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Applied Optoelectronics Inc (AOI) of Sugar Land, TX, USA (a designer and manufacturer of optical and hybrid fibre-coaxial networking products for AI data centers, cable TV and broadband fiber access networks) plans to expand its Houston-area footprint through the addition of two adjacent buildings in Pearland, Texas, adding about 388,000ft2 of manufacturing capacity...

Applied Optoelectronics Inc (AOI) of Sugar Land, TX, USA (a designer and manufacturer of optical and hybrid fibre-coaxial networking products for AI data centers, cable TV and broadband fiber access networks) received a new $71m order for 800G single-mode data-center transceivers in early April from one of its major hyperscale customers. This marks $124m in orders from this customer since mid-March, which will more than double the existing backlog from this customer...

Three smart home hubs, from two different companies. All supporting both 2.4 GHz Wi-Fi and proprietary 900 MHz wireless links. How do they differ, and are similar? Let’s find out.

Last month, I told you about TP-Link’s Tapo Hubs and their functional similarity to Blink’s Sync Modules. And last week, I took apart Blink’s second-generation hub, comparing it to its premiere predecessor which’d gone “under the knife” nearly a decade earlier. Today, I’ll be dissecting the entry-level Tapo H100 hub I conceptually covered in late March.

How comparable (or not) is its design to those of its Blink competitors? Let’s dive in and see.

Smart hub brothers from different mothers?

I shared a full set of outer box shots last month; so to avoid redundancy, this time I’ll show only the perspective that’s different, since last month’s device remains in ongoing use while this one (with a different serial number) is intended (initially, at least) solely for dissection.

As usual, it’s accompanied by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes. Also note that, per the common “US/1.26” notation on the sticker found on the bottom of both boxes, this device and last month’s H100 are presumably based on the same hardware version.

Opening up the packaging, you’ll find a sliver of literature inside, with our patient below it.

The only constant is change

On the product support page I initially referenced earlier, you’ll also discover that there have been four hardware versions to date: v1.0, v1.2, my v1.26, and the subsequent (I’m assuming) v1.8. Attempts to mix-and-match divergent hardware, as I’ve noted before, can be problematic. That said, most households will contain only a single hub device (versus multiple sensors and other “smart” peripherals), minimizing the potential-problem set size in this particular case.

Before continuing, let’s revisit the backside of the device, this time zooming on the markings.

Notice what looks like a label stuck on top of part of the original info? That’s exactly what it is.

As it turns out, the FCC ID found on the backside markings (2AXJ4H100) was also later updated; it’s now 2BH7FH100. Are the two changes related? Dunno.

Time to dive inside, a task that, compared to TP-Link smart switches of (recent) past, was thankfully fairly straightforward this time around.

Inside the front half of the enclosure, you’ll find a speaker (used, for example, to implement the sound emitted when the hub is paired with, and activated by, a “smart” doorbell).

And the mechanical assembly for the pairing-and-reset switch is shown on one side, as seen earlier.

Categorizing the guts

Here, however, is the view that most of you are most interested in, I guess.

The bottom half of the PCB disconnected itself from the back half of the enclosure while I was prying apart the two halves.

Further bending back the PCB reveals how the AC “prongs” connect to it.

As well as the PCB backside itself.

The small five-lead IC in the middle, PCB-labeled U4, is marked:

TACeY1

Its identity is unknown to me (readers?). Below it, in a larger seven-lead package, is On-Bright Electronics’ OB2512NJP offline primary-side-regulation (PSR) power switch. Below that is a M7 high voltage rectifier diode. And to its left is another (bridge and three-lead, this time) rectifier, Galaxy Microelectronics’ MBF10M.

Back to the PCB front side, after “un-popping” the PCB (putting it back in its normal place within the enclosure, which is upside down in both the prior-version and the following photo versus its normal orientation).

Note first the two antennae, one embedded and along the lower edge, the other discrete and along the right side. I assume one’s for 2.4 GHz Wi-Fi while the other supports TP-Link’s proprietary 900 MHz ISM band “ultra-low power wireless protocol”. Reader suggestions as to which is what are greatly appreciated in the comments.

In the upper right (again, lower left in normal operating orientation) is the status LED, which ends up shining out the device front cover. The pairing-and-reset switch is along the left side. The top half of the PCB, perhaps obviously given the sizeable transformer, houses the AC/DC conversion circuitry (the fact that the AC prongs are directly behind it at the rear of the device is another functional tipoff).

