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R&S has extended its BBA300 family of broadband amplifiers with single-band models delivering 500 W and 1000 W P1dB RF output power. The BBA300-DE500 and BBA300-DE1000 cover 1 GHz to 6 GHz without band switching, improving efficiency in automated test environments. Optional BBA-PK1 software for the 500-W model enables bias point adjustment to optimize either linearity for complex signals or pulse fidelity, while providing a tradeoff between output power and mismatch tolerance.

Well-suited for automotive, aerospace, and defense applications, the solid-state amplifiers offer high availability and robust operation under mismatch conditions. They generate high field strengths for component and full-vehicle testing, as well as high-intensity radiated field (HIRF) testing. The amplifiers support a wide range of modulation types, from standard amplitude and pulse modulation to complex OFDM signals.

To achieve high power density, the compact modular amplifiers integrate into 30U racks preconfigured for direct horn antenna mounting. To reduce RF losses at high frequencies, the RF output is positioned centrally within the rack, minimizing cable length to the antenna and improving overall link budget.

Learn more about the BBA-300 family of broadband amplifiers here.

Rohde & Schwarz 

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The TS1800 platform root of trust controller and TS50x secure boot controller expand Microchip’s TrustShield portfolio of post-quantum cryptography (PQC)-ready devices. These ICs address emerging cybersecurity mandates, including the European Cyber Resilience Act (CRA) and Commercial National Security Algorithm Suite 2.0 (CNSA 2.0), across data center, compute, defense, and infrastructure systems.

Designed for external platform root of trust in multi-component systems, the TS1800 provides secure boot, secure firmware updates, attestation, and certificate handling using hardware-accelerated PQC. An Arm Cortex-M4F processor operating at up to 192 MHz provides up to 2× the processing power of previous generations to support the increased computational demands of PQC workloads. The controller also supports Open Compute Project (OCP)-compliant implementations, enabling firmware integrity validation and lifecycle management.

The TS50x series provides PQC-based secure boot for systems that do not require the full OCP-based platform root of trust feature set offered by the TS1800. With a simpler architecture, it focuses on signature verification using both PQC and classical cryptography for firmware stored in SPI flash. The controller holds the main chipset in reset until verification completes. This hybrid approach enables retrofitting existing ECC-based designs with PQC.

TS1800 and TS50x controllers and evaluation boards are available as part of Microchip’s early adopter program. 

TS1800/TS50x product page

Microchip Technology 

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This engineer no longer has a bulbous monitoring device attached to his chest. He’s transitioned to a svelte successor, in the same location but this time placed subcutaneously.

Thanks to all of you who wrote in expressing concern and well wishes subsequent to the publication of my previous two posts in this series, focusing on my recent cardiac issues. I’m happy to report that I successfully made it through the 30-day regimen with a function-tailored smartphone in my pocket and a monitor stuck to my chest . I’m also happy to report that my cardiologist’s analysis of the collected data revealed no serious ongoing concerns. That said, I’m not yet completely “off the hook”, therefore the topic of today’s follow-up writeup.

What the 30-day results did reveal were a few brief episodes of tachycardia, i.e., elevated heart rate and intensity sequences, albeit with a still-regular cadence:

As my cardiologist explained (and I now paraphrase), my heart seemed to be trying to go back into irregular rhythm but (thankfully) didn’t succeed. As such, he was of the opinion that I still should proactively have a cardiac ablation, but I’ve declined that option, at least for now.

During my mid-November episode, while the bulk of my arrythmia rhythm was classified as atrial flutter, which has a near-100% success rate even after only a single ablation procedure:

my heart also occasionally transitioned into atrial fibrillation (AFib), whose single-procedure success rate is lower, due in part to the larger number of impulse sites that typically need to be severed (subsequent repeat procedures bolster the chances of a successful eventual outcome):

Instead, what I proposed (and he eventually agreed to) was a more conservative approach, at least initially. I’d remain on rhythm-stabilizing beta blockers. And he’d embed a miniature leadless cardiac monitor, with three-year operating life, subcutaneously in my chest to enable ongoing logging of any further heart rate abnormalities. He’d then automatically receive a report from the service provider each month. If there was no further detected AFib or atrial flutter after the monitor’s integrated battery eventually died, I could declare an “all clear”, with the now-inert monitor potentially remaining in me for the rest of my life. And if any recurrence of irregular arrythmia did occur, we could revisit the potential ablation scenario.

Tiny but mighty

The system I’m now artificially augmented with—just call me Steve Austin—is from Medtronic. Specifically, it’s the first-generation Reveal LINQ, which has been in widespread use for more than a decade at this point. At its nexus is the model LNQ11 ICM (insertable cardiac monitor), now in residence in my chest, which required only a local anesthetic (lidocaine) and sub-1 cm incision for installation, along with a couple of internal dissolvable stitches and some glue to temporarily hold the incision flaps together for the first two weeks while it healed.

The ICM has dimensions of approx. 44.8 x 7.2 x 4 mm, translating to (at ~1.3 cubic cm) roughly 1/3 the volume of a AAA battery, and weighs around 2.5 grams. Here are some stock shots:

Wireless diversity

The ICM communicates with a standalone AC-powered patient monitor which receives transmissions from the ICM and passes them along to a “cloud” server over a cellular data link:

Here are the meaningful perspectives of the outer packaging I received post-ICM installation:

Opening up the box, there was (obviously) no longer an ICM inside; it had already been relocated to my skin’s underside, at the left pectoral region of my chest, to be precise:

The patient monitor is variously described as needing to be no further than either 2 or 3 meters away (depending on the literature piece being referenced) from the ICM-toting patient in order to ensure reliable data transfers:

The system manual (PDF) accessible (along with other useful info) via the patient portal provides detailed information on the divers spectrum swaths used for various ICM-to-patient monitor and patient monitor-to-cloud functions, along with their associated modulation schemes. The companion ICM manual (PDF) translates these technical specifications into “for the masses” cautions and broader recommendations for cardiac monitor operation in EMI-rich environments (motors, arc welders, radio transmitters, etc.) along with the information you should share beforehand with MRI scanner operators as well as airport and other security personnel (I carry a Medtronic-supplied info card in my wallet for situations such as these).

