Збирач потоків

Reach into your pocket and find your keys without looking. You just did something no robot can reliably do. You couldn’t see into your pocket, yet your fingers sorted keys from coins and lint, registered the weight and the cut edges, and closed around the right object. That was touch, quietly doing the kind of work that sight simply cannot.

Artificial intelligence (AI) has learned to see. It has learned to listen. Increasingly, it can reason, plan, converse, and generate sophisticated solutions to complex problems. Yet despite astonishing advances in foundational models and robotic computer vision, the next generation of intelligent machines remains constrained by a surprisingly simple limitation: ask a robot to pick up a paper cup, a grape, or a crumpled receipt, and the illusion breaks. It hesitates. It crushes the cup or drops the grape. The brain got smart far faster than the hands did.

This limitation isn’t because robots lack intelligence. It’s because they lack touch. For decades, AI has largely been about perception—understanding the world through images, language, and sound. Physical AI systems—that must operate and interact directly in the real world—reshape the challenge entirely. Instead of simply interpreting the world, intelligent machines must interact with it. They must grasp, manipulate, assemble, lift, carry, and collaborate efficiently and safely alongside people.

The gating modality for physical AI, therefore, is no longer being able to see. It’s being able to feel. Vision gave robots a view of the world. Touch, or multimodal sensing, is what will finally let them reach into it.

Figure 1 Physical AI doesn’t stop at perception. Multimodal sensing integrates vision, touch, and edge AI to enable robots to understand, adapt to, and safely interact with the physical world in real time. Source: Synaptics

The shift from perception to interaction

The last decade has been dominated by extraordinary progress in perception technologies. Computer vision systems now recognize objects with remarkable accuracy. Large language models (LLMs) can break down complicated tasks into logical sequences of actions. Robots increasingly understand what they are looking at.

Seeing and understanding, however, is not the same as taking action. A warehouse robot may correctly identify hundreds of products on a shelf yet struggle to remove a flexible package without crushing it. A humanoid robot may recognize a wine glass instantly but fail to grasp it securely and with the carefulness required of a fragile object.

To understand the shift from perception to interaction, it helps to contrast two flavors of autonomy by comparing autonomous vehicles with robotic manipulation. A self-driving car spends nearly all its effort avoiding contact. For an autonomous vehicle, touching something usually represents failure.

A robotic hand exists for precisely the opposite purpose. Its job is to make contact deliberately, continuously, and intelligently. Every successful grasp depends not simply on locating an object, but on understanding how that object responds when the instant contact is made. That information cannot be seen. It must be felt.

Vision plans, touch executes

Researchers increasingly describe robotic manipulation with a simple phrase: vision is for planning, and touch is for execution. Vision excels at global understanding. Cameras determine where objects are located, estimate pose, classify materials, and plan trajectories. They provide the strategic overview necessary for intelligent action.

But the moment robotic digits close around an object, vision begins to fail. The contact point disappears behind the hand itself. Cameras cannot observe friction. They cannot determine whether a paper cup is slightly damp, whether a cardboard box is heavier than expected, or whether a glass has begun slipping between two fingertips.

These are not visual problems. They are tactile problems. Humans solve them effortlessly. We tighten our grip on a slippery glass before it falls. We pick up an egg and instinctively apply exactly enough force—not too little, not too much. We rarely think about these abilities because our nervous system performs them automatically. For robots, they remain among the hardest problems in engineering.

Figure 2 Touch happens fast and intelligence happens faster. In just 70 milliseconds, multimodal touch sensing and edge AI transform raw tactile signals into intelligent grip control for safer, more dexterous robots. Source: Synaptics

The next robotics frontier: The science of the “slip”

Picture a condensation-slicked glass of water in your hand, eyes closed. As it begins to slide, you don’t need to see it to react. Your fingertips pick up a faint change in vibration and pressure, and your brain issues a “smart squeeze”—just enough extra force to arrest the slide without shattering the glass. The whole loop, from sensation to correction, runs below the level of conscious thought.

That loop is one of the hardest things to reproduce in a robot. Machines can compute trajectories, yet they remain clumsy about the feel of friction. The traditional answer was pre-programmed rigidity: apply a fixed grip force and hope the object—steel or sponge—cooperates.

Today’s physical AI system developers, however, look for something more subtle: the moment, invisible to the eye, when a stable hold begins to come apart. Contrary to intuition, objects rarely transition instantly from being secure to being dropped. Slip begins gradually.

At microscopic scales, every surface consists of tiny peaks and valleys. As force builds across a contact patch, the outer regions begin slipping while the center continues gripping. Engineers call this incipient slip, and detecting it early transforms robotic control. Instead of reacting after failure, robots can intervene before failure occurs.

This failure intervention requires two complementary sensing systems that mirror the layered design of human skin. A slower channel continuously measures pressure, shear forces, and load distribution across the fingertip. A much faster channel listens to high-frequency vibrations that signal the earliest stages of slipping. Together they create something remarkably similar to the layered sensing architecture found in human skin. One channel feels pressure, the other hears friction.

Only by combining both can a robot understand what’s actually happening at the point of contact.

Figure 3 Physical AI is a continuous feedback loop, not a one-time decision. True physical AI is possible when vision, touch, and edge intelligence work together in a continuous cycle of sensing, thinking, acting, and adapting. Source: Synaptics

Why touch changes everything

A useful multimodal sensing system must answer four fundamental questions.

