search for: canisters

Displaying 5 results from an estimated 5 matches for "canisters".

Did you mean: canister
2011 Feb 22
1
funding
Maybe what Centos needs is a bridal registry. Here in the US, an engaged couple can tell their friends what they'd like to be given as wedding presents. They do this by listing items in a registry, in various stores around town. Anyway, the idea is, post stuff you need in a list on your site. Say you need 20 hard drives, or a particular power supply, or whatever items that get consumed in
2007 Apr 19
14
Experience with Promise Tech. arrays/jbod''s?
Greetings, In looking for inexpensive JBOD and/or RAID solutions to use with ZFS, I''ve run across the recent "VTrak" SAS/SATA systems from Promise Technologies, specifically their E-class and J-class series: E310f FC-connected RAID: http://www.promise.com/product/product_detail_eng.asp?product_id=175 E310s SAS-connected RAID:
2009 Mar 13
2
S32_LE to S16_LE
Hi developers, I would appreciate if someone can give me a hand here... I need to run speex on a FPU-less platform with an audio card that only reads samples of type S32_LE (even when it is a 16bits audio card). I understand that if my platform is FPU-less then I should use speex_encode/decode_int() but then, how can I convert my S32_LE sample to S16_LE in order to pass it to speex
2012 Apr 06
1
kernel LANG option
One thing that irritates me is that I'll copy something from a site in ff and paste it into webmail (squirrel/ensign, if that matters); then, in the evening, when I want to forward it to one or more of my lists, using t-bird, I have to clean it up *all* the time: 2-3 characters replacing ",' and -. I can't seem to find anything in either the webmail options (which I have set to
2006 Jul 24
2
RandomForest vs. bayes & svm classification performance
Hi This is a question regarding classification performance using different methods. So far I've tried NaiveBayes (klaR package), svm (e1071) package and randomForest (randomForest). What has puzzled me is that randomForest seems to perform far better (32% classification error) than svm and NaiveBayes, which have similar classification errors (45%, 48% respectively). A similar difference in