Speaking of spectrum swaths, the FCC certification ID for the ICM is LF5MEDSIMPLANT1; I encourage you to check out the FCC site for more interesting information on the device, including a set of teardown images. Even more interesting info can be accessed by punching other FCC IDs, found on product labels both above and below this point in the writeup, into the independently developed and maintained FCC certification website search engine.  And further to the spectrum swath topic, I’ll note that Medtronic has subsequently introduced the LINQ II ICM, similar in size (45.1 x 8 x 4.2 mm) and per my online research making several notable enhancements to the first-gen implementation:

• Like the 30-day cardiac monitor I described in my previous writeup, it communicates with the data receiver device over Bluetooth low energy (BLE), not the proprietary protocols leveraged with the first-generation ICM. As such, again as with the 30-day monitor I previously used, it can connect to a conventional smartphone versus requiring my dedicated bedside patient monitor device.
• Its BLE and smartphone intermediary foundations also enable it to be remotely reprogrammed by the cardiologist for settings fine-tuning purposes, versus necessitating an office visit for the patient.
• Estimated battery life is now 4.5 years.
• And the LINQ II is FDA-cleared for pediatric use with patients 2 years and older.

Selective storage and transmission

My previous cardiac monitoring device was bulky and required recharge every five days or so. How on earth, then, does this comparatively tiny ICM run for 3 years on a much smaller and non-rechargeable cell? Selectivity is one key differentiator; while the prior cardiac monitor was constantly logging heartbeat information, the ICM (automatically, at least; keep reading) only captures a data sequence when it senses there’s a potential arrhythmia event occurring, and cloud-based AI algorithms further weed out “false positives” before passing the information on to the cardiologist.

The ICM only houses enough onboard storage for 27 minutes’ worth of this auto-logged information. It’s what’s known as a “loop recorder”, overwriting old data with new, operating under the assumption that the old data has already been transferred to the patient monitor. Yes, this means that, as with my CPAP machine, I also need to travel with the patient monitor and its AC power adapter.

What happens if I’m symptomatic, suggestive of an in-process cardiac event; palpitations, dizziness, light-headedness, etc.? The answer to that question depends on whether my patient monitor is nearby. You may have already noticed in the earlier set of photos that the patient monitor appears to consist of two pieces, with the smaller portion sitting atop the larger base unit. Kudos on your insight: you’re right:

If the patient monitor is nearby when you find yourself in distress, you can detach the “reader” portion (which, perhaps obviously, contains an embedded rechargeable battery), place it on your chest directly above the implant area, and transfer the captured and “flagged” data for analysis by the cardiologist (who can also proactively reach out to you for an ad-hoc transmission of this same way, by the way, if he or she sees something awry in the auto-captured monthly report data).

And if you’re away from your patient monitor? That’s where the pocketable “patient assistant”, accompanied in the following photos by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes, comes into the picture:

Place it on your chest atop the ICM, punch the “record” button, LED light-confirm that the two devices are communicating and, later, that a successful sample has been captured, and the next time you’re nearby the patient monitor it’ll be priority-tagged and transmitted. The ICM contains additional storage sufficient for 30 minutes total (variously segmented) of patient-activated recordings, beyond the earlier-mentioned 27 minutes of auto-logged data.

I’ll pass along any other notable aspects of my “bionic augmentation” experience via this blog if/as I encounter them in the coming months (and years). For now, I welcome your thoughts in the comments on what I’ve shared so far!

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

Related Content

• Wearables for health analysis: A gratefulness-inducing personal experience
• Cardiac monitors: Inconspicuous, robust data collectors
• Adventures with a remote heart monitor
• Heart rate monitor using a programmable SoC

The post Cardiac flutter(ing): Long-term monitoring appeared first on EDN.

Ultra-wideband (UWB) has moved well beyond research labs. Driven by IEEE 802.15.4z standardization and integration into smartphones from Apple, Samsung, and Xiaomi, UWB now underpins industrial real-time locating systems (RTLS), consumer keyless entry, and asset management platforms across multiple verticals.

For most of this adoption, time-of-flight (ToF) ranging has been sufficient, delivering approximately 10 cm accuracy in line-of-sight environments by measuring signal round-trip time. But system architects are increasingly moving to angle-of-arrival (AoA) techniques, which resolve the angular direction of a tag without requiring additional anchor nodes. AoA unlocks more efficient infrastructure layouts and opens new use cases in worker safety, autonomous robotics, and automotive access.

The shift exposes a hardware bottleneck that no amount of signal processing can fully compensate for: antenna isolation. AoA positioning relies on comparing the phase of a UWB pulse arriving at two closely spaced antennas.

If those antennas are mutually coupled—that is, insufficiently isolated—their signals contaminate each other. The resulting phase corruption introduces systematic angular errors that propagate directly into positioning accuracy.

Three design challenges facing UWB AoA antenna engineers

1. The –25 dB isolation threshold

Qorvo’s Application Note APH511—the widely referenced industry guide for AoA antenna integration—sets two non-negotiable requirements. Inter-antenna isolation must reach at least –25 dB across the full operating band, and physical antenna separation should be approximately 0.45 times the signal wavelength (λ).

For UWB Channel 9 (centred at ~7.987 GHz), that spacing equates to roughly 16.87 mm. Even at this theoretically optimal separation, raw isolation without dedicated decoupling structures typically falls short. The shortfall allows mutual coupling to corrupt the phase difference of arrival (PDoA) measurement on which AoA computation depends—and angular errors compound with distance.

2. Broadband impedance matching and pulse fidelity

UWB systems transmit sub-nanosecond pulses spanning hundreds of megahertz of bandwidth. An antenna that appears well-matched at a spot frequency can still distort pulse shape if its phase response is non-linear across the band.

Published time-domain evaluations indicate that group delay variation beyond approximately 1 ns degrades ranging accuracy even when return loss (S11) looks clean. Engineers must validate not just impedance matching, but pulse fidelity and group delay flatness—metrics that add complexity to an already demanding design process.

