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The working principle of an electric vehicle (EV) relies on electrical energy stored in a large battery pack. An electric motor converts this electrical energy into mechanical energy, delivering power to the drivetrain to rotate the wheels and create motion. The exterior of an electric vehicle looks very similar to that of a car running on fuel but the powertrain installed inside an electric vehicle works very differently compared to the combustion engine in a fuel-powered car.

A major difference between a fuel-car and an electric vehicle is that an EV can generate power while applying brakes or slowing down, converting kinetic energy into electrical energy; a fuel-powered car does not support this method.

The complete working mechanism of an EV involves coordination between several systems: the battery pack, battery management system (BMS), power electronics, inverter, electric motor, transmission, thermal-management system and vehicle control system. These systems communicate with each other to control the flow of electricity from the battery to the wheels and recover energy when the vehicle slows down.

Battery Pack Stores Electrical Energy

It is the primary energy storage device containing thousands of individual battery cells arranged into modules and assembled into a large battery pack. It uses lithium-ion battery technology because of its small size, lightweight, high energy density, and low self-discharge rate. The battery stores energy in chemical form and releases it as electricity when the vehicle requires it.

The Battery-Management System Controls the Battery

The Battery Management System is responsible for monitoring and controlling the battery pack. Its monitoring parameters include state of charge (SoC) and State of Health (SoH), which helps the BMS monitor, protect, and optimize the battery pack. To perform its functionalities, it uses a combination of hardware and software components such as cell monitoring ICs, cell balancing circuits, stack communication, and CAN bus.

Power Electronics Control the Flow of Electricity

Power electronics perform switching operations by rapidly switching high-power solid-state semiconductor devices (MOSFETs, IGBTs, and thyristors) to control the flow of electric current. This method can reduce energy losses in the form of heat compared with traditional approaches that rely on resistors before advanced semiconductor switching.

Other core functionalities of power electronics include pulse width modulation to control the voltage or power delivered to the load, energy storage and filtering to manage high-power flow between the battery, electric motor, and accessories, and feedback control loops that adjust the switching frequency or duty cycle in real time to maintain stable power delivery and prevent fluctuations.

The Inverter Converts Battery Power for the Motor

The energy transferred from battery to electric motor is via inverter. It is an important link between a battery and motor that converts DC current from the battery into AC current for running the traction motor.

When the driver presses the accelerator, the vehicle’s control system determines the required power for the demanding acceleration. The inverter then converts DC current from the battery into a controlled AC waveform for the motor. When the vehicle is accelerating, the flow of energy from the battery to the wheels is shown in the given flow chart.

Battery ➔ Inverter ➔ Electric Motor➔ Transmission ➔ Wheels

The Electric Motor Converts Electricity into Motion

The conversion of chemical energy stored in a battery to mechanical energy via the inverter takes place in three phases and it also controls the frequency and voltage of the produced output alternating current (AC). The inverter uses high speed electronic switches (such as MOSFETs or IGBTs) to perform pulse width modulation (PWM). The technique shapes the electricity into AC cycles.

The produced AC cycles are then divided into three separate AC currents sent to the motor’s windings in a carefully timed sequence via the inverter. By adjusting the frequency of the AC power, the inverter dynamically controls the motor’s speed. By adjusting the voltage/amplitude, it changes the motor’s power and torque.

Transmission Transfers Torque to the Wheels

Once the rotational power is produced by an inverter, the drivetrain transfers that power to wheels by converting electrical energy into mechanical rotation. Electric motors spin at a very high RPM (revolutions per minute). Most EVs use a single-speed gearbox to drop this RPM to a lower, safer speed while multiplying the torque force. The resultant force is passed to a physical part, known as a differential, which allows the wheels on the same axle to rotate at different speeds while turning and the same speed to both wheels when driving in a straight line.

The Wheels Convert Motor Torque into Vehicle Motion

The last step is to convert the motor drive torque into work. The reduction gear and differential send the torque to the wheels. Force is created from the relationship between the rotating tires and the ground surface, which causes the vehicle to move. The vehicle’s control system constantly provides data such as wheel speed, accelerator position, steering angle and vehicle speed. This data can be used to control motor torque to achieve the best movement while driving an electric vehicle. These features make modern electric vehicles more advanced not only in terms of battery size but also in controlling torque because of integrated software, sensors, power electronics, and mechanical components that work together.

Regenerative Braking Recovers Energy

As already discussed at the beginning of this article, electric vehicles generate power while applying brakes or slowing down the vehicle. This feature is not available in a fuel-powered car. The amount of energy generated while applying brakes depends on the vehicle’s kinetic energy, which is then converted into electrical energy and stored as chemical energy in the battery.

Given below is the flow chart describing the flow of energy generated from applying brakes to storing energy in the battery:

Wheels ➔ Electric ➔ Motor ➔ Inverter ➔ Battery

The inverter transforms the created power into a form compatible with the battery, and the BMS decides whether the converted energy is accepted and how much is accepted.

