reading all you can: books, google and, very important, a lot of papers about the subjects (there are many free in internet) you are interested in.
And first of all, i think it's fundamental (at least it has been for me) having a good library for testing. The one i have used/use is OpenCV. It's very complete, implement many of the actual more advanced algorithms, is very active, has a big community and it's free.
Open Computer Vision Library (OpenCV)
Have luck ;)
Take a look at AForge.NET. It's nowhere near Project Natal's levels of accuracy or usefulness, but it does give you the tools to learn the algorithms easily. It's an image processing and AI library and there are several tutorials on colored object tracking and motion detection.
Another one to look at is OpenCV from Intel. I believe it's a bit more advanced, but it's written in C.
Take a look at this. It might get you started in this complex field. The algorithm pages that it links to are interesting reading.
http://sun-valley.stanford.edu/projects/helicopters/final.html
This lecture by Jeff Hawkins, will give you an idea about the state of the art in this super-difficult field.
Seems that video disappeared... but this vid should cover similar ground.