## Having Fun with 75 Ohm Technology

If one wants to capture analog images or analog videos (e. g. images coming from a CCTV camera), those signals need to be digitized, decoded and displayed with a device called framegrabber. A framegrabber is basically a video capture card with fast video processing capabilities and onboard memory which is useful for image processing.

I wrote in my previous post how to make good quality screenshots of the Anritsu MS2661N front display. However, there is another ancient method how to acquire images of frequency spectra. In modern test equipment, the video signals are transmitted digitally via DisplayPort or HDMI. Some 20 to 40 years ago, the manufacturers offered instruments with an optional analog video output which could be connected to a television screen or to a video recorder for viewing or documentation purposes.

I’ve got myself a framegrabber card NI PCI-1405 for different projects. I wanted to test the card for its functionality. The quickest access to an analog video was via the composite output of my spectrum analyzer as shown in the previous picture.

The NI PCI-1405 framegrabber card has two inputs: a 75 Ohm video input and a 75 Ohm trigger. The video input is connected via a 75 Ohm cable (type RG-59) to the composite output of the spectrum analyzer.

After installing the drivers from National Instruments, the framegrabber card was successfully detected and I was able to grab some frames inside of NI MAX (Measurement & Automation Explorer).

I didn’t bother to improve the image quality – this task will be relevant for future projects as soon as I get my Tektronix C1001 camera running 🙂  So far, it’s been a successful test!

# Introduction

A spectrum analyzer (SA) is a very useful tool when it comes to measure spectra of radio frequency signals. I recently acquired a 2004 era spectrum analyzer. It’s from a Japanese test equipment manufacturer Anritsu and the model number is MS2661N. Luckily there are operating manuals available online but I wasn’t able to find service manuals for this type of spectrum analyzer on the internet. There are some service manuals available for similar models of spectrum analyzers (e. g. MS2650/MS2660) which would allow troubleshooting but I would be lost if the instrument breaks.

However, I’ve been looking for a decent SA for a longer time and stumbled upon the Anritsu MS2661N. It had a bunch of very nice and useful features: frequency range 100 Hz to 3 GHz, 30 Hz resolution and video bandwidth, oven controlled crystal oscillator (OCXO), GPIB interface, 10 MHz reference IN/OUT and a tracking generator ranging from 9 kHz to 3 GHz. I was looking for a similar SA from HP/Agilent 8590 Series or Tektronix but there were no attractive offers at the time. Either the SA frequency range was too low for modern ages (1 GHz) or outside of my measurement capabilities (26 GHz), the price was either too high or it was partially broken. There were also 75 Ohm spectrum analyzers which aren’t very useful for what I’m doing. On the other side, the documentation for HP/Tek hardware is the real deal so leaving this kind of test equipment ecosystems was a tough decision.

Long story short: I wasn’t disappointed and the SA works perfectly fine. I don’t want to write a lengthily blog about it. One of the first experiments was connecting my GPS disciplined oscillator to the signal generator and spectrum analyzer simultaneously in order to provide the same external reference for both instruments and checking if the frequency (1.5 GHz) and the amplitude  (-35 dBm) are accurate. Acquiring measurements was super easy and the operation of the SA is very straight-forward.

# Documentation of Measurements

I would consider the somewhat cumbersome recording of readings as a minor disadvantage of this SA. Taking a photograph of the display may be “quick and dirty” but you have to deal with bad image quality due to reflections, visible RGB pixels and picture alignment. It is possible to take screenshots in bitmap format (BMP) but one needs a special type of a Memory Card (basically a PCMCIA or PC Card) in order to save the screenshots on an external storage. That’s really unfortunate but measuring instruments of that era were either equipped by a floppy disk or Memory Carc. I was always afraid of damaging the fragile pins while pushing the PCMCIA card in its slot although it is rated for 10k mating cycles. The MS2661N type SA even has a 75 Ohm composite out – it’s possible to record video stills in the NTSC format. However, there are two elegant methods which I would like to show how to transfer the readout from the instrument to the personal computer (PC) by modern means.

## Method 1: Sending a Hard Copy from SA to a PC

Back in the days, the measurement results such as frequency spectra would be printed on a piece of paper as a part of the documentation. A device called printer or plotter was needed and the process was called “hard copy”. The difference between a printer and plotter is how the drawing is generated: while the printer generates text and images line by line, a plotter can draw vectors in a X-Y-coordinate system. HP developed its own printer control language back in 1977 for this purpose – the HP-GL or Hewlett-Packard Graphics Language. HP-GL consists of a set of commands like PU (pen up), PD (pen down), PAxx,yy (plot absoute) and PRxx,yy (plot relative) in order to control a plotter, which is basically an electro-mechanically actuated pen. The commands are transmitted in plain ASCII via GPIB or RS-232C interfaces. If we were somehow able to capture the HP-GL ASCII code, it should be possible to generate a lossless vector graphics instead of a lossy bitmap.

### Hardware Requirements

Besides the already mentioned spectrum analyzer one needs either a GPIB/USB or GPIB/Ethernet adapter. I have tested it successfully with a National Instruments GPIB-ENET/100 on a Windows 10 machine with NI 488.2 v17.6 drivers. It should also work with a NI GPIB-USB-HS+ (Chinese clone) adapter.

### Software Requirements

I was looking for a quick solution how to acquire hard copies. Thanks to einball on a certain Discord channel 😉 for showing me the KE5FX 7470.EXE HP-GL/2 Plotter Emulator. John Miles, KE5FX, already wrote a software back in 2001 which does emulate a HP 7470A pen plotter. The 7470.EXE is still maintained by John and supports popular spectrum analyzers from HP and Tektronix. His software is able to fetch the HP-GL ASCII via GPIB and render the hard copy image on the screen. The image may be saved in a bitmap format (BMP, TIFF, GIF) or in a vector format (PLT, HGL). I have tested John’s software with Anritsu MS2661N and it worked perfectly fine. I suppose this could work on similar Anritsu spectrum analyzer models, such as MS2661C.

### Setting up the Spectrum Analyzer

Here is a brief summary how to obtain a hard copy from an Anritsu MS2661N spectrum analyzer:

• Connect the spectrum analyzer to the GPIB adapter and boot up the device
• Go to the Interface menu and use settings as followed → GPIB My Address: 1, Connect to Controller: NONE, Connect to Prt/Plt: GPIB, Connect to Peripheral: NONE
The SA wants to send its data via GPIB to a plotter. It’s important to disable the “Connect to Controller” option, otherwise it won’t be possible to select GPIB as “Connect to Prt/Plt”. The GPIB address is set arbitrarily to 1
• Go to Copy Cont menu (Page 1) → Select Plotter
• Copy Cont menu (Page 2) → Plotter Setup → Select following options: HP-GL, Paper Size: A4 Full Size, Location: Auto, Item: AllPlotter Address: 2
It’s important to set the “Plotter Address” value to a different number than the “GPIB My Address“. If both addresses share the same number, the hard copy will result in a timeout error

Install John’s 7470.EXE software and start the HP 7470A Emulator. There is no need to change the settings of the GPIB controller, it works out of the box. Click on GPIBPlotter addressable at 2. The selected address in 7470.EXE should be identical as the previously set Plotter Address. In order to obtain a hard copy, press the button w and the 7470.EXE should display a message like shown in the screenshot below. Once you press the Copy button on the spectrum analyzer, a data transfer progress should be visible. It takes about 10-15 seconds to transfer the data (approx 7-10 kb) from the SA to the PC. Once it’s complete, an image of the current frequency spectrum is shown on the display. That’s it.

### Creating Publication-Quality Vector Images

At this point, it’s possible to save the acquired hard copy in a bitmap image format. If one needs a publication quality images – which should be free of compression artifacts – one should save the images in a vector format such as PLT/HPGL. This workflow proved to be a little bit inconvenient but it’s perfectly doable. Save the hard copy as .PLT and open the image in a HP-GL supported viewer. John suggests few of them on his website – I’ve tried CERN’s HP-GL viewer. It’s distributed free of charge and still maintained by the developers. Download their viewer and load the PLT-image. If the colors seem wrong, there is a setting where you can change the pen colors. Once done, it’s possible to export the PLT image as PostScript (PS) or Encapsulated PostScript (EPS) or print as PDF. EPS files can be embedded in LaTeX documents or can be imported in a vector graphics editor such as Inkscape.