Thermal Management Keeps the System Within Safe Temperatures

Temperature of vehicle components plays a crucial role in the proper operation of overall vehicle performance. The temperature of the electric motor should be maintained between 90°C and 95°C. This is the ideal temperature range for an electric vehicle to perform operations. Temperature above 100°C represents the risk state, which can cause battery damage or catch fire.

To avoid these major problems, vehicles come with installed thermal management systems which use liquid cooling and heating circuits to maintain an optimum temperature. Not only high temperatures, but also extremely low temperatures can affect battery and efficiency of an electric vehicle. The vehicle depends on onboard thermal management systems, active battery heaters, and specialised cabin-warming tech to deal with extremely cold weather conditions.

Conclusion

The operation of the electric vehicle depends on a precise sequence of energy conversions. Electrical energy stored inside the battery is regulated by the BMS; conversion and transfer of energy to the traction motor is through power electronics. An electric motor produces torque and transfers it to wheels through a reduction gearbox and differential. The primary purpose of the gearbox and differential is to turn high-speed motor power into smooth and controlled motion at the wheels.

At the same time, many operations are performed by an electric vehicle along with the working state of an engine such as thermal management for maintaining the suitable temperature, software platforms to control powertrain by coordinating with electronic control units (ECUs), power electronics, and BMS. An electric vehicle replaces a combustion engine with tightly integrated electronics, electrochemical, and propulsion systems.

The post How an Electric Vehicle Works: From Battery Packs to Wheels appeared first on ELE Times.

From rule-based workflows to AI-generated content and autonomous decision-making, businesses are entering a new era of intelligent automation.

The Next Stage of Enterprise Automation

Artificial Intelligence Companies are reinventing how they use products, data, and the enterprise. Automation has been a real workload saver for businesses for years and has played a role by automating rule-based manual and rule-based workflows. Then Generative AI arrived with its game-changing capability of content generation- text, images, code, technical documentation. Now we have Agentic AI, which, in addition to generating content, can plan actions, leverage digital assistants, and complete multi-step workflows with very little human guidance.

These three types of technology, Agentic AI, Generative AI, and Traditional Automation, are important for any enterprise running pilots or already investing in digital transformation. They may seem similar but will likely solve different pain points and require different deployment strategies.

Computer scientist and Coursera founder Andrew Ng has said that it’s ‘the new electricity’ because, as widely touted as the technology is, AI is going to change everything, ‘not one thing at a time’. From helping (creating content) to doing (acting), AI and automation are leading a fourth industrial revolution in manufacturing, medicine, finance, security, and IT.

What Is Traditional Automation?

Traditional automation automates instructions, rules, scripts, and flows, automating work that repeats with little human intervention. Examples include: Robotic Process Automation (RPA), batch system jobs, manufacturing controllers, enterprise business rules-based software. These systems work effectively if you know the input conditions, the environment, and the desired output, and you know the relationship between all three. For example, a manufacturing system that controls the plant to follow a control algorithm; a monitoring and alarm system where the alarm sounds when the temperature rises above a specified level; or an ERP system that automatically creates an invoice when the order fulfils certain conditions.

The primary advantages of traditional automation include reliability, stability, speed, and cost-efficiency for repetitive predictable operations. However, conventional and rule-based automation can lack robustness when data is partial, when instructions need to change, or when an unanticipated situation isn’t covered by previous rules. This doesn’t render traditional automation inaccessible, though. There are still uses for conventional automation in situations requiring deterministic behaviour, accurate timing, and consistent reactions—safety-sensitive industrial machinery still needs designed controller hardware. There are still uses for conventional automation in situations where you need deterministic behaviour, accurate timing, and consistent reactions- safety-sensitive industrial machinery still needs designed controller hardware.

What Is Generative AI?

The outputs of generative AI models are created based on the data and format you input into the model. Copywriting, summarisation, coding, translation, and visual design are all use cases for Large Language Models (LLMs), image generation models, and multimodal AI. Unlike classic automation, which relies on structured data, generative AI understands natural language input and can be used by marketers for writing campaigns, software developers for code snippets and summarising technical documentation, and product teams to create ideas for new product designs.

Enterprises are applying Generative AI in their automation use cases. These use cases include customer service, managing the knowledge base, research, and software engineering. Generative AI is useful for providing employees easier ways to search extensive document repositories and to translate complex technical content into understandable content.

However, the technology is imperfect. Models may generate incorrect statements, invented references, or misinterpret instructions. Their results can require review by a human subject matter expert- particularly in technical, financial, legal, and safety- critical contexts. Moreover, generative AI doesn’t just generate content; it lacks the power or capability to actually perform the action. A model might prepare a maintenance report, but it can’t change an asset-management system or make an engineering assignment on its own.

What Is Agentic AI?

