ISDT – Chinese junk

I bought two ISDT Q6 Plus battery chargers over some months, and they both quickly developed the same fault symptoms:

  • jog wheel unreliable / skips; and
  • jog switch unreliable, eventually failing totally.

The worst one was disassembled. These are build a bit like mobile phones, the manufacturer did not want them being disassembled.

Here are pics of the evidence of the problem.

Above is the component side of the PCB, the right hand side is opposite to the rotary encoder for the jog wheel. The PCB around the hand soldered terminals of the encoder has a large amount of flux residue which is usually corrosive to a greater or lesser extent. Over time with atmospheric humidity, this spreads wider and wider, and in this case has penetrated both the encoder and the switch which is to the right of the PCB opening. Continue reading ISDT – Chinese junk

Trying to make sense of Nanosaver’s R/ω and jX/ω graph

Nanosaver v0.4.0 contains a graph that I have found difficult to understand, much less find application.

Above is the graph scaled R/ω and jX/ω, and an untitled X axis, though it would appear to be frequency in Hz (scaled by the M multiplier).

I had difficulty reconciling the Y values plotted for R/ω and jX/ω with the displayed R,jX values.

David F4HTQ offers the following explanation online.

I add some explanations.

I asked Rune if he could add this graphic because it is very useful.
It display curves that have exactly the same shape as the complex permittivity curves (μ’r and μ”r) of the ferrite datasheets.

The values do not match those of the constructor curve ( to have the right value the software might know the exact geometry of the inductor) , but the shape is absolutely identical.

This allow to easy identify unknown ferrite core, and to better understand how to use it in a RF device.

He says permittivity… but he is talking about permeability.

The quote seems to say the Y axis scale is worthless?

In any event, the underlying R,X data only follows µ at frequencies well below the self resonant frequency (SRF) of the inductor.

Let’s try a measurement

Continue reading Trying to make sense of Nanosaver’s R/ω and jX/ω graph

Analysis of output matching of a certain 25W 144MHz PA

Andrew, ZL2PD, contacted me regarding the matching scheme in a 25W 144MHz amplifier published in (ARRL 1977). The design no doubt appeared in many editions of the handbook. He was resurrecting an old build that just didn’t work as expected, and trying to understand why… which starts with understanding how it works, or should work.

Above is the schematic of the amplifier, analysis here is of the 25W configuration using a 2n5591. Continue reading Analysis of output matching of a certain 25W 144MHz PA

The ZS6BKW five band antenna – discussion of an NEC model


(Austin 1987) described a multiband HF antenna that is very popular with hams some thirty years later.

In his article, Austin explained the characteristic of a single wire multiband antenna with a series section matching transformer. The geometry is quite similar to the G5RV with hybrid open wire and coax feed, but Austin pursued lengths of the dipole legs, and matching section length and Zo to optimise VSWR(50).

The design was never an ‘all band’ antenna, but rather a multiband antenna with low feed point VSWR(50) on several bands. Austin tabulated the frequency relationship of the optimised bands for the case of a 400Ω matching section, and they were in the ratio of 1:1.97:2.52:3.47:4.04. If the first frequency was chosen to be 7.2MHz, the other centre frequencies would be 14.2, 18.1, 25.0 and 29.1MHz.

NEC model

To give insight into behaviour of the ZS6BKW I have built and NEC-4.2 model of a ZS6BKW with dipole 28.5m (L1) of 2mm dia copper wire at height of 10m above ‘average’ ground (σ=0.005 εr=13), and 13.44m (L2) electrical length of 400Ω lossless transmission line. L2 was tweaked to optimise alignment of the VSWR(50) response with the ham bands. The model assumes no feedline common mode current.

Above is the VSWR(50) response of the model from 3-30MHz. Minimum VSWR near the nominated five bands is quite low. Note that VSWR(50) at 80m is quite poor. Continue reading The ZS6BKW five band antenna – discussion of an NEC model

Noise Figure Y factor method calculator updated

Recent updates to Noise Figure Y factor method calculator expose the temperature of each of the attenuators in each scenario and so allow more flexibility in application to real world problems.

The screenshot above demonstrates its use where the DUT and Att12 are cryogenically cooled.

For most applications, the default value of 290K is appropriate, so though the form has a few more fields, there isn’t more data entry for most usage.

The calcs have not changed, just replacement of a global Tatt with T for each instance. The input form and output form have been reformatted to suit.