And, last but not least, the various ICs. In the lower right corner of the transformer is an Eon Silicon Solution EN56Q64-104HIP 64 Mbit serial flash memory, which we’ve seen before in both higher and lower capacities. I assume it houses the code for Realtek’s RTL8710CM SoC below and to its left, also found in the first two of the three TP-Link smart switches I’ve dissected so far. At the bottom, in the middle, is WayTronic’s WT588F02B audio DSP with an integrated DAC, which “can directly drive 8R 0.5W speakers”, an unsurprising function given the speaker connection directly to the left of it. Above and to the right of the audio DSP is another IC I can’t ID:

35UT
53C1

And above and to the left of the mono speaker connector is one final mystery:

300A
S992
515

Reader insights into any of the chips I was unable to identify, as well as broader thoughts on anything I’ve discussed here, are always welcome in the comments.

Brian Dipert is the associate editor, as well as a contributing editor, at EDN.

Related Content

• The Tapo Hub: TP-Link joins the low-bandwidth, long-range RF club
• The Blink Sync Module 2: Faster response and local storage, too
• Teardown: Security camera network module
• TP-Link’s Kasa HS103: A smart plug with solid network connectivity
• TP-Link’s Kasa EP10: If at first it doesn’t connect, buy, buy again

The post TP-Link’s Tapo H100: Smart sensing unencumbered appeared first on EDN.

Ketone detection may sound like the domain of biochemistry, but at its core, it’s also an electronics challenge: how do we translate a chemical presence into a measurable electrical signal?

The key lies in the ability of circuits to convert molecular interactions into quantifiable outputs. Through principles like signal conversion, amplification, and conditioning, electronics transform invisible chemical activity into reliable data, making ketone monitoring practical and accurate while underscoring how deeply electronics shape modern health technologies.

Ketones: Small molecules, big impact

Ketone detection is crucial because these molecules act as direct indicators of how the body manages its energy balance. Moderate levels can reflect healthy states such as fasting, exercise, or adherence to ketogenic diets, while dangerously high concentrations may signal conditions like diabetic ketoacidosis that require urgent medical attention.

By providing timely and accurate measurements, ketone monitoring empowers individuals to optimize nutrition and performance and gives clinicians essential data to prevent and manage metabolic complications. In both everyday wellness and clinical care, reliable ketone tracking plays a decisive role in safeguarding health.

Overview of ketone detection sensors

Nowadays ketone detection has moved well beyond the lab bench and into lifestyle and wearable electronics. Compact analyzers are being built into fitness trackers, smartwatches, and portable health devices, giving users real-time insights into metabolism and diet. This evolution is powered by the fundamentals of electronics—miniaturization, low-power design, and signal processing—that make complex biochemical measurements practical in everyday life, turning health monitoring into a seamless part of daily routines.

While electronics provide the backbone for translating chemistry into measurable signals, the choice of sensor defines how ketones are detected. Electrochemical sensors generate currents via redox reactions, optical sensors capture variations in light absorption or fluorescence, and chemiresistive sensors—including semiconductor gas sensors—exploit surface-level conductivity shifts. Each technology offers a unique pathway from molecular interaction to electrical output, setting the stage for circuits to amplify, filter, and interpret the data with precision.

Ketone sensing: The gold standard and beyond

In practice, blood testing is the clinical gold standard, using the enzyme β-hydroxybutyrate dehydrogenase (HBDH) to generate a precise electrical signal from β-hydroxybutyrate (BHB). Keep note that a blood ketone meter functions as a miniaturized potentiostat; it maintains a fixed voltage across the biosensor to measure the current produced by this reaction, providing the data needed to distinguish safe ketosis from metabolic crisis.

Figure 1 Today’s multifunction blood meter kits provide a fast and reliable method for measuring β-ketone, blood glucose, and other parameters from fresh whole blood samples in just a few simple steps. Source: eLinkCare

However, the field is evolving beyond the invasive finger-prick. Researchers are now optimizing alternative biomarkers and delivery methods to bridge the gap between clinical accuracy and user convenience.