The results turned out to be really good. Especially the vector images are crisp and sharp. One can zoom in without any image quality losses. The printouts on my laser printer are perfect. A little downside would be few breaks in the workflow: one has to use three different applications in order to obtain, convert and process the images. But it’s worth it 😉

## Method 2: Readout Data via pyvisa and Plot it via Matplotlib

A different method to plot the frequency spectra would be by downloading the acquired raw data via GPIB and plot it directly. This is exactly what I’ve done. I’ll share the Python code down below. It’s possible to refine the plot by automating more stuff: one can generate annotations directly from queried instrument settings. Just put enough time in it and you’ll get superb results. The plotted image can be saved directly in a Scalable Vector Graphics (SVG) or any supported bitmap/compressed format.

# -*- coding: utf-8 -*-
"""
Created on Tue Jan  3 16:45:39 2023

@author: DH7DN
"""
import numpy as np
import pyvisa
import pandas as pd
import matplotlib.pyplot as plt

#%% Open the pyvisa Resource Manager
rm = pyvisa.ResourceManager()
print(rm.list_resources())

#%% Create the Spectrum Analyzer object for Anritsu MS2661N at GPIB address 13
sa = rm.open_resource('GPIB0::13::INSTR')

# Print the *IDN? query
print(sa.query('*IDN?'))

#%% Take a measurement
# Set frequency mode to CENTER-SPAN
sa.write('FRQ 0')

# Set the center frequency in Hz
cf = 1.5E9
sa.write('CF ' + str(cf) + ' HZ')

# Set span in Hz
span = 10000
sa.write('SP ' + str(span) + ' HZ')

# Take a frequency sweep (TS)
sa.write('TS')

# Select ASCII DATA with 'BIN 0' according to Programming Manual
print(sa.write('BIN 0'))

# Create a Python pandas Series
data = pd.Series([], dtype=object)

# Fetch data, convert string to float, print the power level values
for i in np.arange(501):
data[i] = float(sa.query('XMA? ' + str(i) + ',1')) * 0.01
print(data[i])

#%% Plot the results
# Generate the frequency values for the x-axis
f = np.linspace(cf-span/2, cf+span/2, 501)

# Plot the results, set a title and label the axes
plt.plot(f, data)
plt.xlabel('f in Hz')
plt.ylabel('Power Level in dBm')
plt.title('CF: 1.5 GHz, Span: 10.0 kHz, RBW: 100 Hz, VBW: 100 Hz, \n Peak at 1.5 GHz and -35.85 dBm')
plt.grid(axis='both')
plt.minorticks_on()
plt.show()

Few things to consider when using Python to obtain data from the spectrum analyzer:

• Anritsu MS2661N acquires only 501 data points per sweep
• The frequency axis values need to be generated manually. I used numpy‘s linspace method. It was a bit tricky because you one has to change the generation of frequency step values depending on whether parameters “Center Frequency & Span” or “Start/Stop Frequency” are used
• Fetching the data takes quite some time (approx. 30 seconds). This is due to the fact that every single data point needs to be queried with the XMA? command in a for-loop. This is at least how it’s done in an example from Anritsu’s Programming Manual. I haven’t figured out yet how to fetch a block of data

# Summary and Conclusion

I was clearly impressed how easy it was to obtain good quality frequency spectra images from a 20 year old instrument. I’ll refine the workflows and do further testing in Python. It should be possible to do all of this “automagically” via one little Python script. So far I’m really happy with the results where I don’t have to rely on smartphone pictures anymore. Thanks to einball for his help (basically googling for me) and to John (KE5FX) for writing his plotter emulator which helped me a lot to obtain hard copies from my SA. That’s it for today! Happy measurements! 😉

73 de Denis, DH7DN

## Happy New Year 2023

Hi everyone,

let’s just forget 2022 and move on. Covid, inflation, wars and conflicts – it doesn’t get better. Nevertheless, I had many positive moments in 2022, too! I had some lucky test equipment acquisitions, I managed to sell unused test equipment and free up some space, did some Python and GPIB programming, did a bike tour, met lots of nice and interesting people throughout the year, finally published some decent blog posts and successfully reduced my weight from 132.9 kg (290 lbs) to 115.5 kg (254 lbs) in 365 days.

There is a lot more to do in 2023 and I’ll try to keep up with publishing interesting blog posts. My new year resolution in 2023? No new resolutions! They are useless – at least for me.  I have a bunch of interesting projects on my list and I hope they will happen some day… perhaps this year. If you take on too much, you will end up doing nothing at all.

Happy New Year 2023, stay positive, safe and sound 🙂

73 de DH7DN

# Introduction

Today’s modern technology is full of sensors. Sensing temperature, motion, force, humidity, sound, electricity, radiation – basically every imaginable physical quantity – necessary to measure and understand our environment. This is usually done with so-called transducers (sometimes abbreviated as Xducers) which convert the measured physical quantity (e. g. temperature or any kind of a signal) into a different physical quantity (e. g. resistance, voltage) or any other type of signal. In many cases, the transducer converts the measured physical quantity into an electrical signal which is used as an input to a digital voltmeter or an analog-to-digital converter. The conversion into electrical quantities is highly practical in order to be able to connect the transducer with our measurement instruments or our microcontrollers (which are basically small computers). A special kind of transducers I want to talk about here today are piezoelectric accelerometers.

Just recently I’ve acquired a huge batch of piezoelectric accelerometers in an unknown condition which need to be tested for functionality. The goal of this project is to develop a small prototype calibration device in order to be able to calibrate piezoelectric accelerometers by comparison method.

# Piezoelectric Accelerometers

## Working Principle

Piezoelectric (PE) accelerometers are basically “acceleration-to-charge” transducers. They rely on the piezoelectric effect which – in simple words – converts mechanical energy into electrical energy. A piezoelectric transducer consists of a piezoelectric material (e. g. quartz, lithium niobate) and a small seismic mass.

As soon as dynamic forces act on the spring-mass-system along the acceleration-sensitive axis, mechanical stress is introduced on the piezoelectric material which is mounted inside of the accelerometer housing (see Figure 2). The resulting deformation of the PE material causes a polarization which in return generates a change in surface charge density. The change in surface charge density is directly proportional to the mechanical stresses (e. g. force or pressure) and therefore proportional to the acting force or acceleration (if you remember the Newton’s 2nd law $$F = ma \longrightarrow a = F/m$$). The resulting change in surface charge density can be detected, amplified and converted into a measurable voltage with a proper signal conditioner or so-called “charge amplifier”. For simplicity’s sake I’ll refer to “charges generated by the accelerometer” instead of “polarization and change in surface charge density of the PE material”.

## Harmonic Oscillator

From a mechanics point of view, the basic construction of a PE accelerometer can be approximated as a spring-mass-system with a low dampening as shown in Figure 3.

The harmonic oscillator is a very basic physical model of a spring-mass-dampener system. The huge advantage of this model is its simplicity: the harmonic oscillator equations contain the Newton’s laws of motion ($$F = ma$$) and Hooke’s law ($$F = kx$$) which can be solved analytically using so-called differential equations. While I’m skipping the mathematics part here and just want to mention that in reality things are more complex, some of the results of the differential equation for a driven harmonic oscillator are shown in Figure 3. Applying oscillations on a spring-mass-dampener system leads to the curves (Bode plots) showed in Figure 3. One of critical parameters of a harmonic oscillator is the natural frequency $$\omega_\mathrm{n}$$. It’s a particular frequency where the spring-mass-system is oscillated (or “shaken”) in resonance, e. g. the mechanical system responses with very large displacement amplitudes while being excited by very small amplitudes. Resonance phenomena can be experienced in everyday situations like music instruments, swinging bridges, vibrations in cars driving at certain speeds, tuning forks etc.

For example, sinusoidal excitations of an accelerometer at its resonance frequency can lead to damage or change of its specified properties, e. g. sensitivity. A high resonance frequency is achieved by using stiff material (spring constant $$k$$ should be high) and small seismic mass. In case of an accelerometer, the resonance frequency should be as high as possible, usually in the order of 30…50 kHz for high-frequency or shock measurements. Brüel & Kjaer suggests in [1] that the typical useable frequency range of an accelerometer is specified to approx 30% of the natural frequency.

The equation $$\omega_\mathrm{n} = \sqrt{k/m}$$ for the undamped natural frequency suggests that using no seismic mass would lead theoretically to an infinite natural frequency! In reality, we need a small seismic mass – it has to be just big enough so it can compress or tension our spring (which is basically the PE material) through its inertia. We need to create mechanical stresses on the PE material in order to generate our precious charges. Basically a larger seismic mass leads to a larger signal output which is exploited in the low-frequency range ($$f \ll 10 ~ \mathrm{Hz}$$) and in seismometers.