3. Size constraints vs. isolation performance

Industrial IoT tags, wearables, access cards, and consumer devices impose tight dimensional budgets. Conventional approaches to achieving strong inter-antenna isolation rely on enlarged ground planes or external RF filtering networks; both of which are incompatible with compact form factors. The result has been a persistent trade-off: high isolation or small size, but rarely both.

Chip antenna purpose-built for AoA

LK1820201 is an SMD chip antenna engineered specifically to address these barriers. Key specifications are summarized below.

Source: Leankon

Proprietary decoupling architecture

The central innovation is a proprietary decoupling structure that achieves inter-antenna isolation better than –25 dB between two co-located UWB antennas. In practical validation, a dual-antenna AoA array using the LK1820201 and its decoupling element measures –26 dB of isolation across the complete UWB Channel 9 band, confirming that performance holds across the full 6.0–8.5 GHz operating envelope, not just at a single center frequency.

This directly meets—and in practice exceeds—the Qorvo APH511 threshold, providing a solid electrical foundation for phase-coherent AoA computation.

• Ultra-low 0.5 mm profile

At 0.5 mm in height, LK1820201 is among the lowest-profile UWB antennas available in SMD chip format. This enables integration into slim wearables, access badges, compact industrial tags, and consumer devices without compromising mechanical design. Standard SMD reflow mounting eliminates the need for bespoke assembly tooling, reducing manufacturing entry barriers.

• Radiation pattern and power efficiency

Counter-intuitively for positioning applications, a lower peak gain paired with high radiation efficiency is generally preferred over a high-gain directional pattern. High efficiency distributes signal energy across a wide spatial angle, improving coverage at anchor installations and reducing dead zones for tags moving through complex indoor environments.

The antenna’s efficient radiation characteristic also reduces the transmit power burden on the UWB chipset—extending battery life in tags and wearables that must operate over weeks or months between charges.

Application areas

Centimetre-accurate UWB AoA positioning, enabled by high-isolation antenna pairs, is opening deployments across several industries.

• Industrial RTLS and worker safety: In manufacturing plants, logistics hubs, and construction sites, AoA allows a single anchor to resolve not just distance but the angular direction of a tag. This reduces the anchor infrastructure required for full coverage, lowering deployment cost for geofencing, collision avoidance, and emergency mustering systems.
• Healthcare asset tracking: Hospitals require continuous visibility into the location of mobile medical equipment—from infusion pumps to crash carts. UWB delivers the accuracy to track assets to the correct bay or room, without the ambiguity of Bluetooth RSSI-based systems.
• Automotive keyless access: Digital car key implementations use PDoA and AoA to determine whether a smartphone is inside or outside a vehicle—a security-critical distinction that RSSI cannot reliably make. Multi-channel support and high isolation performance are prerequisites for meeting the phase measurement accuracy demands of these deployments.
• Autonomous mobile robots: UWB AoA enables infrastructure-light follow-me navigation on autonomous mobile robot (AMR) platforms. By resolving both range and angle to a worker’s tag from a single onboard antenna pair, a robot can track a target in real time without requiring a fixed anchor network.

Design enablement and engineering support

Selecting a datasheet-compliant antenna is only the starting point. PCB stack-up decisions, ground plane geometry, feed trace routing, and antenna placement relative to metallic enclosures all interact with measured RF performance. Leankon supports the LK1820201 chip antenna with a design enablement program that covers:

• PCB layout recommendations optimized for isolation performance
• Antenna performance simulation services for pre-layout validation
• Mechanical design assistance for antenna placement within enclosures
• Fast prototyping services to accelerate design verification cycles
• Pre-test support for FCC, CE, and regional certification processes

This end-to-end support model reduces the engineering risk of adopting a high-performance UWB antenna and shortens the path from concept to production-qualified hardware.

Why AoA now

UWB angle-of-arrival positioning is a technically compelling evolution from range-only systems, but its precision depends fundamentally on solving the antenna isolation problem. For years, that barrier has limited AoA adoption to designs with generous PCB real estate or expensive external RF filtering.

Chip antenna changes the equation. By achieving better than –25 dB isolation from a 0.5-mm SMD package, supporting all major UWB frequency allocations from a single component, and simplifying BOM complexity for global deployments, it removes the principal hardware barrier to AoA in compact, cost-sensitive devices.

For IoT hardware engineers, RTLS platform developers, and device makers targeting precise indoor positioning, this antenna represents a technically meaningful step toward aligning hardware capability with the precision that modern UWB applications demand.

Chris Zhong, engineering manager at Leankon, leads the global antenna R&D team, overseeing both RF and mechanical design. With over 15 years of antenna design expertise, he specializes in 4G LTE, Bluetooth, 5G and mm-Wave, UWB, NFC, LoRa, and Wi-Fi technologies.

Related Content

• Trends in UWB technology
• Inside UWB design: A tutorial
• UWB simplifies portable design
• Ultra-wideband antenna arrays–The basics
• Ultra-Wideband Radar in Healthcare: A New Era of Non-Contact Sensing and Monitoring

The post UWB: Why angle-of-arrival positioning hinges on antenna isolation appeared first on EDN.

Op amps tend to make analog design easy. Maybe sometimes too easy?

Don’t get me wrong.  I like operational amplifiers.  Some of my best friends are op amps.  They embrace such a wide range of varied capabilities, including low noise, high power, micropower, zero-drift, RRIO, high speed, etc., that they’re easy to love.  They tend to make analog design easy.  Maybe sometimes too easy?

Wow the engineering world with your unique design: Design Ideas Submission Guide

This design idea applies the ΔVbe temperature measurement principle to make any cheap 3¾ digit digital multimeter with a 300mV range into an accurate, linear, 0.1°C resolution digital thermometer.  As a (hopefully) entertaining exercise, this time it does it without incorporating any op amps.  Here’s how it works.