First, has contact actually occurred? Millimeter-scale positioning errors matter. A robot must know the precise instant it touches an object rather than empty space.

Second, how much force is being applied? Picking up a steel bracket requires entirely different forces than handling fruit, laboratory samples, or medical devices.

Third, is the object beginning to slip? Perhaps the most important capability of all is detecting incipient slip—the microscopic changes that occur before an object falls.

Fourth, how does it feel? While the internet contains unlimited visual data, it contains almost no tactile experience data. No image can tell an AI application what wet glass feels like, how fabric stretches, or how friction changes as an object rotates within a grasp. The only way to learn these interactions is through physical contact. Touch provides ground truth: rich, labeled physical data about what real contact feels like, which is precisely what physical AI systems require.

Intelligence belongs at the edge

Touch also changes where intelligence must live. Unlike language models, tactile decisions cannot wait hundreds of milliseconds for cloud processing or centralized computation. If a glass begins slipping, every millisecond matters. Human grip corrections occur in roughly 70 milliseconds. A robot that must transmit raw sensor data to a central processor before deciding how to respond has already lost valuable time.

Instead, tactile intelligence must move to the edge. Each fingertip becomes an intelligent sensing node, interpreting pressure, force, and vibration locally before sending only meaningful events to higher-level control systems. This architecture mirrors biology: individual nerve endings process local information before communicating with the brain. The brain receives distilled information about events that matter rather than monitoring every sensory receptor continuously.

Physical AI increasingly requires the same distributed architecture. Vision determines the objective. Local intelligence manages contact. Higher-level AI coordinates the task. Touch increases the likelihood that each task is executed successfully.

Physical AI architecture: Reliability is more important than resolution

Much of today’s discussion around robotic touch as the path to dexterous machines focuses on sensor resolution. Resolution matters, but reliability matters more. Building an impressive laboratory demonstration that executes large numbers of touch events is relatively straightforward. Building a tactile sensor that performs accurately after millions of grasps in factories, warehouses, and hospitals is vastly more difficult.

Every tactile technology has weaknesses. Soft materials wear. Temperature changes electrical characteristics. Motor noise contaminates tiny signals. Elastomers gradually develop hysteresis, remembering previous deformations instead of returning instantly to baseline.

Optical sensors produce exceptionally rich data but require bulky camera systems. Magnetic sensing performs beautifully until exposed to external magnetic fields. Piezoelectric materials detect vibration extraordinarily well but cannot measure static force.

The challenge is not finding a perfect sensing modality. The challenge is engineering reliable systems that compensate for imperfections while surviving years of real-world use. Success requires expertise that extends well beyond robotics to materials science, signal processing, embedded intelligence, silicon design and manufacturing, firmware, and systems engineering, all working together as a single discipline. Ultimately, reliability—not raw sensitivity—is what determines whether physical AI scales beyond research laboratories.

Why this moment matters

The urgency surrounding physical AI development is driven by timing. In industrial applications, for example, humanoid robots are moving from prototypes toward mass commercial deployment according to industry research analysts. Conservative estimates from Goldman Sachs Research predict 1.4 million active humanoids will be working in global manufacturing, warehouses, and logistics by 2035. Morgan Stanley projects an even higher trajectory, citing massive production scale and adoption of 13 million service robots working along humans in the same timeframe.

The opportunity extends even further. Prosthetic limbs require tactile feedback to restore natural function. Data collection systems need tactile sensing to train future manipulation models.

Large-area robotic skin improves safe collaboration between humans and machines. Every application depends upon extracting trustworthy physical information from an inherently noisy world, and regardless of form factor, every Physical AI system eventually encounters the same bottleneck: hands or more precisely, contact.

Whether a machine uses five-fingered humanoid hands, industrial grippers, or specialized end effectors, it must regulate force, detect slip, and manipulate objects safely. Touch, therefore, becomes a foundational capability rather than an optional enhancement.

From digital to physical intelligence: Relying on touch for physical AI

History suggests that every major computing revolution has been enabled by a new interface. The graphical user interface transformed personal computing. The touchscreen transformed mobile computing. Touch now appears poised to transform robotics.

Just as computer vision unlocked autonomous perception, tactile intelligence will unlock autonomous manipulation. The companies that succeed in this technology space will not simply build better sensors. They will build complete sensing systems that combine advanced materials, embedded intelligence, signal processing, edge AI, robust manufacturing, and decades of experience extracting reliable information from imperfect physical environments. That combination—not any single breakthrough—will define the next generation of physical AI.

Physical AI is often described as giving machines a brain. In reality, we have spent the past decade building remarkably capable brains. What robots still lack is a nervous system: the ability to feel force, sense friction, recognize instability before failure, and transform contact into understanding.

The future of intelligent machines will not be determined solely by larger models or more powerful processors. It will be determined by whether those machines can interact with the physical world with the same confidence that humans have.

Vision gave AI the ability to understand the world. Touch will give it the ability to change it.

Satish Ganesan joined Synaptics in November 2019 and serves as senior VP and GM of Edge Interface & Sensing Division and Chief Strategy Officer. Prior to Synaptics, Satish served as chief product officer of Keyssa, a wireless startup focused on short-range connectivity. He also held several executive positions at Broadcom and Xilinx.

Related Content

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• From edge AI to physical AI in smart factories: A shift in how machines perceive and act

The post Missing sense: Why physical AI can see everything and feel nothing appeared first on EDN.