## Measuring Accelerations with a PE Accelerometer

The amounts of charge generated by an PE accelerometer are very minuscule. In order to get a measureable amount of charge, the PE elements are stacked in parallel as seen in Fig. 1. We’re talking about tens to hundreds of femto-Coulombs (fC) per m/s² of acceleration up to few pico-Coulombs (or pC) per m/s². Typical values are in the order of few pC where $$1~\mathrm{pC} = 10^{-12}~\mathrm{A} \cdot \mathrm{s}$$. Just imagine charging a small capacitor with a capacitance of C = 100 pF and a voltage of U = 0.1 V and you will get according to the capacitor equation $$Q = C \cdot U$$ a value of $$Q = 10~\mathrm{pC}$$. High-intensity accelerations in the order of 1 … 100 km/s² – which are found in crash or shock testing – may generate few nano-Coulombs of charge. Measuring such minuscule quantities requires a somewhat specialized test equipment: ultra low noise coaxial cables with limited length and a signal conditioner for impedance matching, signal amplification and filtering.

The use of PE accelerometers is pretty much straight-forward. The accelerometer needs to be attached to a vibration source which can be virtually anything: electric motor, mountain bike, structure of a bridge, washing machine, car armature, rocket engine etc. In order to perform vibration measurements properly, one has to consider many experimental issues such as mounting, temperature influences, cable fixture, grounding loops, amplifier settings and few more. Informations on this topic can be gathered from instruction manuals and application notes from different manufacturers. In order to perform accurate measurements, the instruments needs to be calibrated.

## Calibration of a Piezoelectric Accelerometer

As soon as one buys (very expensive) acceleration measurement equipment, the new instruments will be factory calibrated and the manufacturer will provide calibration certificates to the customer. A calibration certificate contains important information how to establish the relationship between the input quantity (acceleration) and the output quantity (charge or voltage). This information is usually called sensitivity of an accelerometer. The sensitivity of an accelerometer is determined during a process called calibration. According to JCGM:200 (2012), the International Vocabulary in Metrology (VIM), a calibration is

[…] operation that, under specified conditions, in a first step, establishes a relation between the quantity values with measurement uncertainties provided by measurement standards and corresponding indications with associated measurement uncertainties and, in a second step, uses this information to establish a relation for obtaining a measurement result from an indication.

In other words: a calibration is a comparison between input and output quantities of any kind. The input quantity is provided by a well-known standard, the output quantity is provided by a device under test (DUT). In case of an PE accelerometer, the input quantity is an acceleration, the output quantity is charge (or voltage if using a conditioning amplifier).

Unfortunately – when buying surplus stuff – there is always a risk of getting either a defective or incomplete unit. The provided calibration certificates may be either wrong or got lost. Some PE accelerometers are well over 50 years old and may have drifted over time. For my purposes, I’ll have to skip the “measurement uncertainties” part for now because I want to test the accelerometers for their qualitative condition and functionality. I’ll return to the metrology part in a future project.

## Description of the Calibration

The calibration process is shown in a block diagram (Figure 5). In order to generate an acceleration $$a(t)$$, we need to set our Accelerometer Standard (REF) and the Device Under Test (DUT) in an oscillating motion. This is usually done with an electrodynamic exciter – a technical term for “shaker” or “loudspeaker”. The working principle of an electrodynamic exciter is identical to the principle of the well-known loudspeaker. We’re generating a low distortion sinusoidal signal with a function generator which is fed into a power amplifier. The amplified signal drives the coil of the moving part inside of the exciter which in return creates the oscillating motion. Amplitude and frequency of acceleration are set by the function generator, which are typically in the range from 10 Hz to 10 kHz and 1 m/s² to 200 m/s². The frequency and amplitude ranges depend strongly on the construction of the electrodynamic shaker and the total weight of the DUT and REF accelerometers.

The generated motion is applied to both accelerometers, which are physically connected to each other. In this case, the DUT is mounted or screwed on the REF accelerometer in so-called back-to-back or piggy-back configuration. Our goal is now to establish the relationship between the input and output quantities by calculating the acceleration and measuring the output voltage of the DUT measuring chain. Basically, the charge sensitivity $$S_\mathrm{qa,DUT}$$ of the DUT can be calculated as follows:

$$S_\mathrm{qa,DUT} = \cfrac{q_\mathrm{DUT}}{a} = \cfrac{u_\mathrm{DUT}}{u_\mathrm{REF}} \cdot S_\mathrm{qa,REF} \cdot \cfrac{G_\mathrm{uq,REF}}{G_\mathrm{uq,DUT}}$$

The shown equation might look scary and complicated but it’s pretty straightforward: we’re measuring the output voltages of both measuring chains and multiplying their ratio with the  charge sensitivity of our reference accelerometer ($$S_\mathrm{qa,REF}$$). Afterwards we’re multiplying the resulting expression with the ratio of transfer functions of our charge amplifiers ($$G_\mathrm{uq}$$), which have to be determined by a different type of calibration. For the sake of completeness, I would like to mention that the sensitivities and transfer functions in general are complex values (e. g. $$\underline{S}_\mathrm{qa} = |S_\mathrm{qa}| \cdot \exp{(\mathrm{j}\omega t + \varphi_\mathrm{qa}})$$) and we’re dealing with the magnitude $$|S_\mathrm{qa}|$$ of the complex transfer function. I’ll try to cover this in a future blog post.

# Experimental Setup

Since we need to perform the measurements over a wide set of frequencies, it is highly recommended to automate the task as much as possible. The instrument control, data acquisition and data analysis can be done with a PC. I’m using Python 3.9 with pyvisa, pandas and numpy on a Windows 10 machine. My accelerometer reference standard is a Kistler 8076K piezoelectric back-to-back type accelerometer. For the purpose of this experiment, I’ve tested two different PE accelerometers: Brüel & Kjaer 4371 and Endevco 2276, which are so-called “single-ended” accelerometers. Single-ended type accelerometers can be mounted on the top of a back-to-back type accelerometer and therefore calibrated by comparison method. The vibrations are generated by a Brüel & Kjaer 4809 electrodynamic exciter which is connected to a Brüel & Kjaer Type 2706 Power Amplifier and an Agilent 33250A frequency generator. I’ve used two charge amplifiers  for the accelerometers, basically Brüel & Kjaer Types 2650 (REF) and 2635 (DUT). They were connected to HP 34401A digital multimeters. AC voltage measurements were performed in “ACV mode” which outputs the root mean square (RMS) voltage of the respective measurement chain signal output. An oscilloscope can be used to monitor the output waveforms in order to detect unwanted noise and distortions. This is a small downside of RMS measurements: the DC offsets and noise fully contribute to the measurement result.

Setting up the devices wasn’t very difficult. The electrodynamic exciter needs a stable and massive base along with an adequate vibration isolation. If the vibration isolation is neglected, the vibrations are coupled into the desk and into the building. Using hard foam between the desk and granite block proved being very inexpensive and efficient. The support for low noise cables are also improvised. The cable mounting is a major source of experimental errors. Due to the triboelectric effect, a bending or vibrating coaxial cable also generates charges which are superimposing the measured accelerometer signal. In short words: the reference accelerometer is measuring a slightly higher acceleration than expected. Therefore the sensitivity drops due to $$S = q/a$$. This can be seen in the measurement results at frequencies below 25 Hz. At higher frequencies (e. g. > 25 Hz) the displacement amplitude of the vibration becomes very small and the triboelectric effect becomes negligible. I’ve used a torque wrench with 2.0 Nm and the contact surfaces were slightly lubricated in order to prevent deviations at higher frequencies (>5 kHz).

# Measurement Results

The calibration result can be seen on the left hand side in Figure 6. The top curve represents the charge sensitivity of the DUT plotted vs. excitation frequency. There are some deviations in the frequency response which are really annoying but I’m really satisfied with the overall result. Calculations of the relative deviation of the charge sensitivity at a reference frequency of 160 Hz can be compared with the data provided by the manufacturer. A 5% deviation at 6 kHz is in a good agreement with the specifications! My measurements show even higher deviations at frequencies f > 6 kHz so there must be some kind of systematic error which has to be investigated. Nevertheless, the measurement is automated and it takes approx. 5 minutes for a “sweep” of 31 discrete frequencies in the range from 10 Hz to 10 kHz. I’ve used standardized frequencies which are known as Third Octave Series according to ISO 266. The bottom graph shows the acceleration amplitude over the frequency range. I’m ramping up slowly in order to minimize distortions. A limit of 20 m/s² is set for noise reasons in my apartment – the generated sine tones can be very annoying and I don’t want to wear ear protection all the time.