Exhaled breath analysis targets acetone—a volatile byproduct of fat metabolism. Current technologies, such as chemiresistive metal-oxide sensors, offer a high-frequency, non-invasive “proxy” for ketosis. While breath analysis currently lacks the clinical precision required for acute emergencies like diabetic ketoacidosis (DKA), it provides a sustainable, pain-free alternative for routine wellness tracking.

In a nutshell, ketone breath analyzers typically employ semiconductor-based, chemiresistive sensors to detect acetone—a byproduct of fat metabolism—in exhaled breath. These sensors function by measuring changes in electrical resistance triggered by volatile organic compounds (VOCs), which serves as a proxy for blood ketone concentration. High-end models often integrate CMOS technology to enhance both sensitivity and measurement precision.

Figure 2 Ketone breath analyzers and subcutaneous sensors deliver real-time feedback on ketosis levels. Source: Author

Continuous ketone monitoring (CKM) is an emerging technology that utilizes a small subcutaneous sensor—similar to a continuous glucose monitor (CGM)—to measure BHB levels in the interstitial fluid. By providing real-time data and automated alerts, these devices aim to detect rising ketone levels before they escalate into metabolic emergencies, effectively transitioning patient care from ‘spot-check’ diagnostics to continuous, proactive health management.

Note that a subcutaneous sensor is a tiny, flexible filament inserted into the fatty tissue just beneath the skin. By monitoring the interstitial fluid in this layer, the sensor uses enzymes to measure specific chemical markers—like glucose or ketones—and converts those readings into a continuous digital stream. Because it stays in place for several days and does not require venous access, it offers a painless, real-time alternative to repeated finger-prick testing.

Electronic biosensing for makers

To wrap this up, remember that while the medical industry uses highly proprietary, pre-calibrated systems, the underlying principle is a fantastic playground for makers.

Whether you are working with a glucose oxidase strip for blood sugar or a β-hydroxybutyrate strip for ketone levels, the principle is the same: enzyme-mediated reactions generate electrons that must be measured against a stable reference potential.

Once you master the transimpedance amplifier (TIA), you have essentially built the core of a professional-grade diagnostic instrument. In fact, most commercial biosensors integrate the TIA and supporting circuitry into an analog front end (AFE), which delivers low-noise performance and simplifies design, an approach that makers can emulate at smaller scale when experimenting.

On a related note, amperometry is the electrochemical technique at the heart of most biosensor strips. It involves applying a fixed potential to an electrode and measuring the resulting current, which is directly proportional to the concentration of the analyte.

In glucose oxidase strips, the enzymatic reaction produces hydrogen peroxide that is oxidized at the electrode, while in β-hydroxybutyrate strips, NADH transfers electrons through a mediator. In both cases, the transimpedance amplifier converts this tiny current into a usable voltage signal, enabling accurate, low-noise measurement.

Figure 3 Quick view shows a closeup of a standard ketone blood tester strip. Source: Author

For those curious about non-chemical ketone monitoring, it’s worth noting that hobbyists have also experimented with MQ13x series gas sensors such as MQ138 to approximate acetone levels in breath.

These gas sensors are not medical-grade and require careful calibration against known standards, but they can respond to volatile organic compounds in exhaled breath. Pairing one with a microcontroller, a stable heater supply and signal conditioning circuitry give you a rough, experimental ketone breath analyzer. It’s a fun proof-of-concept project—ideal for learning sensor physics and electronics.

Figure 4 MQ138 sensor module helps detect acetone in exhaled breath, enabling experimental DIY ketone analysis. Source: Author

Just keep in mind that for any real-world health tracking, these DIY setups should be for educational exploration only. Medical-grade devices undergo extensive clinical validation to handle variables like hematocrit levels, temperature, and signal interference—factors that a prototype might miss.

Finally, do not let the complexity of biomedical electronics intimidate you. Every expert once started as a novice tinkering with circuits and sensors. Dive in, experiment boldly, and let curiosity be your guide—the frontier of electronic biosensing is wide open for makers willing to explore.

T. K. Hareendran is a self-taught electronics enthusiast with a strong passion for innovative circuit design and hands-on technology. He develops both experimental and practical electronic projects, documenting and sharing his work to support fellow tinkerers and learners. Beyond the workbench, he dedicates time to technical writing and hardware evaluations to contribute meaningfully to the maker community.