In booth B03 (Hall 16) at Power Electronics, Intelligent Motion, Renewable Energy and Energy Management (PCIM Asia 2026) in Shenzhen, China (26–28 August), Efficient Power Conversion Corp (EPC) of El Segundo, CA, USA — which makes enhancement-mode gallium nitride on silicon (eGaN) power field-effect transistors (FETs) and integrated circuits for power management applications — is showcasing its newest-generation GaN technology, demonstrating how its Gen7 GaN platform enables compact, high-performance power architectures for intelligent motion control and AI power delivery. The latest generation of eGaN devices combines ultra-low on-resistance, low switching losses and high-frequency operation to simplify system integration while improving efficiency, dynamic response, and thermal performance...

Epitaxial deposition and process equipment maker Veeco Instruments Inc of Plainview, NY, USA says that vertically integrated gallium arsenide (GaAs)-based quantum dot (QD) diode laser developer and manufacturer Innolume GmbH of Dortmund, Germany has purchased a GEN2000 molecular beam epitaxy (MBE) system to expand production for next-generation optical transceivers and silicon photonics applications. The investment supports the increasing demand for optical connectivity solutions for hyperscale AI infrastructure and cloud data centers...

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"Воїни та квіти" в Українсько-Японському центрі КПІ Інформація КП вт, 07/21/2026 - 16:00

This easy-to-build PWM source is a handy testing tool for hobbyists, technicians, engineers and other RC-based device developers.

I work at a university where I’m charged with designing and developing all manner of circuits and systems for students, faculty, researchers, and the like. These circuits include a diversity of projects relying on the standard remote control (RC) pulse-width modulation (PWM) signal for their operation.

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

This signal is generally defined as a TTL pulse train having a frequency of 50Hz and a width ranging from 1ms to 2ms. While these parameters can be programmed into most waveform generators, alternatively having a small, dedicated-function gizmo at the ready can prove to be really convenient! Figure 1 shows what I came up with.

Figure 1 The standard RC PWM signal used to control a myriad of devices is usually obtained using transmitters and receivers. This alternative RC servo operator circuit implements a convenient alternative jig that simplifies attaining various testing objectives.

Components C1, U1, and C2 comprise a low dropout 5V regulator. More generally, any 5V regulator IC can be employed, so long as the dropout is under a volt. Typically, RC servos run on 6V, though some will run on as many as 8V. The nominal input, indicated as 6V here, is forwarded to the servo output for convenient experimentation as shown.

The PWM source is based on Analog Devices’ nice-and-simple LTC6992-1. The trick is selecting the right resistor values, which are shown in Figure 1. Rather than generate a PCB, I soldered up the circuit using proto-board, through-hole components, and adapter boards for both ICs. If done on a PCB using surface-mount components, the entire circuit will take up only about 1 in2.

I don’t personally keep an RC transmitter and receiver combo in my lab. Even if I did, I’d need someone to manipulate the controls while I measured the values of interest on the system I’m developing. With this handy alternative circuit, I can run all my tests without human assistance, not to mention without the RC hardware. It has proven useful on numerous occasions.

Mike Potash is the electronics technician for the College of Engineering at Embry-Riddle University in Daytona Beach, Florida.

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The post Simple, compact circuit conveniently controls devices and systems appeared first on EDN.

Electrification and automation technology provider ABB of Zurich, Switzerland is acquiring Advantics of Saint-Genis-Pouilly, France, which provides silicon carbide-based power conversion solutions (integrating hardware, firmware and software to optimize performance across demanding applications including data centers, industrial microgrids, power generation and EV infrastructure)...

Epiwafer and substrate maker IQE plc of Cardiff, Wales, UK says that greater-than-expected trading in first-half 2026, with strong demand across all core segments, should yield revenue of at least £64m...

Siemens EDA has snapped up a San Diego startup to complement its digital design and implementation capabilities with AI-powered exploration for advanced system-on-a-chip (SoC) architectures. Precision Innovations, a privately held EDA company, helps semiconductor engineering teams evaluate architectural and design trade-offs early in the SoC design cycle for faster time to silicon.

At a time when chip designers face rapidly increasing SoC complexity and the need to explore new architectures faster and more efficiently, Precision Innovations’ AI-powered early design exploration technology is expected to enhance Siemens’ EDA portfolio by accelerating time to silicon and improving power, performance and area (PPA).

The acquisition aims to bring AI-driven chip planning to a much broader set of semiconductor teams. Source: Siemens EDA

Precision Innovations, founded in San Diego, California, in 2019, develops EDA software built on the open-source OpenROAD framework to help engineers evaluate design feasibility, reduce design iterations, and accelerate time to market. “Together, we can help designers leverage AI to evaluate thousands of design options earlier in the process and focus on innovation where it matters most,” said Tom Spyrou, CEO of Precision Innovations.

Precision Innovations will become part of Siemens EDA’s Digital Design Creation software team to complement the company’s end-to-end digital flow that spans architecture, implementation, and the full silicon lifecycle management of SoCs. “With Precision Innovations joining Siemens, we will expand our ability to support customers as they face rapidly increasing SoC complexity and the need to explore new architectures faster and more efficiently,” said Ankur Gupta, executive VP of IC portfolio at Siemens EDA.

The acquisition, subject to customary conditions, is expected to complete in the third quarter of 2026.

