I got your point. I just disagreed with the definitions of information and noise. My point was that when humans are involved, the definitions are highly variable. So the same speech may contain information for one person, but not for someone else. That's one of the problems with applying facts and processes from the physical sciences as metaphors for human behavior and experience.
I think you've missed my point. The signal is the sound pressure wave, or whatever, that you receive. It contains information and noise. Thus a speach that does not contain information IS noise and a news article that does not contain information IS noise. It also means that signals containing stuff that is self evident are not information, i.e. they are nosie.
Off topic - I always get some sort of error message when I access these pages. Everything looks fine except for the text "Error on page." Is it just me or are others getting this as well?
LOL walter, that made me laugh. OTOH, when it comes to humans, the signal is in the beholder, so to speak. For some people, those puppies are definitely a signal, just not the one other people may have been expecting when they tuned in. And a speech can certainly contain multiple types of signals meaningful to humans, but not all humans (even those that speak the language) will pick up the same signals from the same speech. This is, of course, assuming that we define "signal" as meaningful content, or information.
(maybe off topic a bit, but...) As an ME using electronic gear for making vibration and dynamic stress measurements, the prevalence of RMS has always been a nuisance. We measure most things in P-P. Failures occur on the peak stress values and journal bearing damage can occur when rotors move more than the available distance within the bearing clearance. The early spectrum analyzers (like the HP 3582A or 5451c) would make RMS measurements apparently because they came from the EE world. We had to do odd things like use voltage dividers to obtain PP spectral values (or actually 0.1 PP).
Now, although any DAQ system can provide PP, many of the allowable specs were written for RMS and are still used.
Thanks for your comment. It is good to get a peak-to-peak value along with a root-mean-square (rms) value. The latter provides a statistical value that squares amplitudes and thus accounts for positive and negative noise. With a peak-to-peak value you don't know whether you observe that value all of the time, most of the time, or infrequently. So to me, a p-p value represents a worst case and rms represents an average value. Both have value. --Jon
Having been in a couple of different industries, I often wonder why noise is specified in RMS. RMS makes sense, for example, in video, where the eye integrates and RMS gives you a direct measurement of how the image would look to the human eye. But what about other applications? If you are trying to determine uncertainty in a measurement, wouldn't specifying noise in RMS give you a very large discrepancy in numbers versus performance? Of course, comparing RMS to RMS of different systems will give you the relative performance comparison. Now take a step from comparing systems to Output. What does your output respond to? If it responds to every peak, then giving an RMS noise measurement is marketing not engineering.
What do you think?
I get in trouble with our sales department because I measure in P-P and at the point of greatest noise. This, to our customers, would give them the total uncertainty, rather than a number that tells them the total uncertainty can be 6 to 8 times higher.
Are they robots or androids? We're not exactly sure. Each talking, gesturing Geminoid looks exactly like a real individual, starting with their creator, professor Hiroshi Ishiguro of Osaka University in Japan.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.