Unfortunately, when you do a logarithmic sweep, you can’t cover from negative to positive frequencies–which is what you want when you generate a Nyquist plot. With the code below, you can reflect the positive frequencies onto negative to get the full Nyquist plot:

You’ll need Andrew Beckett’s abConcatWaveforms function defined and loaded.

Incrementing index:

Run through a list:

Open a File

Opens new file for writing, existing file for reading, and partial file for appending, respectively.

Write to a file

Write “Hello World!” to a file (newlines are explicit); construct formatted string and write to file; print using native format to a file; pretty-print (useful for nested lists etc) to a file. File handle is h.

Read from a file

Reads a single line as a string

• Into the file “cdsLibMgr.lib” was created the menu item “SVN Unlock Cell”
• Into the file “svnLockCell.il” were created the procedures “svnUnlockCell” and “svnUnlockCellFormCB”
• Inside the procedures “svnUnlockCellFormCB” and “svnUnlockCVFormCB” were made the following changes:

• where you have: if( !(rexMatchp(“.*.svn$”, file~>readPath) || rexMatchp(“.*%”, file~>readPath))
• was changed to: if( !(rexMatchp(“.*.svn$”, file~>readPath) || rexMatchp(” “, file~>readPath))

Behaviors observed:

• We don´t want the files *.cd% under version control, but if for some reason they are under version control that exist the possibility to lock them or unlock them.
• Copying a “cell” or a “view” by “Library Manager” doesn´t copy files *.cd% which is not a problem because we don´t want these files under version control.
• The command “[user@host CDSVN]$ svn lock *” or “[user@host CDSVN/bin]$ lock directory/” locks the files *.cd%, however, the “CDSVN Unlock Cell” or “CDSVN Unlock View” does not unlock.
• The “CDSVN Lock Cell” or “CDSVN Lock View” does not unlock the files *.cd%, however, the commands “[user@host CDSVN]$ svn unlock *” or “[user@host CDSVN/bin]$ unlock directory/” are able to unlock.
• Copying by the command “[user@host CDSVN]$ svn copy” copies the files *.cd% only if they are already under version control. The same behavior occurs for “CDSVN Copy Cell” or “CDSVN Copy View”. This is not a problem.
• The command “[user@host CDSVN]$ svn add”, “CDSVN Add Cell” or “CDSVN Add View” does not add files *.cd% even because this type of file is in the list of ignored files as showed below:
  [user@host /home/user/.subversion] vi config
  global-ignores = *.cd% *.cd- *.cdslck *.Cat% *.abstract.status *.abstract.messages *.inca* *inca* .cdsvmod expand.cfg% transcript_ms

  [user@host CDSVN/]vi cadence_ignores.txt
  *.cd%
  *.cd-
  *.cdslck
  *.Cat%
  *.abstract.status
  *.abstract.messages
  *.inca*
  *inca*
  ….
  cdsvmod
  expand.cfg%
  transcript_ms

Their updated files are attached. Special thanks to PCS for coordinating this. Remember, the CDSVN scripts are licensed under GPL.

cdsLibMgr.il
svnLockCell.il
cdsLibMgr.il

Also, you’ll notice that Google Ads are prominently displayed on this blog. I wanted to recoup the hosting costs of running this blog. Unfortunately, that didn’t end up being as profitable as I thought. So, as soon as I hit $100 of ad revenue (which is the minimum to cash out of my Google AdSense account), I’ll remove the ads. I’ll essentially be eating the hosting cost (in addition to the time I’ve put into the blog).

Unless I change my mind. Which is unlikely.

Also, I might try out some more social features on the web site. If I get the time. Which is also unlikely.

Unfortunately, this can’t be done over a large range, because as Foster’s reactance theorem states, as soon as you deviate a little from your center frequency, both the inductive reactance of your antenna and the reactance of whatever you’re using to cancel it (most likely a capacitive element) both increase (go toward ). So, for every change in frequency from the center frequency, your antenna reactance goes up by some amount , but your matching element’s reactance also goes up by some amount .

If you had a Non-Foster element, the reactance of your tuning element would go down by some amount , compensating for the increased reactance of the antenna. You would then have  a broadband (or broader) match.