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The post Siemens EDA acquires SoC toolmaker Precision Innovations appeared first on EDN.

For coverage of all the key business and technology developments in compound semiconductors and advanced silicon materials and devices over the last month...

Навчання, стажування, обміни: КПІ розширює партнерство з Італією kpi пн, 07/20/2026 - 22:37

Linear variable differential transformers (LVDTs) stand out as one of the most reliable displacement sensors in modern engineering. By converting linear movement into proportional electrical signals, they deliver unmatched accuracy and repeatability across demanding environments—from aerospace control systems to industrial automation lines. Their rugged, non-contact design ensures long service life, while their ability to resist electrical noise makes them indispensable wherever precision and stability are paramount.

This article highlights how LVDTs operate, why they define precision sensing, and what are their key applications.

At the heart of an LVDT is a movable ferromagnetic core that shifts within a coil assembly, inducing voltage changes that correspond directly to displacement. This elegant mechanism defines LVDT’s reputation for precision sensing, enabling engineers to capture minute movements with exceptional accuracy and repeatability.

That reliability translates into critical applications: stabilizing aircraft control systems, guiding industrial automation processes, and supporting delicate medical instrumentation. By combining robust design with noise-resistant performance, LVDTs continue to set the benchmark for displacement measurement across diverse fields.

The electromagnetic elegance of LVDTs

Well, let’s dig deeper into the rabbit hole of LVDT design. Beyond the straightforward coil-and-core mechanism lies a finely tuned balance of electromagnetic principles that ensures linearity and stability across a wide measurement range.

Its differential signal output scales cleanly with displacement, canceling common-mode noise and making LVDTs remarkably resilient in harsh environments. This blend of simplicity and sophistication explains why they remain the sensor of choice when precision, durability, and repeatability are non-negotiable.

In practice, an LVDT is a widely used electromechanical transducer that converts the rectilinear motion of a mechanically coupled object into a proportional electrical signal. Its structure features a primary winding centered between two symmetrically spaced, identically wound secondary windings, forming the stationary coil assembly of the sensor.

The moving element is a separate tubular armature—called the core—made of magnetically permeable material. Free to slide axially within the hollow bore of the coil, the core is mechanically linked to the object under measurement. The bore provides ample radial clearance, ensuring no physical contact between the core and coil.

As the core shifts position, the magnetic coupling between the primary and each secondary winding changes, producing displacement-dependent voltage signals that deliver a precise electrical representation of the object’s position.

Figure 1 Pencil drawing illustrates the cutaway view of a basic LVDT. Source: Author

The primary winding is shown at the center of the LVDT. Two secondary coils are wound symmetrically on either side of the primary coil for short-stroke LVDTs, or concentrically over the primary coil for long-stroke versions. The two secondary windings are typically connected in a series-opposed (differential) configuration, ensuring that the output signal accurately reflects the core’s displacement.

In operation, the LVDT’s primary winding is energized by an alternating current of suitable amplitude and frequency, referred to as the primary excitation. The resulting electrical output is a differential AC voltage between the two secondary windings, which varies with the axial position of the core inside the coil. To make this signal more practical, it’s typically converted by electronic circuitry into a higher-level DC voltage or current.

Note that the two secondary windings are connected in series but wound in opposite directions—a series-opposed configuration that ensures the differential output accurately reflects the core’s displacement. For most industrial LVDTs, datasheets assume this series-opposed arrangement. In high-reliability sectors, however, the windings are often kept separate to enable more advanced error-correction techniques.

Figure 2 Schematic illustrates the circuit model of a basic LVDT. Source: Author

How an LVDT works: The silent precision of linear sensing

An LVDT operates as a specialized transformer with a single primary winding, two identical secondary windings, and a movable ferromagnetic core. The core slides axially within the assembly, linked to the monitored component by a push rod. When the primary winding is energized, its magnetic field induces voltages in the secondary coils.

Because these coils are connected in series-opposition, the output is zero when the core is centered at the null position. As the core shifts, magnetic coupling increases in one secondary and decreases in the other, producing a differential output voltage proportional to displacement. The direction of movement is indicated by the phase of the output signal relative to the primary excitation.

To summarize, an AC LVDT is a variable-reluctance transducer that operates by energizing a primary coil with a constant AC supply to induce a magnetic field. This flux is coupled through a moving armature to two secondary coils which are wired in series opposition.

When the armature is at the null position (dead center), the magnetic flux is distributed equally between both secondaries, causing their output to cancel out perfectly. However, as the shaft shifts closer to one coil, the energy in that coil increases relative to the other; by measuring this differential output, the sensor precisely determines the shaft’s position and direction within the tube at all times.

Figure 3 LVDT AC miniature free core position sensors monitor and track the linear motion or position of a target. Source: HGSI

LVDT mechanical configurations: The art of precision displacement

Modern LVDTs are available in three primary mechanical configurations to suit different integration needs. Free (unguided) armature LVDTs utilize a core that moves freely within the bore; because the core is not physically restrained by the sensor, it’s ideal for high-speed dynamic applications where near-zero friction is required.

Captive (guided) armature LVDTs feature internal bearings to maintain core alignment, preventing lateral wear and simplifying installation in industrial automation. Finally, spring-loaded (forced) LVDTs employ an internal spring to maintain contact with the specimen, making them the go-to choice for gauging and QC applications where a permanent mechanical link to the test object is not possible.

Each variety offers a unique trade-off between mechanical simplicity and operational precision.