Related Content

• What’s in store for optical biosensors?
• The critical role of sensors in medical devices
• Designer’s guide: Sensors for medical applications
• Developing medical sensors compliant with global requirements
• Tools and techniques for electrical characterization of biosensors

The post Electronic biosensing: A quick take on ketone detection appeared first on EDN.

Market analyst firm TrendForce forecasts that the global AI-focused optical transceiver market will rise at more than 57% year-on-year from US$16.5bn in 2025 to $26bn in 2026, as it has entered a phase of rapid growth. This surge is driven not only by specification upgrades but also reflects a broader structural reshaping of the optical communications supply chain amid accelerating AI data-center deployment...

The UK Semiconductor Centre (UKSC) has appointed Brian Robertson as director of international partnerships to lead on strengthening the UK’s global position in semiconductors and deepening connections between the UK ecosystem and international partners...

Tried to use it to light some LED’s though I think the circuit expects a battery voltage to use as feedback as it has very low output current otherwise. Short circuit current was 300mA submitted by /u/ram_the_socket [link] [comments]

Current setup 😅 ESP32 + RFID + SDR + random modules Not sure if this will fully work yet… But it’s getting interesting 👀 Any ideas what I should add next? submitted by /u/AppropriatePen283 [link] [comments]

There must be a T48 UV erasing addon with the EPROM blank check. 270-280nm 800mW diode. submitted by /u/nerovny [link] [comments]

I have been designing PCBs to carry a small microcontroller, an RS485 transceiver, an LED and the associated balance of plant required to make lights for my ROV. Space is at a premium, so track sizes are being chosen to minimise real estate used. KiCad has a netclasses setup page that uses IPC 2221 requirements and PCBway capabilities. I have come up with a sensible set of pre-defined values https://philipmcgaw.com/kicad-traces-net-classes/ submitted by /u/skippyuk [link] [comments]

submitted by /u/AutoModerator
[link] [comments]

Infineon Technologies AG of Munich, Germany says that radiation-hardened (rad-hard) devices from its IR HiRel (high-reliability) division supported the electronic backbone — from critical power supply and control systems to data communications — were at the heart of the Orion capsule of NASA’s Artemis II mission, which recently returned from its 10-days around the Moon (reaching the furthest distance from Earth ever achieved by crewed spaceflight)...

Nuvoton Technology of Kyoto, Japan has announced the start of mass production of the KLC434FL01WW high-power violet laser diode (402nm, 4.5W), which achieves what is claimed to be industry-leading optical output in a 9.0mm-diameter TO-9 CAN package, for continuous-wave (CW) operation at a case temperature (Tc) of 25°C. Due to the proprietary device structure and heat-dissipation design technology, the new product achieves 1.5 times the 3.0W optical output of the firm’s conventional 402nm product in a TO-9 CAN package (the KLC432FL01WW), contributing to improved production throughput in optical equipment such as maskless lithography systems. Furthermore, adding this product to the firm’s lineup enables the product portfolio to support major photosensitive materials used in advanced semiconductor packaging...

40 років Чорнобильської катастрофи: реалії сьогодення та виклики майбутнього kpi пт, 04/17/2026 - 16:24

⭐ Запрошуємо на презентацію дуальної освіти КПІ ім. Ігоря Сікорського та Melexis Academy kpi пт, 04/17/2026 - 16:00

Automated test equipment (ATE) supplier Teradyne is bolstering its test solutions for semiconductor design by acquiring TestInsight, a provider of test program creation, pattern conversion, and pre-silicon validation tools used across ATE platforms and semiconductor design environments.

By acquiring a supplier of semiconductor test development, validation, and conversion software, Teradyne aims to scale its next generation of pre-silicon validation and automated pattern generation technologies. That strengthens Teradyne’s ability to support semiconductor design-in activities to accelerate time-to-market in the emerging AI and data center markets.

Here is how pattern conversion across multiple cores and CPUs accelerates the test program. Source: TestInsight

Greg Smith, president and CEO of Teradyne, calls TestInsight’s tools foundational to modern test program development. “By integrating the TestInsight team into Teradyne, we enhance our ability to help customers achieve silicon readiness faster and with greater confidence.”

The acquisition will allow Teradyne to combine its ATE platforms with TestInsight’s tightly integrated design-to-test workflow, thereby reducing debug cycles, improving coverage, and enabling earlier test program readiness. In short, the acquisition of a design-to-test software firm will help Teradyne close the gap between design and test in semiconductor design environments.