Most attempts to do this have required the use of active elements (such as gyrators) so synthesis a negative impedance. However, I’m wondering if  a switched-capacitor circuit can be used to synthesize this Non-Foster reactance. Most analyses of switched-capacitor circuits show that they are synthetic resistors at frequencies far below the switching frequency. However, what does the impedance look like near the switching frequency?

Well, since you’re pumping current into the circuit at exactly the rate of switching, you should see a large voltage. As a result, it should look like a high-impedance near the switching frequency. I would tend to believe that the switching function takes the usual function and modulates it up to the switching frequency. (I haven’t proved this and probably won’t ever prove it.) However, if one thinks about the following picture, the current coming into the capacitor can be seen as the independent variable. Then, the voltage is the regular voltage, except mixed by the switching function. Thus, the product should be some modulated version of the fundamental capacitor’s impedance.

The upshot of all this mixing is that just below the switching frequency, one would have a reactance that’s decreasing. (I’m doing a considerable amount of hand-waiving here, excluding such things as aliases of the switching harmonics.) What’s even cooler about this reactance is that the Non-Foster region is determined by the switching frequency–so, one could move it around just by changing the switching frequency. You’d effectively have a digitally-tunable antenna.

There are a number of problems with this scheme, most notably the radiation of switching noise and any switched-capacitor noise effects. However, I think it’s worth looking at and building upon.

If the distribution is Gaussian, the mean and median are equal.

(Mean is defined as where is the probability distribution function (PDF) of —that is, it’s a average of X, weighted with the probability density of . The median defined as —that is, the point where is equally likely to be lower than or greater than (50% probability).)

Many times in engineering and process control, we keep track of the mean and standard deviation. One of the reasons is that if the thing we’re trying to control is Gaussian, the mean/median and standard deviation give us good design criteria to minimize failure: if we allow our system to tolerate 3 standard deviations () around the mean/median, then it has a 99.7% chance of success (0.3% chance of failure).

However, we can generalize this: if we wanted to be more lax, we could only design (or require) the system to tolerate 2 standard deviations (4.5% failure). In some cases, systems are designed to tolerate 4 standard deviations (0.006% failure). So, one can design the system to tolerate , where is some factor (3, 2, 4 for example) that determines the probability of failure.

However, what if the distribution is bimodal? Take for example, two modes of operation (each more or less Gaussian):

Due to the asymmetric distribution, the mean and median are now not the same. In this case, we could posit that some secondary mode (or external factor) causes that second hump. Let’s call the main hump the primary mode and the smaller hump the secondary mode. If things are behaving “normally” we get the first hump, but some failure or aberration causes the second hump.

However, what if the system was more sensitive to this failure (secondary mode). Then, we’d see something like:

Notice what happened? The median stayed exactly the same. However the mean mislabeled “average”) moved proportionally to that secondary hump. Incidentally, the standard deviation () also moved proportionally to the distance between the two humps—but let’s focus on the fact that the mean just changed.

The question you’re probably asking is “what’s so bad about that”? Well, if you’re computing six-sigma-like design criteria, you’re taking . Recall, however, that we could pick any factor depending on the probability of failure we want (I should say want to avoid). So, when both the average and the standard deviation change, how can we be sure we’re getting the right value for ?

The nice thing about picking the median as the average is that it doesn’t depend on the magnitude of the secondary mode—only on the probability of the secondary mode. The magnitude of failure impacts the standard deviation. I like to view these (median and standard deviation) sas two independent metrics that tell different stories.

Another thing to note is that one could view the 2nd illustration above as an input to a nonlinear amplifier (for example) and the 3rd illustration as the output. That’s another nice thing about the median: it commutes with a monotonic nonlinearity. That is, if is monotonic, and , then . So, we don’t have to worry so much that we’re measuring the correct independent variable. Our median will give us the same information (albeit in a different, nonlinear domain).

Expect a bit more down-time: I’m going to do a bit of experimentation to ensure that future down-time doesn’t happen again (improve reliability).

You rock, Justin! I (and my readers) thank you.