LVDTs: Key features and operational benefits

LVDT stands as a premier electromechanical transducer, primarily distinguished by its friction-free operation; since the movable core does not touch the coil assembly, there is zero mechanical wear, ensuring an exceptionally long lifetime and high reliability. This physical decoupling allows for infinite resolution, enabling the detection of sub-micron displacements limited only by the signal-conditioning electronics, while its single axis sensitivity ensures that cross-axial movements do not interfere with measurement accuracy.

The design’s separable coil and core mechanism simplifies integration into complex machinery, and its environmentally robust construction allows it to thrive in extreme temperatures or high-vibration settings. Furthermore, LVDT provides remarkable null point stability for consistent zero-referencing, a fast dynamic response capable of tracking rapid changes in position. And, most importantly, an absolute output that retains the correct position data immediately upon power-up, even if the core moved while the system was inactive.

Support electronics: From AC physics to DC simplicity

Although an LVDT is technically a transformer, it requires specialized AC excitation—typically a few volts RMS at several kilohertz—rather than standard line power. Supplying this excitation is a key role of LVDT signal-conditioning electronics, which also convert low-level AC outputs into high-level DC signals, decode directional information from the 180° phase shift at the null point, and provide precise electrical zero adjustments.

These electronics are available in multiple forms, from chip-level components for OEM integration to modular boards and full laboratory instruments. Some LVDTs incorporate integral electronics for simplified “DC-in, DC-out” operation, but such self-contained units may be unsuitable for extreme environments where heat, vibration, or space constraints can compromise internal circuitry.

Figure 4 Datasheet excerpt shows an in-line amplifier transforming LVDT displacement into a directly proportional DC signal. Source: MTS

As a quick side note, LVDT is the technical acronym for the component itself—linear variable differential transformer—while LVDT displacement sensor is often used in industrial catalogs to distinguish it from other measurement technologies, such as capacitive or eddy-current sensors. This descriptive naming helps engineers quickly identify the device’s primary function as a tool for tracking linear position, even though both terms refer to the same electromagnetic hardware.

LVDT signal conditioning: Differential vs. ratiometric

Back to the signals, when choosing how to process an LVDT signal, the main difference lies in how the system handles fluctuations in power. Differential input AC signal conditioning is the more straightforward approach, where the sensor simply measures the difference in voltage between its two secondary coils to determine position. While effective, it has a notable weakness: if the input power (excitation voltage) fluctuates even slightly, the output reading will change as well, potentially leading to errors.

Ratiometric signal conditioning is a more sophisticated method designed to eliminate those errors. Instead of just looking at the difference between the coils, it compares that difference to the total sum of the voltage from both coils. By calculating this ratio, the system automatically cancels out any inconsistencies in the power supply. This makes ratiometric conditioning much more stable and reliable for high-precision tasks, as the measurement stays accurate even if the environment gets hot or the input voltage drifts.

Figure 5 This standalone universal LVDT signal conditioner supports any LVDT sensor with 4, 5, or 6 wires, as well as RVDT or 3-wire half-bridge sensors. Source: Lab Systems

It’s worth noting that an LVDT measures straight-line (linear) displacement, whereas a rotary variable differential transformer (RVDT) measures angular (rotary) displacement.

Precision in motion—From floor to space

From machine tools and robotics to aerospace and space-borne instrumentation, LVDTs remain the gold standard for precise, contactless position measurement. Their rugged construction, infinite resolution, and immunity to wear make them indispensable wherever accuracy must endure vibration, temperature extremes, or time itself.

For today’s makers and engineers, the story doesn’t end at the sensor—modern signal-conditioning ICs such as Analog Devices’ AD598 and Texas Instruments’ PGA970 bring excitation, demodulation, and calibration into compact solutions, while discrete signal-handling circuits continue to offer flexibility for custom designs and extreme environments.

And for those who venture into DIY territory, crafting the coil-core assembly demands patience, precision winding, and careful alignment—skills that embody the very discipline of engineering. Whether you’re building a robotic actuator, a precision test rig, or a satellite mechanism, LVDT proves that elegant physics and smart electronics can turn motion into measurable insight.

I’m ready for a larger displacement of detail in future posts, but at this time, that’s all. Take this knowledge forward—experiment, prototype, and innovate with LVDTs, integrated ICs, discrete signal handlers, and even your own hand-built coil assemblies. Push your designs from the lab bench to the factory floor, and even to orbit. The next breakthrough in precision engineering could start with your hands.

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 Precision in motion: The engineering value of LVDTs appeared first on EDN.

Worldwide end-user spending on AI models and platforms is projected to total $64 billion in 2026, up 63.4% from $39 billion in 2025, according to Gartner, Inc., a business and technology insights company. Spending on GenAI models is forecast to grow 117%, while AI platform spending will rise 36.9% in 2026.

“Enterprise AI budgets are coming under greater scrutiny, with increased focus on usage efficiency, cost control and measurable outcomes,” said Arunasree Cheparthi, Sr Principal Research Analyst at Gartner. “Spending is shifting toward providers who can demonstrate clear value across cost, latency, performance and reliability.

“This is giving an edge to providers that embed evaluation, cost transparency and usage tracking into customer workflows, making it easier to manage and optimize AI use. However, as spending becomes more usage-driven, providers face increasing pressure to demonstrate real adoption, sustained use and durable margins.”

