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.
Related
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
I'm using the minimize function in Z3 a lot and I'm worrying about some scalability issues (when the number of variables I'm minimizing grows). What is the underlying algorithm of "minimize" and is there a general way to speed things up?
See this paper for details on the optimization algorithms used in Z3. Regarding your question about "general way to speed things up:" Impossible to tell without seeing exactly what you're trying to do and how you are encoding it. Posting a concrete example where things don't "scale" might be helpful.
I want to use feature selection to find the terms in a document that are most useful for a binary classification task.
I've been looking around:
This mentions Mutual Information and the chi-squared test metric
http://nlp.stanford.edu/IR-book/html/htmledition/feature-selection-1.html
MATLAB has a number of functions as well:
http://www.mathworks.com/help/toolbox/stats/brj0qbu.html
Feature Selection in MATLAB
Of the above, relieff and rankfeatures look promising.
I do not know if my data follows a normal distribution. Any thoughts on which technique performs the best? Are there any newer methods you would suggest? The focus is to increase classification accuracy.
Thank you!
Since the answer is highly dependent on the nature of your data, I'd suggest playing with several options, possibly using a hold-out set for verification.
The easiest path would probably be to use Weka or RapidMiner for experimenting. Choosing from the plethora of options provided by them, you'll probably get acquainted with several other methods.
Having said that, I have found Mutual Information/Infogain to be useful on a large variety of problems.
I've done some research and so far haven't found anything very helpful. My question is what technology can I use to identify at least most nude images, etc. when being upload from users to my site and not allow them to be posted? From what I understand, there isn't anything "perfect" of course but I would like to get a good start with something half decent verses nothing at all what-so-ever. Any suggestions, links, or helpful bits of information are greatly appreciated!
You could make a histogram of the images and check for the amount of skin-tone-ish pixels.
I would go the route of building your own.
Here is a good project that has helped me in my first attempts at image recognition
http://www.codeproject.com/KB/cs/BackPropagationNeuralNet.aspx
There has been a lot of success with writing tools that look for nude images. Generally speaking, they look for certain amounts of skin-tone and/or shapes. Those will flag potential images for human review.
good luck!
Is anyone aware of a reasonably well documented example of simulated annealing in Visual Basic that I can examine and adapt?
This project looks pretty well documented: http://www.codeproject.com/KB/recipes/simulatedAnnealingTSP.aspx. It's C# but contains only one important source file (TravellingSalesmanProblem.cs) so it's pretty easy to run it through a converter. Maybe: http://labs.developerfusion.co.uk/convert/csharp-to-vb.aspx?
MSDN magazine also had an interesting article on neural networks. As I understand simulated annealing, you can add it to other function estimation methods (like neural nets). So you could add simulated annealing to the MSDN VB code by shrinking the Momentum over time. The network starts 'hot' by backpropagating error with a large Momentum and slowly 'cools' by shrinking the Momentum and thus reducing the effect of output error in backpropagation.
Cheers.
I generally refer to "Numerical recipes in C/C++" for all the pseudocode and adapt to my own later. That is the best documentation/implementation you could find. Sometimes you could even find better algorithms or an alternative way of solving. (In case Newton Raphshon is not the way to go)