# Summary and Conclusion

This project clearly is a success! It took much time and effort in order to get the experiments straight and to automate the measurements. I was able to perform a calibration of a piezoelectric accelerometer with decent quality equipment. The results are “not bad” although I see much room for future improvements. I’ll have to improve the measuring chains and eliminate noise sources. I’d like to improve the Python code and generate a Graphical User Interface (GUI) for calibration purposes. Playing with a HP 3562A Dynamic Signal Analyzer was also very fun! I was able to dump the FFT measurement data (Thanks to Delrin for his Python hint!) via GPIB and didn’t rely on photographs of the display. A little downside of this instrument is its loudness and electricity consumption in the order of 400 W. I’ll certainly use the signal snalyzer during the winter months in order to heat my apartment 😉 I’m literally scratching on the surface in the fields of vibration measurements and the future will bring more interesting projects. The ultimate goal is to build a laser interferometer as an acceleration reference standard and to estimate the uncertainties of the built calibration devices.

# References

[1] Serridge and Licht, Piezoelectric Accelerometer and Vibration Preamplifier Handbook, Brüel & Kjaer Naerum, Denmark, 1987
[2] Methods for the calibration of vibration and shock transducers – Part 21: Vibration calibration by comparison to a reference transducer, ISO 16063-21:2003
[3] Richtlinie DKD-R 3-1, Blatt 3 Kalibrierung von Beschleunigungsmessgeräten nach dem Vergleichsverfahren – Sinus- und Multisinus-Anregung, Ausgabe 05/2020, Revision 0, Physikalisch-Technische Bundesanstalt, Braunschweig und Berlin. DOI: 10.7795/550.20200527

# Introduction

About a year ago, I bought an Agilent E4432B ESG-D Series Signal Generator on eBay. Those signal generators are used in the area of analog and digital wireless communications system testing. The frequency range of my model goes from 250 kHz up to 3.0 GHz, the maximum radio frequency (RF) power output into a 50 Ω termination is in the range from +17 dBm  to -136 dBm. This instrument will be useful in a different project of mine where I need a good spectral quality sinewave at frequencies of about 80 MHz for mixing RF signals. Another very useful feature is the ability to modulate the carrier frequency (AM or FM) which can be demodulated by a Software Defined Radio (SDR). It can be automated via GPIB and synchronized to a 10 MHz reference. Due to its high broad frequency coverage and level accuracy, it would be useful to test my (currently broken) spectrum analyzers, too. Unfortunately, my eBay score had a flaw which I want to talk about.

# Problems with the Step Attenuator

The unit I received is about 15-20 years old and had already approx. 61000 operating hours (equals to ~7 years of 24/7 operation) and 770 power cycles. It was overall in a good shape although one of the external input BNC jacks was loose and had a slightly damaged thread. After turning the signal generator on, I ran the diagnosis and no faults or errors occurred. I tried to set up an RF signal output at different frequencies and I noticed a very uncommon behavior: there was a sinewave at set frequencies in the range from 250 kHz up to 400 MHz (max. analog bandwidth of my oscilloscope) but the signal wasn’t present at certain amplitude levels. I checked the different amplitude levels noticed some kind of a strange pattern (see Table 1).

 Amplitude (dBm) -60 -55 -50 -45 -40 -35 -30 -25 -20 -15 -10 -5 0 Signal present? ? + + – – + + – – + + – –

The signal at -60 dBm and lower amplitudes was too weak for my oscilloscope to detect but the pattern indicated a problem. Luckily I’ve seen a repair video by Shahriar of TheSignalPath  some time before acquiring this signal generator where he had a similar unit on the healing bench with identical symptoms. This kind of problem seems to occur in HP/Agilent test equipment (e. g. Types HP 8648A or E4400 Series signal generators or HP spectrum analyzers) where mechanical attenuators are actuated by solenoids. The detailed construction and working principle of the mechanical step attenuator is well explained in Shahriar’s video. There is also a very good repair video from YouTube user idpromnut where I got many useful repair tips.