This dynamic is accelerating growth in domain-specific language models (DSLMs) and specialized models, which are forecast to grow 210% in 2026 (see Table 1).

Table 1: Worldwide AI Platforms and Models End-User Spending Forecast, 2025-2026, (Millions of U.S. Dollars)

			2025-2026 Growth %)
Segment	2025	2026
Foundation Generative AI Models	11,438	23,356	104.2
DSLMs and Specialized GenAI Models	1,583	4,910	210
AI Application Development Platforms	6,885	9,541	38.6
AI Platforms for Data Science and Machine Learning	19,405	26,444	36.3
Total Market	39,311	64,252	63.4

 

“Over the long-term, the biggest winners will be vendors that help enterprises manage where and how AI is used across the business,” said Cheparthi. “As more models enter the market and usage-based pricing becomes harder to predict, buyers will turn to platforms that help them choose the right tools, monitor performance, enforce policy and keep costs under control.”

 

The post Gartner Forecasts Worldwide AI Platforms and Models Market to Grow 63% in 2026 Biggest Winners Will Be Vendors That Help Enterprises Manage Where and How AI is Used appeared first on ELE Times.

Bluetooth and Wi-Fi combine to untether smartphone-to-car connections…at least until the former’s battery drains, that is.

At this year’s beginning, EDN published my overview and hands-on impressions of Apple’s CarPlay and Google’s Android Auto standards, which enable a vehicle radio or automotive head unit to be a display and controller for an iOS or Android device, respectively. Historically, both protocols leveraged a wired USB-based interface between mobile device and vehicle to accomplish the integration objective, for latency, bandwidth and interference-prevention reasons. Newer vehicle models switch to Bluetooth (for initial discovery and connection) and Wi-Fi (for ongoing transmission) communication, enhancing convenience. And bridging these two approaches are wireless adapters that mate an untethered phone to the tethered vehicle.

At the end of that prior coverage, I wrote, “FYI, I’ve also got two single-protocol wireless adapter candidates sitting in my teardown pile awaiting attention.”

Today I’ll analyze the insides of a CarPlay wireless adapter, albeit not the “unit from the Luckymore Store” that I initially planned on disassembling. Next month, I plan to take apart a wireless adapter that implements the Android Auto protocol. And I’ll subsequently wrap up my dissections with a dual-protocol wireless adapter functionally akin to the one still in use in my wife’s Land Rover.

Supplier disappearance and switcheroo

Here again is the wireless CarPlay adapter I’d originally intended as today’s patient.

Right now, as I write these words, it lists for $44.17 on Amazon’s website. But when I bought it in mid-December, it was on sale for $2.99 plus tax, with free shipping. Although the merchant subsequently reported to both Amazon and me that it had shipped, it never actually arrived. And I wasn’t the only one that had this same underwhelming seller experience. Yes, I got my $3.12 back eventually.

I subsequently picked up this WOLIOS adapter (which apparently also came in white), well-reviewed but seemingly no longer available for sale as I write this, from Amazon’s Warehouse-now-Resale section for $11.85 in late February.

Gotta love these conceptual teardown images. And no, I don’t definitively know what “5G” means, either, although I suspect they’re referencing “5 GHz” Wi-Fi.

And, wrapping up the “stock content” suite, a promo video (which, alas, I can’t figure out how to embed) can be found here.

Overview introductions

Now for some real-life photos, as usual accompanied by a 0.75″ (19.1 mm) diameter U.S. penny for size comparison purposes, and of the product packaging first:

Next, what’s inside:

The red disc is an optional sticker for adhering the wireless adapter to the vehicle interior. The included USB-A (female) to USB-C (male) adapter for newer-vehicle use is a nice touch:

And now for our patient:

The hole in the center allows the status LED inside to shine through, as conceptually shown in the prior “stock” images. And the seam around the rim? I’m betting that’s our path inside.

Stubborn adhesive

Speaking of paths to the insides, let’s dive in.

Abundant exposure to my wife’s hair dryer on “high” (both temperature and fan speed) helped, but only a little. I resisted using my heat gun as it might have been melt-inducing overkill.

The last bit of the panel stubbornly refused to dismantle, so I eventually turned to a cutter tool.

Finally free, courtesy of a needle-nose pliers, albeit resulting in PCB mars.

This side’s much more interesting:

The embedded antenna at upper right is curious. As previously noted, two wireless broadcast links are necessary: 2.4 GHz Bluetooth for initial setup, and Wi-Fi for ongoing streaming. The latter is usually based on the 5 GHz band for higher transfer bandwidth, since range isn’t a concern in a diminutive vehicle interior. But I only see one antenna here.

Did the manufacturer figure out some way to passably enable it to support both bands? Or is there another one somewhere that I’m overlooking? Or does this particular design, in contrast to “Wireless Compability [sic]: 5.8 GHz Radio Frequency” spec claims to the contrary, leverage 2.4 GHz Wi-Fi instead? Or…???

Equally baffling is the user-inaccessible switch in the lower left corner (with the status LED to its right, in the middle of the lower edge). What’s the switch for? And then there’s the unmarked square IC enigma above and to the switch’s right (and LED’s left). I assume it’s the application processor, and that it runs at 24 MHz (courtesy of the marked oscillator to its left).