ΔVbe temperature measurement is described and applied in an app note written by the famed analog design guru Jim Williams. See page 7 (PDF). Williams explains that the ΔVbe/°C effect depends solely on the ratio of applied currents, independent of their absolute magnitudes, and has an amplitude of 198μV per °C per current decade.  198uV=1V/5050, so 198μV/°C per current decade works out to ΔVbe/°C = Log10(Current-ratio)/5050.

Therefore, for any chosen ΔVbe/°C, the required Current-ratio = 10^(5050 Vbe/°C). So if we want ΔVbe/°C = 1mV, the solution couldn’t be simpler.  We “only” need to set Current-ratio = 10^(5050 * 1mV) = 10^(5.050) = 316,228:1.

Yikes!

The challenge, of course, is to achieve such an extreme current ratio. If the high side current were 1mA, then the low side would have to be very (very!) low indeed…like 1mA/316,228 = 3.2nA low.  This would involve Gohm current-setting resistors and circuit impedances in the multi-Mohm range.  So it’s not so simple after all and in fact is very likely impractical—without op amps, that is.

But consider this.  If it’s impractical to get enough ΔVbe signal from a single junction, why not wire N junctions in series and let their signals add up?  For example, if N = 5, then to get the required 1mV/5 = 0.2mV, we only need Current-ratio = 10^(5050 * 200uV) = 10^(1.01) = 10.23.  That ratio is highly practical.  It’s exactly what Figure 1’s circuit does, in fact:

Figure 1 Switch U1a and current mirror Q2Q3 apply an excitation current ratio of 10.23:1 to the 5 sensor transistor series array.  This creates a 5 x 200uV/°C = 1mV/°C AC signal synchronously rectified by U1c.

Circuit details include the D1R6 dummy load that serves to balance the currents passed by the two sides of the U1a switch, thus equalizing Ron voltage losses.  Current mirror aficionados (I’m looking at you, Ashu) will probably wonder how the Q2Q3 mirror, consisting of unmatched transistors with no emitter degeneration, can possibly have an accurate gain ratio?  The answer, of course, is: it doesn’t.  But that’s okay. It doesn’t need one.

Remember that Jim Williams said that the ΔVbe/°C effect depends solely on the ratio of applied currents, independent of their absolute magnitudes.  So the mirror’s gain can vary as it pleases without significantly affecting temperature measurement accuracy. Multivibrator U1b provides ~7kHz timing for synchronous sensor excitation and rectification with a ~33% duty factor.  This takes advantage of the 10x lower sensor array impedance at the high-current side of the excitation square wave.

If a more usual temperature readout in Celsius rather than Kelvin is desired, just plug the minus lead of the DMM into Figure 2 instead of ground, to offset 273K to 0°C:

Figure 2 This precision voltage reference converts Kelvin to Celsius.

Speaking of variations that don’t spoil accuracy, the V+ supply, for example, can vary from 5 to 6 volts without affecting accuracy.  Output impedance is roughly 2k, so variation of output loading by a typical 10M DMM input won’t impact accuracy, either. Who needs op amps, anyway?  (Not a serious question!)

Thanks, Jim!

Stephen Woodward‘s relationship with EDN’s DI column goes back quite a long way. Over 200 submissions have been accepted since his first contribution back in 1974.  They have included best Design Idea of the year in 1974 and 2001.

Related Content 

• ΔVbe + DMM = Celsius, Kelvin, Fahrenheit, and Rankine thermometer
• BJT is accurate sensor for absolute temperature in Kelvin and Rankine
• Temperature compensation with a simple resistance temperature detector
• A temperature-compensated, calibration-free anti-log amplifier

The post ΔVbe thermometer outputs 1mV/°C without calibration or op amps appeared first on EDN.

Misbehaving buffer pointers, whose effects threatened to create a fatal project setback, were identified via a clever software subdivision technique.

In the 1990s, I was working as a motion control engineer for the Giant Meter Wave Radio Telescope Project (GMRT). The radio telescope consists of 30 giant meter wave antennas, each a parabolic dish 45 meters in diameter. The motion control electronics (i.e., the control computer and power electronics) were located inside a control room within the supporting tower below each antenna. The servo computer received motion control coordinates from a master computer situated in a central building via an optical fiber link.

Do you have a memorable experience solving an engineering problem at work or in your spare time? Tell us your Tale

After the first two prototype antennae were commissioned, the radio astronomers started using them for their observations. Whenever a celestial object is to be observed, the antenna has to move in opposition to the earth’s motion in order to remain focused on the object under observation. After a few weeks I received a phone call from the security guard manning one of the antennas. “The antenna was dancing madly,” the guard said. “I had to shut off the power supply! Please come down and investigate.” I reached the project site only to discover that the problem could not be reproduced.

This story repeated itself every few days, for both antennae in turn. The control system development team blamed the “dancing behavior” on erratic fluctuation with the rural electricity grid, suggesting that “if your grid bus voltage dances madly, the antenna will do the same.” However, I couldn’t “buy” this explanation. If it had been true, a repeated power on/off sequence could have reproduced the problem. But it didn’t.

The developers then handed me a 2,500 page printout of the source code, which was written in Turbo Pascal. Since I instead suspected a control software bug as the culprit, I was tasked with finding it. But how could anyone debug such a voluminous amount of software, written by multiple development team members, none of them myself? And what debugging tools could I use to track down an issue that occurs only once a few weeks? The situation appeared hopeless.

I decided to make use of the three LEDs located on the front panel of the servo computer, Each LED can have three states: on, off and blink. So we have cube of three combinations, 27 possible combinations in total. I divided the program into 27 different parts. A specific combination out of the 27 was therefore illuminated on the LEDs each time the associated code portion was being executed. I then asked the security guard to record the LED pattern being displayed every time the antenna was “dancing”, before he shut down power.

After only two or three iterations of the “dancing antenna event”, the culprit area of the program was identified, located within a two-page portion of the original 2,500-page source code printout. I was admittedly thrilled at the seeming magic of my debugging technique. The culprit program segment implemented a 128 byte circular communication buffer. When the master computer was issuing commands, the buffer would store them until the servo computer could execute them. Occasionally, however, the motion trajectory was so fast that the buffer would also rapidly begin to fill up.