TestInsight announced that it will continue to support its existing customers across all ATE platforms.

Related Content

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• Flexible Test Strategies Keeping Pace with Semiconductor Evolution

The post Teradyne snaps up TestInsight to boost ATE for semiconductors appeared first on EDN.

👍 Запрошуємо на вебінар "Ліцензії Creative Commons: шлях до відкритої науки для українських авторів та видавців" kpi пт, 04/17/2026 - 15:56

💢 Онлайн-лекція “OpenAlex – найбільша відкрита база наукових робіт” kpi пт, 04/17/2026 - 15:51

Aliasing is thankfully becoming a less frequent problem due to improved instrument designs. Users should still be aware of it to prevent time- and money-costly errors.

Aliasing is an ever-present potential problem in sampled data acquisition systems. It occurs when input signals are sampled at a sample rate that is too low. If you haven’t been bamboozled by an aliased signal, you are extremely lucky.

Sampled data instruments, such as digitizers and digital oscilloscopes, must sample their input signals at a rate greater than twice the highest frequency component present in the input signal. If this criterion is not met, then aliasing can occur. Figure 1 shows an example of aliasing.

Figure 1 In this example of aliasing, a 50MHz sine wave was acquired at sampling rates of 1 Giga samples per second (GS/s) and 55 Mega samples per second (MS/s). The 55 MS/s acquisition is aliased and displayed as a 5 MHz waveform.
Source: Art Pini

A 50 MHz sine wave was acquired at both 1 GS/s and 55 MS/s. The waveform acquired at 1 GS/s has the correct frequency of 50 MHz as shown in the frequency parameter P1. The waveform acquired at 55 MS/s is aliased and has a frequency of 5 MHz as reported in parameter readout P2. The alias waveform will appear as having a different frequency than the correctly sampled waveform. This can be a significant problem that can be costly if not addressed carefully.

Let’s look into aliasing and learn how to deal with it. Sampling is a mixing process. When you apply an input signal to a sampler, the resulting output from the sampler contains the original waveforms, the sampling waveform, and the sum and difference frequencies, including the harmonics of the sampling signal. This is illustrated in Figure 2.

Figure 2 Sampling is a mixing or multiplicative process. The baseband frequency spectrum of the acquired signal appears as the upper and lower sidebands about the sampling frequency and all its harmonics.
Source: Art Pini

A correctly sampled waveform will have more than two samples per cycle at the bandwidth limit. In the sampler output, the baseband frequency spectrum of the input signal will appear as upper and lower sidebands about the sampling frequency and its harmonics. The right-hand graphs show the output spectrum of the sampler for the correct sampling rate (upper) and the undersampled case (lower). As the sampling frequency is decreased below twice the input signal bandwidth, the lower sideband of the sampling frequency interferes with the baseband signal, resulting in aliasing.

In the time-domain view (left-hand graphs), the aliased signal lacks sufficient time resolution to track the input waveform. Returning to the example in Figure 1, the 50 MHz input sampled at 55 MS/s will result in sum and difference image frequencies that are above and below the 55 MS/s sampling frequency. The lower sideband image falls into the baseband region of the spectrum and is the source of the 5 MHz alias signal.

Current digital instrument designs generally use sampling rates much higher than the instrument’s analog bandwidth. Some instruments may use sharp-cutoff anti-aliasing low-pass filters to limit the input bandwidth and control the instrument’s frequency response. These techniques, combined with long acquisition memories, also minimize this classic problem.  Still, users should be aware of aliasing.

Recognizing Aliasing

It is good practice to determine the frequency of the measured signal and verify that it has not been aliased. If the characteristics of the input signal are unknown, it is good practice to view the signal at the highest available sample rate, then decrease the sampling rate as needed. If aliasing occurs, you will see the signal’s frequency change as you select a lower sampling rate.

Another hint that a signal is an alias is that it will appear to have an unstable trigger and will jump erratically in time. This occurs because the instrument is triggered by the signal, and the alias, with fewer samples, may not display the trigger point. The instrument displays the nearest sample, which varies from one acquisition to the next, causing instability.