But who makes it, and what’s its specific product code? Another mystery. These are commonly Arm-based, with Chinese fabless suppliers Allwinner and Rockchip common sources (so says Google AI Overviews, at least), although this reverse-engineered unit from early and ongoing-leading adapter supplier Carlinkit reportedly leverages a Freescale (now NXP Semiconductors) SoC, the i.MX6 UltraLite.

To its right, and to the left of (and slightly below) the aforementioned antenna(e) assembly, is a Winbond W25Q128 128 Mbit serial flash memory, presumably housing the user-upgradeable system firmware image. And then there’s the even larger shiny-shield covered square IC below the antenna(e). This one’s easier to figure out, thanks to the careful application of a flat-head screwdriver acting as a lever.

It’s Realtek’s RTL8733, supporting the device’s wireless subsystem. The RTL8733 comprehends both Bluetooth and dual-band Wi-Fi transceiver functionality and presumably operates in conjunction with the mystery application processor to implement the adapter’s wireless-to-USB wired bridge functionality.

That’s all I’ve got for you today, folks. As always, let loose with your thoughts in the comments!

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

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The second phase of the India Semiconductor Mission has received a much larger budget than the first phase, with Rs 1.27 lakh crore allocated to support different areas of semiconductor and electronics manufacturing. Similar to the first phase, it is expected that most of the budget will be allocated to the selected individual projects, either as a capital subsidy (in which case, the government might contribute less than the 50% that it agreed to in the first phase) and as manufacturing-linked incentives paid on a per-unit basis once sales are completed. An added element of Incremental incentive boosters will be made available for those manufacturing those electronic goods that make use of domestic technologies and components.

It promises to extend the government’s stated intent of making India a strategic hub for the electronics value chain and build those capabilities in-house, with both intellectual and human capital, when only a few countries globally command such capabilities across different components of the value chain.

The government had stated from the very beginning that this would be a multi- decade process and its continued patronage with a more substantive kitty of money is indeed welcome. While this might not be a high job-generating industry per se, it is their strategic, geo-political considerations to deploy public funds. Whether this was a good initial wager is still largely unclear, as many of the first round of awarded projects have still to commence commercial production.

Some technology is well beyond the reach of all economies: The complexity of the machines that print nanoscale feature images from “extreme ultraviolet,” or EUV, lithography, currently mastered only by the Netherlands with a looming challenge
from Japan, means the thought of India’s committing investment to such a technology is enticing, but unfathomable. Such strategic capabilities have hard payoffs. Such a capacity to do “hard things” will breed resistance to India rapidly developing the capability and attracting talent.

 

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Tata Electronics, a group company under the $103 billion Tata Sons conglomerate, is set to establish the country’s first commercial chip fabrication plant utilising an older technology to focus on a market segment for semiconductor designs from external customers, said an official with knowledge of the project. This would mark the first time the Indian government allocates resources to support establishing a chip making unit. The Tata Group had earlier reported the creation of India’s maiden integrated device manufacturer (IDM) in the country, a unit that designs and produces semiconductor chips in-house.

Tata Electronics Pvt. It will use technology far older than planned to produce India’s first semiconductor wafers, coming from a country with virtually no history of the sophisticated technology, highlighting how much work it will need to do to catch up. The tech unit of the sprawling Tata conglomerate will use 90-nanometer process technology for a large part of its first chip fab, which will be located in Dholera, a city in the western state of Gujarat, according to the sources.

That’s a very mature technology used in low-end industrial applications and cars – one that might even be obsolete in the coming years. It’s a more humble beginning compared with the 28nm node which Tata Sons Pvt., the group’s holding company, said would be the first step of its chipmaking innings in its annual report for the year ended March 2025.
It has been observed that Tata’s public plans could have been aspirational regarding what is realistically possible on the ground in the next couple of years. Tata’s entry into chipmaking involves a partnership with Taiwanese rival Powerchip Semiconductor Manufacturing Corp.

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The STM32U3B5/C5 are the first STM32U3 devices featuring 2 MB of flash and our new hardware signal processor (HSP). In a nutshell, the HSP is an embedded signal processor that accelerates certain computations, enabling a whole range of industrial and AI applications on our ultra-low-power microcontroller. While the new models retain the near-threshold design that allowed the STM32U3 series to boost efficiency by a factor of four, the HSP opens the way for sensing and machine learning programs that would have been too demanding before. We wrote an accompanying blog post on the new hardware signal processor to help avid readers grasp the performance gains and what it means for the STM32 community.

What’s new? The new STM32U3B5/C5

The STM32U3B5/C5 primarily stands out from the other STM32U3 devices thanks to its HSP. It’s also why it carries double the flash memory of previous models and 640 KB of RAM, as we expect developers to need a much greater capacity for larger applications and datasets. The new device also comes with one additional group of interfaces, bringing the total to four SPI and I2C, two I3C and CAN-FD, and five UARTs. There are also five more 16-bit timers, for a total of 10. The only difference between the STM32U3B5 and the STM32U3C5 is that the latter includes a cryptocore to accelerate encryption and decryption operations, as well as offer CCB (see more on that later).

What makes the STM32U3 series special? Ultra-low-power consumption and high energy efficiency

One number sums up the efficiency of the STM32U3: at 117 Coremark/mW, it breaks the 100 symbolic threshold, literally making it a new benchmark in the industry. Indeed, most of the best devices from the competition hover below 100, and the STM32U5 reached 53.9. This significant jump in energy efficiency is due to our near-threshold design. We dedicated an entire blog article to this region of CMOS transistors that many are trying to utilize, but that has remained mainly in the background until now due to its inherent challenges.