While changing the attenuation range in steps of 5/10/20/40/60 dBm, the corresponding solenoid exerts a force on very small rubber O-rings which are used as an interface between a metal plate and plastic plungers (see photos). The plastic plungers are connected to metal strips which are pushed onto resistor traces in order to make electrical contact with a corresponding attenuation resistor. Over time, the O-rings lose their flexibility, become brittle and are either crushed by the actuating mechanics or just fall off their spot. This impairs the ability to switch the attenuator levels properly. Checking the instrument diagnosis revealed over 660k attenuator cycles which supports the faulty O-ring assumption. Luckily, this fault is  easily repairable to a certain degree without having to disassemble the delicate RF parts. Buying a new attenuator wasn’t an option because the current prices on eBay are in the order of $500 per attenuator. The signal generator is worth$700 – $1200 depending on measurement capabilities/installed options so replacing the attenuator would hurt the hobby budget. In order to repair the attenuator, the O-rings have either to be replaced or glued with a flexible epoxy according to TheSignalPath video. I’ve chosen the replacement method since I don’t have proper epoxy glue. However, idpromnut showed in his attenuator repair video the replacement procedure of O-rings on a similar unit. He suggested to buy “Wrist watch clock Crown-O-Rings” on eBay from RUIHUA No. CO-12. It’s a set of O-rings in different sizes used by watchmakers. I ordered mine from XIAOJIA for approx 9 EUR. The replacement O-ring size is “2.0mm 1.0×0.5” (2.0 mm outer diameter, 1.0 mm inner diameter and thickness 0.5 mm). # Repair Attempt Fast forward to August 2022. Back in June 2021 I didn’t have the right lab equipment and spare time. It wasn’t a high priority repair so more than a year passed by. Meanwhile I acquired enough lab equipment to start a repair attempt. My basic tools were: screwdriver with hex/torx bits, flashlight, magnification glass, tweezers and a 1 Nm torque wrench for the SMD connectors. I was a bit paranoid concerning electrostatic-sensitive devices (ESD) so I used an ESD mat and ESD-compliant tools during repair. Wearing latex gloves proved to be useful because of ESD matters and avoiding contamination of sensitive parts and metal surfaces with grease or fingerprints. I had access to other tools like torque screwdriver and a stereo microscope for additional delicate work which weren’t necessary for this repair. I cleaned my lab prior to this repair attempt in order to remove as much dust as possible to mitigate the contamination of the (ESD-)sensitive electronics. I’ll post some pictures showing the repair steps. I’ve uploaded many more pictures into my Piwigo Photo Gallery. Check them out if you want to see more high-resolution images. At this point, the visual inspection showed one attenuator where O-rings were missing. Other stages were in a decent working condition. However, the impending failure was clearly visible. I decided not to disassemble the whole unit and try to insert new O-rings. This had to be done by fiddling around with tweezers and trial-and-error. It took me about two hours to insert two O-rings. In the end, they fitted perfectly. # Summary and Conclusion I would consider this repair as “easy” thanks to TheSignalPath and idpromnut‘s detailled repair videos. I couldn’t do it by myself because I’m not an RF test equipment expert and I haven’t done such repairs before. It’s just another hobby and I’m relying on some help from the outside. However, this repair was very rewarding and only temporary. The visual inspection showed the impending failure of the remaining O-rings. Servicing this unit will surely be necessary somewhere in the future. I’ll have to replace every O-ring to be sure they won’t fail again for the next few years. Also one has to consider post-repair procedures as the service manual suggests. I haven’t checked the amplitude over the frequency range because of limited measurement capabilities. I just recently acquired a RF power meter which has to be calibrated first. My spectrum analyzers only cover the frequency range up to 1…1.8 GHz and aren’t very trustworthy. I’ll also have to check the post-repair performance of the step attenuator with a Vector Network Analyzer (e. g. NanoVNA V2 Plus4) in order to be sure the unit is working properly. ## Bicycle Tour 2022 I really love bicycle touring. My last bike tour was back in July 2019 and took like 16 days and approx. 1000 km. The past two years have been very difficult for travelling due to COVID-19 restrictions. The camping sites either closed or the COVID-19 rules were very restrictive and differed from site to site. Travelling in 2020 and 2021 was really difficult and risky. Luckily, the situation changed in 2022 and due to vaccinations, declining COVID-19 pandemic situation and reopening of the tourism and travelling sectors in Germany, it was possible to travel again. I took the chance and organized a little bike tour during my vacation. My original plan was something like this: travel by bike and tent for 6 days from Hanau to Braunschweig, Germany. It was coupled with a visit to my relatives in Hanau – a mid-sized town near Frankfurt am Main in the state of Hesse. The daily tour distance should be something like 60-80 km and the total distance from Hanau to Braunschweig should be approx. 400 km. Planning the route by OpenCycleMap.org was pretty easy but I missed two important factors: the terrain and total weight of me and my bike. While the terrain in Lower Saxony is mostly flat and very easy to manage – this isn’t the case in the hills of Hesse, especially when one has a 40 kg packed travelling bike and approx. 120 kg of muscles fat riding the bike. So yeah… I kinda underestimated the effort which was punished later 😉 However, I was preparing this tour for about a week. Riding approx. 30 km per day helped to build condition and to get used to sitting on a bike for 2+ hours. # Day 1: Hanau – Gelnhausen – Schlüchtern The tour started on the hottest days of the month. This wasn’t planned at all. The temperatures were around 37-39 °C and there was not even one cloud from early morning until sunset. This was definitely not my cycling weather. Cycling the first 40 km was easy until the heat drained my powers. The last 20 km were exhausting but I managed to reach Schlüchtern. My first camping site was about 3 km outside of Schlüchtern – “piece of cake” I thought. The camping site was on a hill of approx 250 m height. My mistake was using the Google Maps guide. The suggested route was closed and I spent 2 hours in the woods and hills pushing my 40 kg bike at 12% upward slopes. I finally managed to reach the camping site Hutten-Heiligenborn at 8 pm and was wrecked. The camping site was very nice and I was welcomed by the site manager. Tent, shower, dinner, sleep, RIP. # Day 2: Schlüchtern – Fulda – Schlitz The cycle route downhill was really nice! I was able to go at 40-50 km/h for about 7 km. 26 km later, I reached the city of Fulda. Fulda was very inviting due to very good cycling roads. I visited the central train station involuntarily because I got lost few times due to bad road signs. Nevertheless, the temperatures rose higher and higher up to 39 °C and my performance dropped steadily. In the village of Kämmerzell I got lost again but this was really bad. The route got me into a 7 km lasting agony of steep hills (remember? 12% upward slope at 39 °C and 160 kg of total mass?) which lasted for like 3 hours. Luckily, I was pushing my bike on forest roads where most places were in the shadows at 35ish °C but that’s it. This unintentional route killed my schedule and I was unable to reach my planned camping grounds. My provisions have been spoiled by the hot weather and I had to resuppy it on the next day. Luckily, there was a small camping site in the city of Schlitz (where I got lost again, thanks Google Maps). Later at night, there was a thunderstorm but this was no problem for my Hilleberg Unna tent! # Day 3: Schlitz – Bad Hersfeld – Melsungen I think this was the best cycling day I had in a long time. Due to the thunderstorm and colder weather, the temperatures dropped significantly from 39 °C to a cloudy 23 °C. This was like heaven for cycling – the heatwave was gone and the temperatures were ideal for cycling. My performance on this day was OK: my muscles and my butt didn’t hurt much and I was able to cycle 95 km total. I still wasn’t able to catch up to my schedule because I was like 40 km behind. When reaching the city of Melsungen at 7 pm, I thought I had enough power to make it the next 40 km but as soon as I saw the next camping site, I knew it was time to get some rest. Cycling at night in unknown terrain can be dangerous (you get lost very easily, bad sight during night). So I stopped on a small camping site near Melsungen and it wasn’t a bad decision at all. # Day 4: Melsungen – Kassel – Hann. Münden – Hemeln Oh yeah, I overdid it on Day 3. The daily goal was set to 90-100 km but I managed to get only 78 km total. The weather was very good (cloudy, 25 °C) and I was progressing very well until I reached the city of Kassel. Here I got lost multiple times due to construction sites and spend like 2-3 hours cycling through Kassel. I stopped here and there to take photographs but wasn’t able to do a sightseeing tour. Kassel was very stressful because I had to take routes on busy roads for a while until I found the correct cycling route. This set me further back in my schedule. The route from Kassel to Hannoversch Münden was very nice and relaxing. I met another elder cyclist which cycled with me for about 12 km. Having company was nice because we were very fast and could talk about the usual stuff (small talk). Hann. Münden was a very nice city – this is the place where the rivers Fulda and Werra combine into the river Weser. I wish I could have stayed there longer. However, I left Hann. Münden at 2:30 pm and at about 3:30 pm, my performance started to decline due to muscle and rear pain. I didn’t want to ignore the pain signs and I visited a camping site in a village called Hemeln. Good decision (as usual). I needed some rest and as soon as my tent was up, I went inside and slept for like 2 hours. Then shower, dinner, sleep 8h. # Day 5: Hemeln – Höxter – Holzminden – Stadtoldendorf – Braunschweig (by train) Unfortunately the final cycling day. I got up very early in the morning and left the camping site at about 8 am. All the muscle pain was gone and I was able to cycle at a fast pace again. The weather wasn’t very bad and this got me excited because I felt like I could do 100+ km on this day. My goal was Seesen, a small town near the Harz mountains. The distance was calculated at about 140 km. Since 100 km are no problem, an extra 40 km should be possible, too. Wrong! The first 60 km weren’t bad at all. My speed was OK but the temperatures started to rise again. By noon, the temperature was at 28-30 °C and my rear started to hurt every few kilometers. So I had to push the bike for a while until the pain went away but this set me back in my schedule. After visiting few cities such as Bad Karlshafen, Höxter and Holzminden, I left the Weser cycling route and set the route towards east (R1). At 80 km distance, I was really exhausted and had to take breaks every few kilometers. I reached a place called Stadtoldendorf at 4 pm and still had to cycle 50 km to Seesen. At 13 km/h, this would