Aliasing can also be recognized by observing the effect on the input signal’s frequency-domain spectrum as the signal’s frequency is varied. A spectral component that shows a decrease in frequency when the input signal’s frequency is increased, a reversal of direction, is an alias. As the frequency of a sine wave increases, the spectral line corresponding to that sine wave will move to the right until it hits the Nyquist frequency of one-half the sample rate.

As the frequency increases above Nyquist, an aliased image from the lower sideband about the sampling frequency will fold back into the baseband spectrum, moving downward in frequency. The lower-sideband images for each harmonic of the sampling frequency show this reversal. Upper sideband images will move in the correct direction. This phenomenon is called spectral folding.

A helpful technique to view an aliased signal

If the signal is a relatively simple periodic waveform, such as the example sine wave, then enabling infinite display persistence will show the underlying waveform, as shown in Figure 3.

Figure 3 The aliased signal (upper trace) and the same signal displayed with infinite persistence turned on (lower trace). The persistence display accumulates all the sample values showing the original 50 MHz waveform.
Source: Art Pini

All sample points in the aliased waveform are real. If infinite persistence is enabled, all samples are accumulated on the persistence display, and the original unaliased waveform is eventually recovered. This technique won’t work for complex signals such as non-return-to-zero (NRZ) data or broadband signals.

Using aliased waveforms

Given that aliased signals are made up of real samples, an aliased signal can be used in measurements, as long as the signal’s frequency is not being measured. Consider measuring the output of a remote keyless entry transmitter. This device outputs a pulse-modulated RF signal with a carrier frequency of 433MHz. This signal has a relatively narrow bandwidth about the carrier frequency. The information being transmitted is encoded in a 400 ms pulse pattern.

Two measurement scenarios are needed. The first is to characterize the RF signal. Parameters like frequency. Also, the shape of the RF envelope affects the purity of the transmitted signal. The second measurement would involve decoding the information content. Using an oscilloscope with a 20 Mega sample (MS) memory at a horizontal scale setting of 100 ms per division (1 second acquisition time), the sampling rate would be 20 MS/s. Figure 4 shows the two measurement processes for both the RF and Data decoding measurements.

Figure 4 Measurements on a remote keyless entry transmitter use an aliased signal to decode digital data.
Source: Art Pini

The traces on the left side of the screen show the RF measurements. The signal is acquired at 20 GS/s, and its leading edge is captured. The oscilloscope measures the RF carrier frequency at 433.9 MHz. The envelope of the RF carrier is extracted by applying the absolute value function, followed by a low-pass filter to create a peak detector. Trace F1 (bottom) shows the envelope. A copy (Trace F3) of the Envelope is also overlaid on a horizontally expanded zoom view (Trace Z1) of the leading edge of the signal. The envelope can be used to measure the envelope’s rise time.

The right side of the display shows the data decoding process. The entire data packet is acquired on a 100-ms-per-division horizontal scale. The sampling rate is 20 MS/s. The RF carrier is aliased down to 6.13 MHz as measured in parameter P2. The aliased frequency of the carrier is the result of mixing the twenty-second harmonic of the sampling rate with the 433.9 MHz carrier. The same envelope detection technique is applied to the entire packet, rendering the data content as an NRZ signal. Aliasing has enabled the acquisition of the entire signal data packet.

Conclusion

Aliasing in digital instruments is a digitizer characteristic that is becoming less frequent a problem due to improved instrument designs, including anti-aliasing filters, oversampling, and very long acquisition memories. Users should still be aware of aliasing to prevent errors that cost time and money.

Arthur Pini is a technical support specialist and electrical engineer with over 50 years of experience in electronics test and measurement.

Related Content

• Sampling and aliasing
• Using oscilloscope filters for better measurements
• Combating noise and interference in oscilloscopes and digitizers
• Building a low-cost, precision digital oscilloscope—Part 1
• Building a low-cost, precision digital oscilloscope – Part 2

The post Aliasing, the bane of sampled data systems appeared first on EDN.

It is 3-rd LCD panel in a month with the same issue, backlight stopped working, there was one resistor still measuring 270 Ohm so we know what it should be, all others are open circuit or in xx MOhm range. No signs of corrosion or overheating anywhere, just crappy components, never have seen this issue. It is planned obsolence or bad combination of materials in resistor. Share your experience with similar cases. submitted by /u/Al3x_Y [link] [comments]