0.65 V minimum and 105ºC maximum

In a nutshell, near-threshold conduction in a CMOS transistor occurs when applying a voltage between the gate and the source near a threshold (VT). In most of the devices used in a microcontroller like the STM32U3, that threshold is 500 mV. The vast majority of near-threshold designs apply a VGS of about 700 mV or more. And since near-threshold conduction is a diffusion current as residual leakage current flows under the gate oxide, the transistor itself experiences strict limitations in its operating voltage and temperatures. Most competing devices don’t go above 85ºC, which is why we don’t usually find these devices in industrial applications.

The STM32U3 is different because, thanks to unique optimization in the lithographic processes and manufacturing, ST can apply a lower voltage of 650 mV. The most direct benefit of a VGS value that’s closer to VT is that it helps lower the VCORE further, meaning that we can reach a minimum of 0.65 V and a typical value of 0.75 V. Additionally, as we are experiencing significantly less leakage current than competing solutions, the STM32U3 supports an operating voltage of up to 3 V and a temperature of 105ºC. It can, therefore, tolerate far harsher environments. Hence, the STM32U3 is unique because it makes near-threshold designs mainstream in most industrial applications.

Adaptive Voltage Scaling

Another issue common in near-threshold designs is die variability. Because the near-threshold region is sensitive to the smallest voltage variations, it affects dies on the same wafer more significantly. That’s why tuning each die to account for changes between them can be time-consuming and costly. To solve this challenge, ST implemented a testing system at the factory level, which automates machine learning on STM32 devices. We call it Adaptive Voltage Scaling. Simply put, our machines test each die, and a machine learning algorithm automatically tweaks various aspects to ensure consistent ultra-low-power consumption.

Versatile peripheral offering without compromising cost efficiency

Another challenge of near-threshold designs is performance. Indeed, as the VCORE is low, so is the operating frequency. However, that is not the case with the STM32U3, which features a Cortex-M33 running at 96 MHz. Moreover, we ensured that despite its more cost-effective pricing, engineers would still get a lot of peripherals and timers. Indeed, the new device supports two I3C buses, CAN-FD, one octo-SPI interface, and more. It also comes with 16 timers, including two 16-bit ones for motor control applications, and a touch-sensing controller for those working on a UI.

Robust safety and security for sensitive and mission-critical applications

Besides efficiency and performance, ST also designed the STM32U3 for safety and security. Consequently, the new device offers up to 1 MB of dual bank flash, enabling firmware updates without shutting the system down, which is often a critical consideration in mission-critical applications. The STM32U3 also introduces CCB to securely transmit keys by using independent buses ([patent filed in 2023 and 2024]). And we’ve already updated STM32CubeMX, our initialization tool. After activating the random number generator, users can select “CCB” in the list of cryptographic options and start using the feature. Finally, the STM32U3 can also target PSA L3 and SESIP3 certifications.

What’s next

To ensure the STM32U3 can reach numerous industrial applications, we are offering eight packages, which is unique for a near-threshold design. We are also releasing a Nucleo board to help developers rapidly design a proof-of-concept or run their tests to witness the ultra-low-power consumption for themselves. SmaXtec, a member of the ST Partner Program specializing in bovine monitoring already shared how,

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Researchers at Jilin University in China have reported a significant enhancement in the two-dimensional electron gas (2DEG) mobility of nitrogen-polar (N-polar) GaN/AlGaN heterostructures grown on silicon carbide (SiC) substrates by metal-organic chemical vapor deposition (MOCVD)...

NUBURU Inc of Centennial, CO, USA (a dual-use defense & security integrated platform company) has closed its best-efforts public offering, generating gross proceeds of about $38m, before deducting placement agent fees and other offering expenses...

The effective implementation of India-UK Comprehensive Economic and Trade Agreement (CETA) on July 15, 2026, highlights a significant growth for India’s automotive and electric vehicle (EV) sector. This agreement is a landmark free trade that eliminates custom duties on thousands of products, significantly boosting exports and foreign-investment by encouraging UK companies to invest in India through supply chains and manufacturing facilities. It was introduced by Indian Prime Minister Narendra Modi and UK Prime Minister Sir Keir Stamer, officially signed in July 2025 by both nations with the primary objective of expanding bilateral trade and investment.

The CETA establishes a comprehensive framework for creating new opportunities for Indian EV manufacturers, component suppliers, and battery companies by reducing traffic barriers and improving market access. Indian automakers like Tata Motors, Mahindra, and Maruti Suzuki can export up to 88,000 vehicles to the UK completely duty-free and scale their domestic manufacturing by integrating domestic MSME suppliers deeper into the UK and global EV supply chains.

The UK gains benefits by this agreement through deepened cooperation with India’s expanding clean energy and technology sector. The CETA provides UK businesses with improved access to India’s rapidly growing market and creating opportunities for greater trade and investment. This agreement improves the UK economy by granting tariff cuts on 90% of UK exports to India, lowering costs for UK consumers, and improving market access for British products.

The agreement is fully active and remain operational to its provisions on implementation. The future goal is to double bilateral trade to 100-120 billion by 2030. The current status of this agreement is that it operates alongside the Double Contribution Convention (DCC), a social security pact that saves Indian companies up to $600 million annually by exempting Indian professionals working temporarily in the UK from paying social security contributions in both countries simultaneously.

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