In the worst-case scenario, the entire 128-byte buffer would become full. The buffer management routine maintained two pointers: a read pointer to the next command to be executed and a write pointer to the last location written. The pointers normally circularly wrapped around after reaching the 128th location. However, in this particular situation the read pointer was erroneously advancing to an invalid 129th location instead. No wonder it would then read a junk motion control command, resulting in the antenna “dancing” erratically!

I corrected the bug, to the delight of the other team members. The antennae had been running the risk of falling down during the “dancing”, leading to a fatal setback for our project. After more than three decades of development work, I have accumulated enough experience (and experiences) to come up with “life-saving” countermeasures for bugs such as these:

• Motion control software needs to carry out a “sanity check” before executing any motion command. Such a huge amount of inertia cannot be given a violent added acceleration beyond a reasonable threshold. Any command breaking this rule can be safely ignored, with an error subsequently flagged.
• A simple checksum for every command bit stream could have identified a “junk motion command” situation such as the one described here.

Our project received a prestigious IEEE Milestone Award a few years ago. Needless to say, if this difficult-to-find bug had not been identified and rectified, the project would not have even seen the light of the day, far from basking in the global good reputation it has achieved over the years among the international radio-astronomer research fraternity.

Vishwas Vaidya is a graduate of the Indian Institute of Technology in Delhi, India. Currently, he is self-employed as an engineering consultant and industry faculty member in the field of embedded systems for global automotive clients and high-repute academic institutions. Vishwas’ articles and research reports have appeared in many worldwide engineering publications.

Related Content

• Getting real with the real-time clock
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• The world’s largest telescope under construction; unironically called Extremely Large Telescope

 

The post The curious case of the dancing antennae appeared first on EDN.

The concept of the “smart factory” has evolved significantly over the past decade. Early industrial AI deployments, often categorized as Industry 4.0, focused on centralized analytics. This typically involved collecting data from machines, transmitting it to the cloud, and generating insights for later action.

While useful for optimization and reporting, that model is no longer sufficient. What’s changing now is not just where AI runs, but how it operates—shifting from centralized analysis to systems that can perceive, decide, and act in real time within the physical environment.

Today’s factories demand intelligence that operates in real time, directly at the point of action. Whether detecting defects on a production line, coordinating robotic motion, or identifying safety hazards, AI is increasingly expected to function as an always-on, embedded capability within industrial systems.

This shift marks a broader transition in smart factories, from traditional edge AI toward more contextual awareness and autonomous operation: systems that not only analyze data, but perceive, decide, and act within the physical world. While the promise is substantial, realizing it introduces a new set of technical challenges that require purpose-built solutions.

Why edge AI Is moving closer to the machine in smart factories

Several converging forces are pushing AI workloads out of centralized infrastructure and toward the factory floor, where real-time interaction with physical systems is required.

Latency is among the most critical. In applications such as robotics, inspection, and safety monitoring, even small delays can result in defects, downtime, or safety risks. Round-trip communication to the cloud is often incompatible with these requirements. This is further compounded by the fact that many industrial environments operate with constrained, segmented, or variable network connectivity, making consistent low-latency cloud access difficult to guarantee.

Data volume is another key driver. Modern industrial systems generate vast streams of multimodal data—high-resolution video, audio signatures, vibration patterns, and increasingly, tactile inputs. Transmitting all of this data offsite is not only expensive but also unnecessary. In most cases, only a small fraction of events—such as anomalies, defects, or threshold violations—require action, making local inference far more efficient.

Figure 1 The transition from centralized AI to edge AI represents a fundamental shift in industrial computing. Source: Synaptics

Security and data sovereignty further make this trend important. Manufacturing processes and operational data are highly sensitive, and many organizations prefer to keep raw data within controlled environments.

The emergence of physical AI

On top of those factors, as AI moves closer to machines, its role is expanding. Instead of simply classifying or predicting, systems are beginning to interact with their environments in more dynamic ways.

This is the essence of physical AI in industrial systems, where they can:

• Interpret complex, multimodal sensory input in real time
• Adapt to changing physical conditions
• Execute actions with precise timing and coordination

Figure 2 The edge AI-enabled systems are now interacting with their environments in more dynamic ways. Source: Synaptics

Consider robotics as a leading example. Advances in tactile sensing now allow robotic systems to “feel” objects, adjusting grip force based on material properties. In one recent deployment developed with our partner Grinn, a robotic hand integrates distributed touch sensing with embedded machine learning, enabling nuanced manipulation of objects ranging from fragile materials to rigid components.

Such capabilities represent a shift from scripted automation to adaptive, context-aware behavior, bringing machines closer to human-like interaction with the physical world.

Key challenges in deploying edge and physical AI

Despite the momentum, implementing AI at the edge, and especially physical AI, presents several challenges.

1. Balancing performance and power

Industrial AI systems must operate continuously, often in constrained thermal and power environments. Unlike data centers, where peak performance is the primary metric, factory deployments prioritize sustained performance per watt.

Always-on workloads, for instance, predictive maintenance or safety monitoring, require efficient architectures that can run continuously without excessive energy consumption.

2. Managing workload diversity

Industrial AI is inherently multimodal. A single system may combine:

• Vision for inspection
• Audio for anomaly detection
• Vibration analysis for predictive maintenance
• Sensor fusion for robotics and control

These workloads have different computational characteristics, making it difficult to rely on a single type of processor. Increasingly, heterogeneous architectures that combine CPUs, GPUs, NPUs, and specialized sensors are required to efficiently handle diverse tasks.

3. Ensuring long-term reliability

Industrial systems often remain in operation for years or even decades. This creates unique requirements around:

• Silicon longevity and availability
• Stable software ecosystems
• Predictable behavior across revisions

Frequent hardware changes or software incompatibilities can disrupt operations and increase lifecycle costs.

4. Addressing model drift and lifecycle management

Unlike controlled lab environments, factories are dynamic. Lighting conditions change, materials vary, and equipment degrades over time. These factors can lead to model drift, where AI performance degrades after deployment.