have taken 4 hours at least. I resupplied my water and food in Stadtoldendorf and I got lost again due to a major construction site. Unfortunately, Google Maps calculated a wrong route directing me into steep hills and I gave up. I re-routed Google Maps to the next train station and started the adventure back home to Braunschweig by train. Luckily, I was able to travel with my 9-EUR-Ticket and the trains were not full at the time. After spending 3 hours in regional trains, I was able to reach home safely. My daily distance was 110.95 km, my new personal record (my highest was at 110.68 km in the year 2019). I must admit, I had to use Ibuprofen due to muscle pain. # Summary It was a very interesting and adventurous cycling tour 2022 (as usual). I’ve seen many wonderful landscapes and places, cycled about 425 km total. The weather was good and bad and I got lost many times (as always). I learned a bit about planning routes and learned a lot about my cycling performance. Using petroleum jelly for skin lubrication was a winner. I packed the wrong stuff which I never used on tour (gas stove, blanket, accessories, food) but which added to the total weight. My provisions got spoiled by the hot weather so this is an important issue to consider for future routes. I hope to reduce my body weight by winter so I’ll be able to do another cycling tour at the North Sea. I’ve done this before and it was one of the best cycling tours I had so far. I would recommend everyone to participate in such cycling tours. If you’re not a cyclist, go hiking instead! It’s a challenge to travel alone over the course of many days. For me it’s important to realize that travelling from A to B takes time and effort. Sure, one can drive into holidays or fly by airplane with little to none effort. This behavior has two negative effects: people just don’t realize how hard it is to move things from A to B and we take many modern things for granted. Cars and airplanes may be the foundation stone of our modern civilization but our civilization will have to change significantly in the next few decades or we’ll have really difficult times here on earth (climate *cough* change). ## Tektronix TDS 754D, Four Channel Digital Phosphor Oscilloscope # Introduction Over the past two years, I have acquired a decent amount of Tektronix analog oscilloscopes. I would say “decent amount” because having 10+ oscilloscopes is not that much compared to other Tektronix enthusiasts. Nevertheless, my colleagues already started questioning my sanity. Different topic. I really like Tek analog oscilloscopes and I love to work with them. However, doing everyday measurements in analog has its downsides in terms of comfort and workflows. It was about time to go digital! # Analog vs Digital Do you want to do a “quick” measurement and document it? Sure: turn on the oscilloscope, set the ranges/triggering and observe the electron beam trace! Experienced users can set up the measurement in seconds to less than a minute. However, when it comes down to taking quantitative measurements, this process may take a while. If the values of the amplitude, phase or frequency of a signal are not of importance, e. g. one does qualitative measurements where just the shape of the signal is of interest (sinewave, squarewave, noise), the measurement can be done in a matter of seconds. However, if quantitative measurements are of importance (amplitude, phase, frequency, risetime), the average user will spend a lot of time counting the marks on the graticule which can be prone to errors, too. In order to document the measurements, one has to take photographs of the screen. Unfortunately, some analog oscilloscopes have no measurement annotations on the screen. The user has to take care of this – it can be a huge trap for young players. I’ve been there before and it wasn’t easy figuring out why I have 13 pictures of oscillograph curves but only 10-ish lab notes documenting the oscilloscope settings… Which setting did I use for Channel 1 and Channel 2? Why are the images blurry? How about those annoying reflections on the screen? Another downside of an analog oscilloscope would be of a “first-world-problem” type: at very low frequencies (in the range of few Hz), it’s very difficult to view a complete image of a single waveform. The electron beam sweeps too slowly across the screen – it doesn’t leave a persistent trace which makes reliable measurements difficult. However, it is possible to view single waveforms with an analog storage oscilloscope. Fast-forward to the 1990’s to 2020’s – the era of digital oscilloscopes. The early digital models were slow and limited in their measurement capabilities. The advances in technology (improved analog-to-digital converters, more RAM and storage, digital signal processing, automation) helped digital oscilloscopes to become very powerful instruments. As the name suggests, a digital oscilloscope converts an analog signal into a digital one. Once the time-dependent (analog) voltage has been converted into basically numbers, one can play with waveforms and do useful calculations. It’s very easy to calculate values such as min/max/average or standard deviation. Implementation of sophisticated signal processing algorithms opened the doors to Fast Fourier Transform (FFT), logic analysis, complex triggering and much much more. Digital oscilloscopes also have one huge advantage over their analog counterparts: one can capture and store a single waveform for viewing (e. g. zoom in in order to see more details). Tektronix released a very nice publication on this matter, The XYZs of Oscilloscopes Primer. It can be downloaded for free from Tek’s website. # Purchase Criteria for a Digital Oscilloscope In my case, I just needed a general-purpose digital oscilloscope in order to make quick measurements and do the documentation stuff. Now what to look for? The key specifications would be: number of channels, analog bandwidth, sampling rate, memory and vertical resolution. Modern-day oscilloscopes have all kinds of additional features such as interfaces (USB, RS-232, Ethernet, GPIB), touchscreen display, integrated digital multimeters, VGA/HDMI/DisplayPort, input termination (1 MΩ/50 Ω) and much more. Those features can improve your workflow and speed up your work. I should mention that the size of the oscilloscope can be important, too. Digital oscilloscopes can be very compact and take almost no space on the bench compared to their analog counterparts. This is important for people with space constraints like a student’s living room or a small garage. There is one more parameter which sums up the features: the price 🙁 I’ve scrambled few hundred Euros in order to buy myself a 2nd hand digital oscilloscope. Since 2010’s/2020’s oscilloscopes are a little bit too expensive, I’ve looked into the mid 1990’s to mid 2000’s era digital oscilloscopes. By the end of 1990’s, digital oscilloscopes had quite potent measurement capabilities. Tektronix and Agilent Technologies (successor to Hewlett-Packard, now Keysight Technologies) designed excellent oscilloscopes for all kinds of applications – mobile communications, digital design and debugging, video and others. Other brands to consider would be Fluke/Philips, Hameg (today Rohde & Schwarz) or LeCroy. Fast-forward 25 years into the future and a 1990’s 20k USD high-end oscilloscope can be bought today in the order of just few hundreds Euros. I guess the only reason I’m stuck with Tektronix oscilloscopes in general is their excellent documentation (User and Service Manuals) and the ability to repair it according to the documentation. Huge props to Kurt of TekWiki and also to all the TekWiki contributors and enthusiasts which maintain the wiki pages and manuals archives. Another invaluable source of Tektronix-related information is the mailing list TekScopes@Groups.io. Speaking of few hundreds of Euros: the price range below 1k EUR is in direct competition with modern-day digital oscilloscopes! I certainly had considered whether to spend let’s say 500 EUR on a new Rigol oscilloscope (which can be hacked/upgraded to a multi thousand dollar machine) or on a 25 year old vintage chobby which has possibly seen darkness, torture, abuse and failures. I’ve been hesitating buying different brands such as PeakTech, Rigol, Siglent or UNI-T for different reasons. Don’t get me wrong – they manufacture great and affordable oscilloscopes. The devil is in the details. # Tektronix TDS 754D Series Oscilloscope The Tektronix TDS Series seemed to fit very well in my scope scheme. I was looking for a decent amount of analog bandwidth (at least 400 MHz), a high sampling rate (if possible 5x to 10x the analog bandwidth) and 4 channels. Tektronix TDS 784D came in my mind because it had everything I needed. Unfortunately, it wasn’t available at the time on German markets like eBay or eBay-Kleinanzeigen (German analogy to craigslist). So I was looking for a suitable alternative. The EEVBlog community has quite a few threads on TDS Series oscilloscopes. One in particular was interesting: the models TDS 754D and 784D are electronically nearly identical. It is possible to convert a TDS 754D model (500 MHz bandwidth) into a TDS 784D (1 GHz bandwidth) by means of a small modification. This modification (removing a resistor and few capacitors) is well documented and can be done very easily. Since there were few Tek TDS 754D oscilloscopes on the market, I thought to give it a try. ## First Impression Tektronix TDS 754D is a compact unit when compared to my 7000 Series “boat anchors”. It has a very clean designed front panel, color display and vents for a large fan (approx 15 cm diameter) on the left hand side. The noise generated by the fan is a little bit annoying but tolerable. Since the heat generated by the oscilloscope has to be carried away, it’s crucial to maintain a proper airflow. There are many ventilation slots on the housing which need some clearance, otherwise the oscilloscope may be damaged due to overheating. Booting up the device takes about a minute and the warm-up time is at least 15 minutes. The color display is a little bit strange: while moving my head, the display colors are splitted into their red/green/blue components (color fringing). This is a little bit annoying but on the other hand, the image quality (in resting position) is very good – sharp image, rich colors, “dark theme” which is pleasing for my eyes when working in dim or darker environments. The oscilloscope takes quite a lot of space on the workbench. Placing the oscilloscope outside of ergonomic positions is not recommended because one has to push many buttons and turn the knobs in order to find optimal settings. ## Quick Check of the Four Channels The pictures provided by the seller showed a deviation on Channel #2 which was a little bit worrying. He attached the probe and got no signal displayed. The root of cause could have been anything ranging from a defective channel to false user setting. Luckily, it turned out to be a false setting by him. All four channels were tested with a function generator in 50 Ω and 1 MΩ (“High Z”) terminations. The amplitude was set to 1 Vpp at various frequencies – The sine waveforms were clean without any visible noise or distortion. I was able to test the analog bandwidth with my Leo Bodnar Fast Pulse Generator. It generates a 10 MHz squarewave with a very steep rising edge. When measuring the 10% to 90% rise time, one can determine the oscilloscope bandwidth as shown in my previous blog entry. The rise time of channel 1 through 