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Nonlinear Neural Nets Smooth Wi-Fi Packets
mindless4210 writes "Smart Packets, Inc has developed the Smart WiFi Algorithm, a packet sizing technology which can predict the near future of network conditions based on the recent past. The development was originally started to enable smooth real-time data delivery for applications such as streaming video, but when tested on 802.11b networks it was shown to increase data throughput by 100%. The technology can be applied at the application level, the operating system level, or at the firmware level."
..How could it be used in firmware, hardware, and software?
It can increase throughput but caan it deal with noisy conditions. IMHO noise filtering is more important
This sounds like a scam. The CEO is a furniture salesman, the CTO was a consultant to DEC with an EE from the University of South Maine.
To me, this sounds like (in the simplest form) that this is a variant on the Tit for Tat strategy that is usually applied to the Prisoner's Dilemma.
I hear all too often from people in the field of machine learning who get their favourite solution (SVMs and NNs are the most common) and then they go hunting for a problem.
It might not be exactly the best technique, but if at the time it was the easiest to understand and use, and gave really good results, then the right decision was made.
Is that the difference between theory and practice right there?
Well, it's quite obviously because a Support Vector Machine is inherently linear, and to make it nonlinear, you must insert a nonlinear kernel which you need to select by hand.
Not true.
"This invention provides a selection technique, which makes use of a fast Newton method, to produce a reduced set of input features for linear SVM classifiers or a reduced set of kernel functions for non-linear SVM classifiers."
There are online methods using both the techniques you mention. The theory is usually a little more involved, so you're not likely to get a good tutorial from page 1 of google results.
Try MIT's open courseware (Machine Learning course) for some better explanations of this stuff, if you can handle the maths, ughhh.
Actually the whole point of SVMs is that they can be used to model non-linear decision boundary. Contrary to the above post selecting the non-linear kernel isn't a big deal because the three common ones (polynomial, radial basis function and sigmoid ) generally produce similar classification results in most applications. Also SVMs are pretty damn fast to train and update since only the support vectors need to be remembered and changed. Just check the literature...
I figure the real reasons they use NNs are much simpler. Firstly, its really easy to implement NNs that predict numeric values instead of classes and even more importantly they work. Research usually involves trying everything under the sun and reporting/patenting/exploiting whatever worked best.
I couldn't agree more. Last summer I was working with a satellite link from a third world country. Now the pipe on those guys is pretty big, we were working with 3MBps. 24Mbps. However, latency on the tcp packets from the south pacific to space and back to sunny CA is such a large problem it slowed an asynchronous transfer to about 20KBps. I had to write a special segmented upload package to be able to send video feeds sychronously and maximize the bandwidth utilization.
In other words, bandwidth will do you zero good with a traditional tcp/ip stack if your latency is too high.
perl -e '$_="\007/4`\cp%2,".chr(127);s/./"\"\\c$&\""/gees
Interesting question. I would assume that any given existing 802.11b adapter can receive packets of any size, given that there's a protocol to packets that lets the receiver know how big they should be or when they've finished. Thus you could just deploy a new access point and get a boost from it to the computers. Similarly, you could install a new NIC in a particular PC and boost the transfer rate from it to the access point. For benefits in both directions you'll have to upgrade both ends.
I vaguely remember a friend working for a startup doing this down in houston in 2000. They were basically shorting stocks on the nasdaq, the idea being the nazdaq was volatile, so any time it took a slight dip it would probably continue to dip.
he's at another job now in a different state, so the real answer is no, there are no commercially viable Holy Grails of Day Trading.
I'm pretty sure that's not the case. Besides, if the technology you're pushing boils down to 'variable-sizing', seems like someone's thought of that before.
As far as neural networks are concerned, a sufficiently complex neural network can adapt to learn reasonably complicated functions. Backpropagation networks and radial basis function networks can make good filters and make sense of noisy data. A network that doesn't adapt its structure boils down to a few matrix operations, so it's easy to script in Matlab.
With all that in mind, shouldn't they have looked at Kalman filters?
The whole point of SVMs is that they can be used to model a linear decision boundary. They were developed to find a maximum-margin hyperplane separating positive and negative training instances, and the kernel methods to allow them to work on non-linear boundaries were a later addition.
10 PRINT CHR$(205.5+RND(1)); : GOTO 10