On 6/4/2013 6:26 PM, Roger (K8RI) wrote:
On 6/4/2013 2:12 PM, David Robbins wrote:
that sounds like a nice idea, but is much harder than it sounds.
first, the software would have to know how to sort out the noise from
the desired signals. it would also have to be able to scan wide
bandwidths, maybe even outside the range of most hf receivers, just
using 3khz wide audio is impossible in many cases... the grow light i
found had a fundamental around 100khz with differing amplitude even
and odd harmonics to above 4mhz where they started to overlap making
an almost continuous noise, video systems could have fundamentals as
low as 15khz, and harmonics can cover very wide bandwidths, doing a
thorough analysis may require covering a large portion of the spectrum
while ignoring many different legal signals. plus since switching
supplies are becoming plugin components there may be many devices
using identical supplies and thus creating identical signatures.
It's both much simpler and much harder...
Some of the things needed are the simple, frequency, harmonics, rise and
fall times. then it starts getting messy with changes in rise time IE:
shape of rising wave form, and the same for falling as well as peak
shape. Then there a changes in characteristics versus time (short term
and long term) periodic variations/patterns, ringing or oscillations
triggered by the pulses, and these patterns often change with load, age,
temperature, and humidity.
This is where all that math required to earn a degree in CS begins to
pay off.
You may be lucky to just isolate the signal to Ballasts, power supplies,
and street lights. Then again it may be possible to identify them right
down to make and model in some instances.
Of course if you come up with some good algorithms that really work well
and make them public, you may discover they are either copy right, or
patented. <:-)) No good deed goes unpunished!
73
Roger (K8RI)
You only need to look for specific characteristics and generally these
can be determined by looking at only a few spectrum segments and
matching them against known offending signals..
As you are looking for specifics you don't normally have to sort any thing.
The difficult part is developing and cataloging the signals into the
database. Once the database is developed, you only need the complicated
software to identify new signals. Like Voice recognition and finger
prints you are matching key points so even if there are other signals
present you only need match the key points. Of course you do need to be
able to hear those points. So if looking for the ID of an S4 signal
under a 20 over 9 SSb signal you will need for them to take a breath.
This is one of the few legitimate reasons for "the cloud", A place that
should be avoided unless you have no choice.
The difficult part is developing the database and recognizing the new
sources. Once developed it becomes one of matching key segments.
So some parts are very difficult while others can be relatively easy as
algorithms already exist for the pattern matching. OTOH pattern matching
often takes multiple samples, some times for extended periods gaining on
and off cycles. It may come down to statistical matching which does not
normally result in quick answers.
Jun 4, 2013 01:32:21 PM, w4byg@att.net wrote:
Interesting article.
As a result a thought: Wouldn't it be great if someone could develop
software that when the output from your HF receiver is connected to your
sound card, it could help identify the kind or type of noises received?
It seems this would help decrease the time it would take to track down
the source(s).
The software probably exists that could do this now, but if the
algorithm is involved in pattern matching, the chances are the software
is not available outside of it's proprietary use.
73
Roger (K8RI)
Ray, W4BYG
On 6/4/2013 7:50 AM, Steve Sacco NN4X wrote:
Fascinating RFI story from Australia.
http://www.itnews.com.au/News/345494,rogue-beer-fridge-caught-by-telstra-robot.aspx
73,
Steve
NN4X
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