4 with a 50 Ω termination was in the order of 680 ps which corresponds to a -3 dB bandwidth of 514 MHz. ## Menus and Settings The menus and settings are easily accessible through push buttons and knobs. The response is good, the menus and sub-menus are arranged in a logical order. Some settings are a little bit hidden, e. g. the switch between XY and YT display modes and waveform average settings. Nevertheless, with a little bit of practice, one will memorize the location of the settings automagically. The numerical keyboard is very convenient as soon as exact setting values are needed (e. g. if you want to set the trigger level to an arbitrary value, you don’t have to turn the knob and fine-tune). ## Tektronix WaveStar and Remote Control via GPIB I must admit, this one blew my mind. I was concerned how to transfer screenshots and images from the oscilloscope to my PC. I have bunch of 3.5″ diskettes lying somewhere around and an “old PC” (IBM ThinkCentre from the early 2000’s) which could be used for data transfer. However, transferring the data from oscilloscope to an ancient PC and from there to the local network would have been a workflow horror. According to the oscilloscope manuals, the data transfer was possible either via serial interface (RS-232) or the general-purpose interface bus (GPIB). Older oscilloscopes with GPIB interface (e. g. the Tek 2465B) can be controlled remotely but the user is unable to readout the measurements. TDS Series oscilloscopes do have this capability! It’s possible to control the oscilloscope remotely and fetch the waveform data in a human-readable ASCII format. The communication with the oscilloscope can be established either via RS-232 or GPIB. The setup is straight-forward in case of GPIB: set up a GPIB address on the device, connect the oscilloscope via a GPIB controller to the PC, install VISA drivers and it should work. Once everything is installed, the communication with the oscilloscope can be checked by sending the *IDN? command. ## Installing Tektronix WaveStar and Setting Up the Oscilloscope Communication The Tektronix WaveStar Software manages the communication between the remote controllable oscilloscope and the PC. Unfortunately, the software isn’t maintained or supported by Tektronix anymore. I’m not sure if WaveStar can be obtained from the Tek website directly – however, there is a downloadable ZIP file on TekWiki. The product key for this application is available on the internet (e. g. Tektronix user forum). I have successfully installed WaveStar on my Windows 10 (version 21H2) PC. Windows 7 or Windows XP should work, too. I’ve set up an image gallery with screenshots from the installation. ## Tektronix WaveStar GUI Using the Graphical User Interface or GUI wasn’t very intuitive after starting it. WaveStar has a Explorer View which allows access to the oscilloscope functions, such as settings, measurements, cursors etc. In order to transfer data from the oscilloscope to the PC, one has to generate a new datasheet. The selection of datasheets leads to different types of measurements. The YTSheet for example is able to plot the voltage vs. time. In order to create a “screenshot”, one has to create a new datasheet “NotesSheet” and drag-and-drop the “Screen Copy” from the Explorer View Local > TekTDS454D > Data > Display > Screen Copy (Color) into the newly created NotesSheet. After downloading the Screen Copy into the NotesSheet, it is possible to copy & paste it into different applications, such as LibreOffice Writer or MS Paint. The resolution of the image is 640 x 480 px and good enough for quick documentation purposes. ## Further Data Processing After playing few hours with WaveStar, I figured out how to extract measurement data in different ways: as vector graphics and as comma separated values or CSV. Vector graphics are very nice when it comes to publication quality images. It is possible to print out the YTScreen as a PDF file (Windows 10 offers a PDF printer driver natively, otherwise you’ll have to install one manually). The informations inside of the PDF are vector graphics which can be imported into a suitable program such as InkScape (another well-known programs would be Corel Draw or Adobe Illustrator). InkScape is an open source vector graphics editor which allows to manipulate vector graphics. Vector graphics are basically numbers which contain x,y-coordinates for lines to be drawn. Bitmap graphics on the other hand are color and brightness informations stored in a matrix. Whenever scaling bitmap images, one has to deal with (ugly) scaling artefacts where vector graphics can be scaled (or zoomed in) indefinetely. Last but not least, I was able to export the WaveformTabular data into a CSV file. A CSV file can be loaded into a spreadsheet application such as LibreOffice Calc or Microsoft Excel. The waveform data can be processed afterwards – doing statistics or digital signal processing (e. g. with Python) can be done very easily! # Conclusion Well, I’ve got myself a very powerful digital oscilloscope now. I still haven’t tested the full capabilities of this instrument but I’m very satisfied with its performance at this point and can recommend it to anyone seeking a 20-30 year old instrument. Although I paid far too much for it (it was well over 500 EUR), it was worth it. I’ll test its measurement capabilities during my vacation which is starting next week 😉 ## My “9 Euro ticket” Travelling Experience # Introduction Since June 1st 2022 it is possible to travel within the whole country of Germany by public transportation system with a flat-rate ticket which costs only 9 Euros per month. The ticket is time-limited and valid only during the current month. It applies only to the German public transportation system (e. g. trains of German Railways such as DB RegionalBahn, RegionalExpress, S-Bahn, also metropolitan transportation systems such as U-Bahn, busses & trams). The ticket doesn’t apply to inter-regional trains such as ICE, IC or EC and doesn’t cover 1st class travelling in any of the mentioned transportation systems. It was proposed by the German Government as a time-limited experiment in order to motivate people to drive less cars and as a financial relief due to very high energy and gasoline prices. The 9 Euro ticket will be obtainable during months June, July and August in 2022. The financial losses to the transportation companies due to this very cheap ticket will be compensated by the taxpayers. I think this is a good idea although its execution wasn’t very well planned. Nevertheless, it’s a step in the right direction! The German public transportation system is “OK” but isn’t able to handle – let’s say – sudden passenger increases of 20% or more very easily. # My Trip from Braunschweig to Meschede I’ve bought myself a 9 Euro ticket and went on a trip from Braunschweig to a small city called Meschede. I’ve been living and working in Meschede for about 1.5 years before moving to Braunschweig. Although its location is in mid-western Germany and it’s only 260ish kilometers distant, it is a remote location because it’s surrounded by forests and hills of the Sauerland region. There is no easy way to get inside or outside of Sauerland 😉 My trip planner calculated a total travel time of about 5 hours and 21 minutes with approx. 1 hour of exchange durations. Travelling from Braunschweig to Meschede and back by train will be at least a 10 hr and 42 minute trip (which was the case). I’ve started from my apartment at roughly 6:30 in the morning and took the bus to the central station. My first train went from Braunschweig to Bielefeld, a ~2hr trip. The two floor train was very clean and there was enough room for passengers and bicycle users. Changing train in Bielefeld offered a different image. The train was heading in direction to Dortmund. It was pretty full with passengers and commuters but I was able to get a seat in the bike wagon. People standing around me were 10 drunk ladies (bachelor party) which were very funny and entertaining. In Dortmund, the train filled up to its maximum capacity where I felt sorry for the passengers. They were stuffed like canned tuna. I’ve spent about 20 minutes on the Dortmund central station gathering some fresh air. Nothing special: cops everywhere, fewer-than-usual soccer fans, LGBTQ pride flag carriers, occasionally religious fanatics and beggars. It’s a wild place but somehow appealing. The last section of the route was from Dortmund to Meschede. The train was OK – clean, not too full, a little bit loud due to its diesel engines. I found a seat near a group of drunk guys which were also very funny and entertaining. At one of the train stations, another drunk guy joined and collected refundable bottles. This was so unreal, he looked like a 70 year old smashed alcoholic, barely able to walk but the drunk party donated like 10-12 empty bottles of beer instantly to him 😀 To my surprise, all trains were on time and there were no major delays. I arrived in Meschede at 12:40 so the total travelling time starting from my apartment was approx. 6 hr 10 min. I couldn’t stay for a long time in Meschede because of the 6h return trip so I planned to stay for about 4.5 hours. This is one thing to consider when using 9 Euro ticket: the travel duration killed most of the day and reduced the visit duration significantly. However, I was able to visit my old workplace and say hello to my former colleagues and some old friends. The Open-Door Day at University of Applied Sciences in Meschede offered a rich program of science, engineering, entertainment and food. The weather was excellent and very inviting. The return journey was very tiring: the train from Dortmund to Bielefeld was 15 minutes late and full to the max. We were standing for about 30 minutes without any room to move until a portion of passengers left the train. Luckily due to long exchange duration, the meanwhile 20 minute delay didn’t affect the connecting train from Bielefeld to Braunschweig. The section from Bielefeld to Braunschweig was OK – no problems whatsoever. About 90% of people inside of trains were wearing a face mask due to COVID-19 pandemic which was very nice. The current infections are very low in the region of 350 per 100 000 inhabitants but one has to be careful anyways. There may be a new infection wave in the autumn this year… # Summary and conclusion I’ve spent most of my day travelling in trains. It was cheap but also do-able. I was able to travel from Braunschweig to Meschede and back in one day for 9 Euros! This trip would have cost at least 50 Euros instead without much improvement in travel quality. I’ve been to Meschede few weeks before this trip by car and the gasoline alone cost me 80 Euros. The quality of trains (size, cleanliness) and its connection vary significantly from region to region. One has to consider that trains will be full during commuting hours and empty outside of them. During weekends, bicyclists take up a lot of space in the trains so this can be an issue, too. The people are usually friendly and very helpful but there are also exceptions: drunk and loud people, some of them smell badly due to sweat or even urine. The toilets are in an acceptable condition but don’t rely on this – try to take Number One/Number Two at a train station instead inside of a train toilet. The 9 Euro ticket is definitely worth it and should be used to visit all kinds of places in Germany. There are wonderful towns and regions which should be visited during the summer time. I haven’t been to cities such as