Rigexpert’s Antscope takes a bigger step backwards

At Rigexpert’s Antscope takes a step backwards I wrote of Rigexpert’s determination to cripple Antscope by reducing the maximum value of R and X on graph axes to +/- 1600Ω.

I have deferred trying the new Antscope2 until now to allow it to reach some maturity.

This article is a brief review of Antscope2 v1.0.10, brevity driven by the need to cut losses and run.

The first thing I noted is the difficulty in reading some textual data due to low contrast. The mid blue on mid grey above is very hard to read and would be even harder outdoors if measurements were being made in that environment. I did not search for alternative themes, none jumped out, but out of the box, this is very limiting. FAIL. Continue reading Rigexpert’s Antscope takes a bigger step backwards

RBN for antenna comparisons – Radcom 2018

There are a plethora of articles and presentations on the ‘net showing how to use the Reverse Beacon Network (RBN) to make quantitative antenna comparisons over real propagation paths.

It is certainly an interesting subject to most hams with a deep interest in antenna systems.

So called A/B comparisons of antennas are as old as ham radio itself, and experience hams know that they are quite flawed.

Because ionospheric propagation paths vary from moment to moment, the challenge is to make a measurement that is directly comparable with one made at a slightly different place, or frequency or time. Accuracy is improved by making several measurements, and finding a central value, more observations tends to reduce uncertainty in that estimate of the population central value.

The challenge is finding that central tendency.

Central tendency

There are three common methods of estimating the central tendency of a set of figures:

  • mean (or average);
  • median (or middle value); and
  • mode (or most common value).

The mean is a popular and well known measure of central tendency. It is a very good estimate of the central tendency of Normally distributed data, and in that case, we can compare means and calculate confidence levels for assertions about the difference between means. The mean is very susceptible to errors due to outliers, and skewed distributions.

The median is usually a better measure for skewed data.

The mode is if you like, the most frequent or popular value and has a great risk of being quite misleading on this type of data.

A recent article (Appleyard 2018) in Radcom provides a useful example for discussion.

Figure 3

Appleyard gives a summary table where he shows means of a set of RBN measurements of signals from two stations observed at 21 remote stations, and differences in those means.

There are some inconsistencies between the text and data recorded in the RBN database on the day. Continue reading RBN for antenna comparisons – Radcom 2018

A tutorial on estimating the impedance of a toroidal ferrite cored inductor for radio frequencies

This article is a walk through of a process for designing a toroidal ferrite cored inductor for radio frequencies.

Designing with magnetics can be a complicated process, and it starts with using reliable data and reliable relationships, algorithms, and tools. Continue reading A tutorial on estimating the impedance of a toroidal ferrite cored inductor for radio frequencies

W3LPL’s paired WSPRlite test – test 2

Frank, W3LPL conducted two interesting experiments with WSPRlites on 20m from the US to Europe essentially.

The first experiment was a calibration run if you like to explore the nature of simultaneous WSRP SNR reports for two transmitters using different call signs on slightly different frequencies simultaneously feeding approximately the same power to the same antenna.

This article is about the second test which he describes:

The second test uses a WSPRlite directly feeding the same stacked Yagis, and the second WSPRlite feeding nearly identical stacked Yagis that point directly through the other stack located four wavelengths directly in front. Power at each antenna was about 140 milliwatts for each WSPRlite.

The data for the test interval was extracted from DXplorer, and the statistic of main interest is the paired SNR differences, these are the differences in a report from the same station of the two signals in the same measurement WSPR interval.

There is an immediate temptation of compare the average difference, it is simple and quick. But, it is my experience that WSPR SNR data are not normally distributed and applying parametric statistics (ie statistical methods that depend on knowledge of the underlying distribution) is seriously flawed.

We might expect that whilst the observed SNR varies up and down with fading etc, that the SNR measured due to one antenna relative to the other depends on their gain in the direction of the observer. Even though the two identical antennas point in the same direction for this test, the proximity of one antenna to the other is likely to affect their relative gain in different directions.

What of the distribution of the difference data?

Above is a frequency histogram of the distribution about the mean (4.2). Each of the middle bars (0.675σ) should contain 25% of the 815 observations (204). It is clearly grossly asymmetric and is most unlikely to be normally distributed. A Shapiro-Wik test for normality gives a probability that it is normal p=4.3e-39.

So lets forget about parametric statistics based on normal distribution, means, standard deviation, Student’s t-test etc are unsound for making inferences because they depend on normality. Continue reading W3LPL’s paired WSPRlite test – test 2