Addressing this requires:

• Continuous monitoring and validation
• Local recalibration capabilities
• Secure, manageable update mechanisms

AI in industrial environments must be treated not as a static feature, but as a lifecycle-managed subsystem.

5. Integrating compute and connectivity

As systems become more distributed, the interaction between compute and connectivity becomes critical. Many manufacturers still rely on separate vendors for processing and wireless communication, leading to integration challenges and fragmented support models.

In physical AI systems, high-bandwidth, low-latency data movement between sensors, processors, and actuators is essential for safe and reliable operation.

The role of Wi-Fi 7 and next-generation connectivity

Connectivity is often a critical enabler of physical AI in smart factories, where real-time coordination between distributed systems depends on low-latency, high-reliability communication. As industrial systems scale in complexity and device density, traditional wireless technologies struggle to meet performance requirements.

Advancements in Wi-Fi and Bluetooth are addressing this, but wireless connectivity can no longer be viewed as a standalone, discrete capability. Without this level of connectivity, many physical AI use cases, particularly those requiring coordination across multiple systems, are not feasible.

There is a growing need, and clear benefits, in integrating processing and connectivity. This helps reduce system complexity, improve reliability, strengthen security, and simplify development for design teams.

Bringing together connectivity and processing changes how design decisions are made early in the product lifecycle. When core system functions work together, teams can simplify architecture choices from the outset and reduce the number of variables that typically slow progress.

Integrating connectivity and compute has benefits beyond the engineering and manufacturing phase. Over the lifetime of a product, integration helps reduce power consumption, lower device weight, and decrease overall system cost. At scale, even small reductions in size, mass, and power can translate into meaningful savings across production, shipping, and years of deployment.

Of course, wireless performance, range, and reliability are still critical in their own right. While existing Wi-Fi and Bluetooth standards have advanced the state of wireless connectivity, the emergence of Wi-Fi 7 introduces capabilities that enable more scalable and deterministic edge AI, supporting higher device densities and more predictable low-latency communication in smart factory environments.

• Multi-link operation (MLO) allows devices to transmit data simultaneously across multiple frequency bands. This provides redundancy and helps maintain consistent, low-latency communication even in environments with interference or congestion.
• Wider channel bandwidth (up to 320 MHz) supports high-throughput applications such as machine vision, where large volumes of image data must be transmitted quickly and reliably.
• Higher spectral efficiency (via 4K QAM) enables more devices to share the same wireless spectrum without degrading performance, an essential feature as industrial systems scale.

Toward a new system architecture

The convergence of edge AI, physical AI, and advanced connectivity is reshaping how industrial systems are designed, requiring more integrated and system-level approaches.

Some guiding principles to consider in developing such intelligent deployments are:

1. Start with system constraints

Rather than beginning with AI models, successful deployments start with system-level requirements:

• Latency and timing constraints
• Power and thermal limits
• Reliability and safety considerations

These factors should guide architecture decisions, including silicon selection and model design.

2. Embrace distributed intelligence

Instead of centralizing all processing, intelligence should be distributed across the system:

• Sensor-level processing for early data reduction
• Edge inference for real-time decisions
• Connection to cloud-based training and optimization for continuous improvement

This layered approach balances performance, efficiency, and scalability.

3. Design for multimodal integration

Physical AI systems rely on combining multiple sensing modalities. Architectures must support efficient data fusion and coordination across these inputs.

4. Treat AI as a lifecycle capability

Deployment is only the beginning. Ongoing monitoring, updates, and optimization are essential to maintaining performance over time.

The path forward

The smart factory is no longer defined solely by automation, but by intelligence embedded throughout the system, enabling decision-making that operates in real time, it adapts to its environment, and interacts with the physical world.

This transition from centralized AI to edge AI represents a fundamental shift in industrial computing. Performance and accuracy are still important, but what matters most is whether AI can operate reliably under real-world constraints: continuously, efficiently, securely, and in close coordination with physical processes.

Advances in heterogeneous computing, integrated connectivity, and open software ecosystems—as evidenced by AI-native platforms such as the Synaptics Astra Platform—are enabling this shift.

As these elements come together, the factory floor is becoming not just automated, but perceptive and adaptive, comprised of increasingly autonomous systems that do more than execute tasks; they understand context and respond accordingly.

Neeta Shenoy is VP of marketing at Synaptics.

Special Section: Smart Factory

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The post From edge AI to physical AI in smart factories: A shift in how machines perceive and act appeared first on EDN.

This audio-capture computer peripheral contains an integrated-transistor pickup capsule and a hunk of metal.

Back in November 2022, EDN published my introductory tutorial on standalone microphones—single- vs. multi-element, electret condenser vs. dynamic (including the associated necessity-or-not of a separate preamp), and analog vs. digital interface (and variants of each)—along with a separate piece on system-integrated mics a couple of months later.

I followed up those conceptual pieces with a USB-interface mic teardown in October 2023. And in both standalone-mic coverage cases, I mentioned (among others) one other USB-interface product, Blue’s (now Logitech G’s) Snowball, two examples of which were in my possession.

The Snowball, which supports both omnidirectional and cardioid pickup patterns, remains on my teardown pile. Stay tuned; it’s supposedly based on dual 14-mm electret condenser capsules, although there’s some controversy here, which I hope to sort out by putting my own eyes on the situation.

What we’re taking apart today is its spherical “little brother”, the cardioid-only Snowball iCE, which comes in both black and white color variants. I’ll start with some stock shots of my black-color ones, one of which I’ll be disassembling (non-destructively, hopefully).

Mine were a $40 (post-20%-off promo discount) two-pack ($20 each) bought from Woot in early 2024. Woot’s posting included a few other stock images I thought you’d find interesting.

Having a ball

While the mics themselves were brand new, their blank-cardboard and scant bubble wrap on-arrival packaging was definitely not retail-grade.

This last shot, along with others that follow it, as usual includes a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes.

I’ll start with the “extras”; a modest-but-functional tripod stand that screws into the mic underside, along with a legacy USB-A, to mini-USB cable and a sliver of literature.