Rostock, Schwerin, Konstanz, Kiel, Flensburg, Saarbrücken and many others so I’ll try to catch up with my visits. ## Controlling Test Equipment via GPIB and Python # Introduction Taking measurements with electronic equipment doesn’t require much skill and effort: turn the device on, set up the measurement parameters such as input channel/range/sensitivity, trigger the measurement and read the numbers on the screen (if there are any). The same procedure can be applied in everyday situations – whether you have to read the time from a clock or check the temperature in your fridge/oven. But what if one does have to take thousands or even million of measurements every day? What happens if measurements have to be performed 24/7? There surely are applications where it is necessary to take measurements manually but putting a human behind a bench and performing boring and repetitive measurements during a 8-hour-workday is prone to exhaustion and errors. This is where automated test equipment (ATE) kicks in. It is possible to let a computer do the “boring stuff” while the human operator may spend his or her precious time on solving problems instead of doing repetitive work. I’ve successfully stolen borrowed and tested foreign Python code and want to demonstrate how to control a digital multimeter and a function generator via General-Purpose Interface Bus (GPIB) and Python’s pyvisa module. # GPIB GPIB is a 8-bit parallel interface bus which has been invented in the late 1960’s by Hewlett-Packard (see Wikipedia article). Prior to its standardization in the late 1970’s as IEEE 488, it was known as HP-IB (Hewlett-Packard Interface Bus). It allows interconnection between instruments and controllers. It is system independent of device functions. This de facto standard has been around for a while and is well established in the Test & Measurement industry. Fast forward to the year 2022, GPIB is still present in test equipment although in recent two decades, communication with instruments have been enabled with modern-day interfaces such as USB or Ethernet. GPIB allows a very diverse topology – one can connect all instruments either in a Daisy Chain or parallel configuration or even in a combination of both of them. There are certain GPIB-specific limitations concerning cable lengths, maximum number of connected devices, data transfer speeds and others which have to be considered when automating test equipment. # Experimental Setup I’ve connected a HP 34401A digital multimeter to a HP 3325B function generator in order to generate a sinusoidal test signal, which can be represented by an equation $$v(t) = v_0 \cdot \sin (\omega t)$$ with $$\omega = 2\pi f$$. Both instruments are connected to a GPIB controller National Instruments GPIB-ENET/100. The GPIB-ENET controller acts as a bridge and allows communication to GPIB devices via ethernet, which is highly convenient in my lab. It allows me to control the instruments via LAN from any of my computers. This device requires drivers from National Instruments so I’ve chosen NI MAX v17.5 along with NI-488.2 driver in order to be able to use the legacy GIPB-ENET/100 controller. I’m using Windows 10 along with the WinPython distribution. WinPython comes along with Python 3.9.10 and hundreds of useful modules, such as numpy, pandas, matplotlib etc. (which are optional). The pyvisa module needs to be installed manually via pip, see PyVISA’s project website for further installation instructions. ## Important notice for new users Many of the GPIB-USB adapters sold on eBay are fakes or counterfeits. They are sold very cheap (different brands such as National Instruments, Agilent/Keysight, Keithley) in the price range of 100 EUR to 120 EUR per piece whereas the genuine GPIB-USB adapters often cost more than 400 EUR. Buying a new unit directly from the manufacturer will cost in the order of 900 -1200EUR which is kinda crazy! Nevertheless, some of the fake GPIB-USB adapters seem to work pretty well and are recognized by the official drivers. However, some EEVBlog users also report having problems with fake adapters… So it’s really a gamble. I’ve bought a 2nd hand “fake” GBIB-USB-HS for cheap which didn’t work for the previous owner but works perfectly on my Windows 10 machine. I haven’t tested it with Raspberry PI yet which will be done in a future project. The GPIB-ENET/100 unit is a little bit pricy (~200ish EUR) but the most affordable interface cards are the PCI-GPIB types. I’ve seen genuine units being sold on eBay for 40-80 EUR. No need to spend hundreds of Euros for a GPIB interface. Microcontroller-based GPIB-USB projects also exist where one can assemble a GPIB-USB or GPIB-LAN controller for cheap (which may not work always but gets the job done). Proper GPIB-cables can be expensive, too, but I’ve seen them being sold for approx. 7 … 12 EUR per piece occasionally. Custom-made GPIB cables out of ribbon cable and Centronics/Amphenol connectors may also work and get the job done. # Example Code First of all we’re gonna need to import some Python modules. pyvisa is mandatory, other modules will be useful. # Import Python modules import pyvisa import numpy as np import pandas as pd import matplotlib.pyplot as plt import datetime import time Next step: we need to open the pyvisa Resource Manager. We need to know the GPIB addresses of our instruments. For example, I’ve assigned GPIB address No. 4 to my digital multimeter dmm and the frequency generator fgen has the GPIB address No. 18. GPIB 22 is another digital multimeter which isn’t used in this example. # Open pyvisa Resource Manager, print out a list with active devices rm = pyvisa.ResourceManager() print(rm.list_resources()) The print(rm.list_resources()) statement gives us the GPIB addresses of the connected devices, such as: ('ASRL3::INSTR', 'GPIB0::4::INSTR', 'GPIB0::18::INSTR', 'GPIB0::22::INSTR'). Now we can open a connection to the resources. # Assign GPIB address 4 to dmm and open resource dmm = rm.open_resource('GPIB0::4::INSTR') dmm.read_termination = '\n' dmm.write_termination = '\n' # Assign GPIB address 18 to fgen and open resource fgen = rm.open_resource('GPIB0::18::INSTR') fgen.read_termination = '\n' fgen.write_termination = '\n' The dmm and fgen objects have been created. The statements dmm.read_termination = '\n' deal with line feed and carriage returns. The next step will set the DMM settings and call an identify string via a SCPI command *IDN? # Set the DMM for measurements print(dmm.query('*IDN?')) # Identify string print(dmm.write('CONF:VOLT:DC 10,0.001')) # Set DC voltage range and 4.5 digits of resolution The same commands are applied to our frequency generator. The output of the DMM looks something like (name, model, firmware version): HEWLETT-PACKARD,34401A,0,4-1-1 22 # Set the FGEN for measurements according to the manual print(fgen.query('*IDN?')) # Identify string print(fgen.write('AM 2.0 VO')) # Set amplitude to 2 V_pp print(fgen.write('FR 0.1 HZ')) # Set frequency to 0.1 Hz The parameters such as measurement range, frequency and amplitude have been set. Now let’s take a single measurement. This is done via query command. A query is a short representation of dmm.write() and dmm.read() commands. The READ? command fetches the current measurement value from the digital multimeter. # Example 1: Take a single measurement print('Measured value: {} Volts.'.format(dmm.query('READ?'))) Measured value: -9.97000000E-02 Volts. We have measured the voltage! Yaay! Its value is -99.7 mV, written in the engineering notation. Our next code example will be used to take 2048 measurements and timestamp them. I’ve used the pandas DataFrame as a convenient structure to store the data. This could have been done with numpy arrays for sure! OK, after setting the Number of acquired samples to N = 2048, I’ve created an empty pandas DataFrame. It will be filled with our precious data. The next line (6.) prints out the date and time. The t0 variable in line (7.) is used to get the timestamp value at the beginning of the measurement. This value will be subtracted from the timestamps generated by time.time(). The for loop starts at the value 0 and calls two commands according to line (9.). It writes a timestamp and measured value into the DataFrame df at index 0. After execution of line (9.), the for loop is repeated N-1 = 2047 times. As soon as the for loop finishes, we need to convert the string-datatype voltages into floating point numbers, otherwise Python will have difficulties handling them with further calculations. # Example 2: Take N numbers of measurements N = 2048 # Number of samples to acquire df = pd.DataFrame(columns=['t', 'V']) # Create an empty pandas DataFrame # Print date and time, write data into the DataFrame print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')) t0 = time.time() for i in range(N): df.loc[i] = [time.time()-t0, dmm.query('READ?')] # Convert dmm readout strings to a float number df.V = df.V.astype(float) That’s it! We’ve acquired 2048 measurements. Just imagine sitting there and typing all these numbers in Excel! Such a waste of time 😉 The next line of code extracts the total measurement time from the DataFrame timestamps. The measurement took approx. 25.8 seconds to complete. This corresponds to approx 79.2 samples per second at 4.5 digits of precision. # Show elapsed time in seconds df.iloc[N-1].t 25.846375942230225 # Calculate the sampling rate in Samples per Second str(np.round((len(df)-1)/df.iloc[N-1].t, 3)) '79.199' The last step of this example will be to plot the results into a graph figure. First we create an empty figure called fig with sizes 11.6 x 8.2 inches at 300 dpi resolution. The plt.plot() command needs the $$x$$– and $$y$$– values (or time and voltage values) as function arguments in order to plot a nice diagram. We label the axes, set a title and grid. The figure is then saved as PNG file and displayed. See picture below! # Plot the results, annotate the graph, show figure and export as PNG file fig = plt.figure(figsize = (11.6, 8.2), dpi=300, facecolor='w', edgecolor='k') plt.plot(df.t, df.V, 'k.') plt.xlabel('Time (s)') plt.ylabel('Voltage (V)') plt.title('HP 34401A measurement (Range: 10 V (DC), Resolution: \n 4.5 digits @ ' + str(np.round((len(df)-1)/df.iloc[N-1].t, 3)) + ' Sa/s,$f$= 0.1 Hz,$v_0$= 2 V$_{pp}\$)')
plt.grid(True)
plt.savefig('Measurement_result.png')
plt.show()

Now we could do some statistics or fit a sine function to it. I’ll explain this in a future blog post. A small bonus can be achieved via the following command:

dmm.write('DISP:TEXT "73 DE DH7DN"')

## Salvaging, desoldering practice

I’m still in the process of building my electronics lab. After two years of acquiring test equipment, it will still take some time to get it to a satisfactory level. The measurement capabilities have been extended to electrical quantities such as voltage, current, resistance, impedance, capacitance, inductance and frequency. Unfortunately, I’m reluctant to buy new general-purpose parts like BNC connectors or electrolyte capacitors. Luckily I was able to find some useful components in the dumpster, which is currently my main source of electronics components. The components are still useful and of value. Those were doomed of being thrown away.

I have few days off right now and some spare time to desolder and sort the components. They will be very useful for future my projects! It’s been a while since I’ve been (de)soldering and this takes forever. It’s hard to forget how to solder but if one doesn’t practice, it takes some time to get the muscle memory back.