Now for our dissection patient. Front:

Left side:

Rear, showcasing the aforementioned mini-USB connector (when’s the last time you saw one of those?) leveraged for both power and digital audio transfer purposes:

Right side, completing the circle:

And, last but not least, the top:

And bottom, showcasing the “adjustable desktop stand” mentioned in one of the earlier stock images (and implemented via a swivel mount in the microphone, mind you, versus anything to do with the stand itself):

For those of you curious about what the sticker circumnavigating the mic says, here are four consecutive segment snapshots for you to verbiage-glue together in your mind.

Severing the sphere

And now to get ‘er apart. In the earlier rear view, you might have noticed what looked like four screw holes, one in each corner. Kudos: you were right. It took me a bit of wading through my screwdriver collection to find one that:

• Had the right screw bit tip type-and-size
• With a bit that was both narrow enough to fit within the hole and
• Long enough to reach the screw heads deeply embedded inside

At that point, I expected the two halves of the sphere to neatly detach. But no. The previously mentioned sticker was still holding them together. There were two stickers, actually, as it turns out; the smaller one communicated device-specific info such as the serial number.

While the larger one handled the two-halves adhesion duties:

After I peeled it off, I thought its underside looked nifty and decided to share it with you, too.

And now the two halves of the sphere neatly detached:

FETalistic

Let’s first look at the moveable mount that fell out when the halves separated.

I trust many of you have already guessed that the red-and-black cable harness still connecting the two halves, which I promptly detached, is for the red LED. It only references the presence (or absence) of power to the microphone, by the way; there’s no integrated mute switch or any other reason for the LED to blink or otherwise communicate status.

There’s a notch in the internal assembly’s PCB that normally slots into a bracket at the inside back half of the microphone. With the two halves detached, the PCB slides out straightaway.

Assembly front view first:

Blue-now-Logitech claims that the 14-mm element is a “custom cardioid condenser capsule designed to deliver clear audio for recording and streaming, providing a significant upgrade over standard built-in computer microphones”. Marketing blah blah blah. Admittedly, it does review well, particularly considering its economical price tag. But its notable (IMHO) aspect, which I came across in my research, courtesy of a blogger who upgraded his, is its silicon integration:

The capsule in the Snowball is a 14-mm electret with an in-built FET that bears a striking resemblance to a JLI-140A-T. It uses a three-wire connection to the mic’s PCB, one each for the FET’s drain and source, and one for gate/ground. This means any electret with an in-built FET with all three pads brought out should work just as well (emphasis on “should”).

The fundamental purpose of the FET (alternatively a vacuum tube in some designs) is for impedance conversion and associated signal gain, thereby rationalizing why one well-known external mic preamp line is branded the “FetHead”. This thread on the Electrical Engineering Stack Exchange site gives a nice summary, complete with schematics and a conceptual diagram.

Heavy metal

Now for the left-side perspective:

Normally, when I see a hunk of metal, I assume that at least one of its primary purposes is to act as a heatsink. Not in this case. It just adds “heft” to the Snowball iCE, holding it in place on the user’s desktop (in partnership with the rubber-tipped stand “feet”) and suppressing ambient vibrations from being picked up by the capsule (along with the flexible rubber mount that mates it with the rest of the assembly). Here’s a bottom-side view, further showcasing the “hunk of metal”:

Back to the side views, next of the back of the assembly (with the mini-USB connector obscured by the ever-present penny, apologetically):

And finally, the right side:

Now for the perspective you all care about, that of the assembly-including-PCB topside:

Zooming in on the PCB itself, and after disconnecting the capsule cable harness:

The dominant IC on the landscape, toward the center of the PCB, is (unsurprisingly, given the mic’s digital output) the audio ADC-plus-USB interface device, C-Media Electronics’ CM6327A. This chip also embeds an I2C interface, harnessed in communicating with the Fremont Micro Devices  FT24C02A 2 Kbit serial EEPROM in the lower left corner (presumably housing system firmware).

In the spirit of thoroughness, and in closing, let’s take a peek at the PCB underside:

There’s nothing there that I can discern, other than test points, solder blobs and traces. In the interest of hopefully preserving mic functionality subsequent to re-assembly, I won’t proceed further with the dis-assembly. Sound off (bad pun intended) with your thoughts in the comments, please!

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

Related Content

• Microphones: An abundance of options for capturing tones
• Microphones: On-PCB options for catching tones
• Disassembling an in-line microphone preamp
• Checking out a USB microphone

The post The Blue (now Logitech) Snowball iCE: This mic sounds nice appeared first on EDN.

The science of pH measurement has progressed from the crude color changes of lichen-based litmus paper to the precision of modern electronic meters. What began as a qualitative test has become a cornerstone of quantitative analysis, enabled by advances in electrode chemistry, signal conditioning, and digital display technology.

Today’s pH meters—combining robust sensor design with microcontroller-driven accuracy—are indispensable not only in semiconductor fabrication and pharmaceuticals but also in agriculture, aquaculture, food safety, and even everyday aquarium care. This evolution from natural dyes to digital readouts highlights how engineering ingenuity transforms simple chemical principles into reliable, scalable instrumentation across diverse fields.

Figure 1 The demo shows an advanced pH meter in operation. Source: Labo Hub

Understanding pH meters and their components

So, pH meters are electronic devices designed to measure the acidity or alkalinity of an object by detecting the voltage produced by a specialized sensor. They offer greater precision than pH paper or visual indicators, providing digital or analog readings that represent the hydrogen ion concentration in a sample.

A complete pH measurement system generally includes three essential components: a pH measuring electrode, which features a glass bulb highly sensitive to hydrogen ions; a reference electrode, which maintains a stable, known voltage; and a high-impedance meter, which amplifies and interprets the millivolt signal.

In modern applications, these components are frequently integrated into a single “combination electrode” for convenience and to enable measurements in smaller sample volumes. The pH electrode behaves like a tiny, ion-sensitive battery, producing a voltage that varies with the hydrogen ion activity across the glass membrane, while the reference electrode remains constant and serves as a stable comparison point.

In other words, the glass-electrode method works by comparing the voltage generated between two electrodes: the glass electrode and the reference electrode. The known pH of an internal reference solution is established, and the difference in potential between the two electrodes is measured.

This potential arises because the thin glass membrane of the electrode allows hydrogen ions to interact with a hydrated gel layer on each side, creating an electromotive force proportional to the difference in pH between the internal solution and the external sample. This thin barrier is known as the electrode membrane.

Put simply, the glass electrode is designed to generate an accurate electromotive force that reflects pH differences through the surface ion exchange, while the reference electrode is engineered to remain stable and unaffected by pH, serving as a reliable comparison point.

Figure 2 here is an educational pH sensor suitable for laboratory experiments and demonstrations traditionally performed with a pH meter. Source: Vernier

It is worth noting at this point that a pH electrode, a pH sensor, and a pH meter are closely related yet distinct components of pH measurement systems. The electrode serves as the sensing element, the sensor is the complete assembly that incorporates the electrodes and housing, and the meter is the instrument that amplifies, interprets, and displays the measurement.

In some modern designs, pH sensors also include integrated electronics that provide signal conditioning or temperature compensation, making them more versatile and easier to interface with digital instruments.

A brief note on the rise of ion-sensitive field-effect transistor (ISFET) technology: Traditional glass electrodes rely on a delicate bulb, but ISFET technology replaces the glass membrane with a solid-state semiconductor. In an ISFET sensor, the gate of a transistor is exposed directly to the solution. As hydrogen ions accumulate on the gate surface, they alter the electrical current flowing through the transistor.

This “glass-free” design offers significant advantages for the food and beverage industry, as it removes the risk of glass fragments contaminating a production line. Moreover, because ISFET sensors are manufactured using silicon-based processes, they can be miniaturized into tiny, “lab-on-a-chip” devices for real-time medical monitoring.

Buffer solutions and electrode choices

Buffer solutions remain the backbone of accurate pH measurement, providing stable calibration points and resisting shifts when acids or bases are introduced. Electrode material selection is equally critical: glass electrodes deliver high precision but are fragile, plastic electrodes trade sensitivity for ruggedness in field or teaching labs, and PTFE electrodes excel in corrosive industrial environments with their chemical resistance.

Specialized designs such as the quick-response probe (QRP) extend performance with faster response times and robust construction, making them well suited for rapid testing scenarios.

Reference electrolytes and junctions

In any pH electrode system, the reference half-cell is just as critical as the sensing element. The reference electrolyte, commonly potassium chloride (KCl), provides a stable ionic environment that maintains electrical continuity with the solution being measured. The reference junction serves as the interface, allowing ions to flow between the reference electrolyte and the sample solution.

Junction design directly affects measurement stability: porous ceramic junctions are widely used for general laboratory work, polymer or plastic junctions offer durability in rugged applications, and PTFE junctions resist fouling in viscous or dirty samples. Advanced junctions, such as double junction designs, minimize contamination of the reference electrolyte and extend electrode life, making them especially valuable in industrial or biological environments.

Calibration and real-world pH values

Accurate pH measurement hinges on proper calibration, typically performed at 25°C using standard buffer solutions at pH 4.00, 7.00, and 10.00 to span the acidic, neutral, and basic ranges. These points anchor electrode performance across diverse applications.

In practice, pH values vary widely: drinking water sits near neutral (~7), milk is slightly acidic (~6.5), soft drinks fall between 2 and 4, seawater averages around 8, and soaps or detergents trend alkaline (9–11). Such examples underscore why calibration across multiple buffer points is essential; electrodes must remain accurate whether measuring beverages, biological samples, or industrial solutions.

Figure 3 Datasheet snippet presents the technical parameters of a pH electrode, a high-quality sensor for analyzing liquid solutions in industrial automation, with applications spanning chemical processing, petrochemicals, semiconductors, biotechnology, and wastewater treatment. Source: Supmea

Signal parameters: Understanding your pH probe

Whether you call it a probe, sensor, or electrode, your pH device relies on a measurable slope to convert electrical signals into pH values. At 25°C, a perfect electrode produces 59.16 mV per pH unit, but real-world sensors typically achieve about 98% of this efficiency.

As the glass ages or becomes contaminated, the electrode slope declines, signaling the need for cleaning or replacement. Moreover, the mV change per pH unit is temperature-dependent, varying with sample conditions.

Unlocking innovation: Crafting your own pH meter

Building a pH meter from scratch can be challenging, especially since manufacturers closely guard electrode designs. Yet, innovation thrives on resourcefulness. You do not need to master glassblowing to succeed—DIY kits provide the specialized components that make it possible to assemble an experimental meter and bring your project to life.

For those aiming to push their designs further, dedicated analog front-end (AFE) ICs open up exciting analytical-sensing applications. These chips streamline the process of handling delicate electrode signals, offering precision amplification, filtering, and conversion. By integrating AFEs, experimenters can transform a basic DIY setup into a robust instrument capable of reliable measurements across research, industrial, and educational contexts.

Figure 4 A legacy reference circuit from 2013 demonstrates a completely isolated low-power pH sensor signal conditioner and digitizer with automatic temperature compensation for high accuracy. Source: Analog Devices Inc.

Equally important are today’s temperature sensors, which ensure accurate compensation for thermal effects in pH readings, and solid-state pH sensors, which provide rugged, low-maintenance alternatives to traditional glass electrodes. Combined with the accessibility of general-purpose hobbyist microcontrollers and single-board computing platforms, makers now have a powerful ecosystem at their fingertips.

This synergy of specialized ICs, modern sensors, and affordable computing hardware empowers innovators to bridge the gap between DIY experimentation and professional-grade instrumentation.

Take the leap

Well, take the leap from experiment to innovation. The tools are here, the components are accessible, and the knowledge is within reach. Whether through DIY kits, AFEs, modern sensors, or hobbyist computing platforms, the path to building your own pH meter has never been more open.

Start experimenting today; turn curiosity into creation, and creation into innovation. Most importantly, embrace mistakes because they are the fuel for progress.

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.

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The post From lichens to digits: The evolution of electronic litmus paper appeared first on EDN.