An agentic AI can goal-seek (when given instructions), reason out (by planning and executing steps to accomplish a goal), use tooling (e.g., APIs and tools), get feedback, and alter behaviour if the context changes. We see agentic AI as being powered by an LLM (or, in our focus case, a Generative AI) for reasoning and language tasks, while utilising APIs, enterprise data, memory, rules, etc. Agentic AI systems are taught by enterprise chat to explore how they can use generative models for input interpretation, problem solving, and action planning.

Think of a particular IT support function. A classic automation would trigger an alert whenever a server fails. A Generative AI assistant may review the alert and suggest a short list of the most probable root causes. An Agentic AI system that has been configured correctly would gather diagnostic information, review peer-approved troubleshooting instructions, recommend actions, and carry out peer-approved remediation.

Agentic capabilities also enable coordination of multi-agent AI. A retrieval agent might surface results, while one or more subsequent specialised agents carry out processing, and yet other sketches out an execution plan. Multi-agent support might facilitate a variety of advanced workflows, but supporting such collaboration introduces additional problems-especially in coordination, monitoring, permission, and accountability. Agentic skills do not mean that humans lose control.

Agentic AI vs Generative AI vs Traditional Automation: Understanding the Differences

The biggest contrast between Agentic AI, Generative AI, and Traditional Automation is how each approaches a given task. The traditional Automation approach is to follow a set process. This is most effective for repetitive tasks where the data inputs and outputs are known—examples include AP automation, predefined reporting, and assembly line control.

Generative AI generates text or other output from a prompt. It is helpful for writing documents, summarising, generating code, and knowledge work. Agentic AI combines reasoning, planning, and use of tools to achieve a long-term goal via a series of steps. It can gather information, decide between alternatives, connect to multiple interacting applications, and determine when a goal has been reached.

For instance, with traditional automation, the ticket would be assigned based on parameters, and with Generative AI, it could be summarised and potentially even responded to. Generative AI could even be used by an agent to create a draft resolution. An Agentic AI could retrieve the customer order history, compare that to the refund policy, generate a suggested outcome, and activate a supported process step. And it’s not always a case of choosing just one: all three can be used together in the same automation.

Real-World Applications Across Industries Manufacturing and Industrial Operations

At the manufacturing stage, Traditional Automation sets the pace for manufacturing, conveyor planning, and repetitive assembly. Generative AI may help engineers when they read logs, write manuals, or guide troubleshooting of failed equipment. Agentic AI can support authorised workflows: Collect sensor data, generate a service ticket, check availability of spare parts, and coordinate dispatching a technician. In the industrial use case, keep the AI agents as close as possible to the traditional control system: Request safety interlocks and high-impact actions only after engineering control approval has been obtained.

Healthcare and Financial Services

In healthcare, Generative AI in the healthcare could generate summarise of clinical documentation, and help with administrative correspondence. Agentic AI might lead scheduling, obtaining approved records, and similar tasks. Clinical decision-making would require appropriate professional governance.

In Financial Services, traditional automation handled transactional processing, whereas Generative AI can create summaries of financial documents and even assist with customer engagement. Agent AI can help with fraud investigation by gathering evidence and escalating concerns. Payments and changes to accounts should have controls and audit trails.

Cybersecurity and IT Operations

Typical security automation generates alerts by configured indicators. Agentic AI may help security analytics by summarising incidents, investigation reports and producing plain language explanations of technical results. Agentic AI for cyber security may gather approved data sources, prioritise alerts, collect evidence, and carry out approved actions.

However, with greater access comes greater risk. A badly trained agent could alter data, disclose sensitive details, or make a mistake and take the wrong action. This is why organisations need least-privilege access, observability into actions and decision points, and the ability to pause and revert those decisions.

Challenges and the Road Ahead

Digital automation processes are different. You are switching from high-volume test automation to a completely new process, and there are a few factors to consider before you decide. Data quality, integration cost, human resilience, security, and performance. Conventional automation is reliable but less flexible. Generative AI is flexible but unpredictable. Agentic AI is flexible but unpredictable. Agentic AI is more powerful operationally but comes with new, more complex risks, as the system can query applications.

Microsoft CEO Satya Nadella has been talking about an emerging agentic world- where AI agents operate in the jobs of humans, teams, and enterprises. This vision’s next step is to utilise enterprise systems to orchestrate work between teams and individuals and not simply in response to the work they submit. Pragmatic advice: automate the routine work that is deterministic; Generative AI for work that involves language and content generation; and Agentic AI for case orchestration that is multi-step, multi-function, multi-stakeholder, multi-outcome where the complexity merits more nuance. Minimise human oversight as the scale of potential consequences of an action increases.

Conclusion

Looking at Agentic AI vs Generative AI vs Traditional Automation, we see three flexible dimensions for enterprise tech: Traditional automation provides predictable execution, Generative AI allows machines to produce and consume new data, and Agentic AI gives machines the ability to orchestrate a series of actions to achieve an explicit goal.

Hence, for businesses, the goal shouldn’t be to replace each process with the latest AI, but rather to choose the right capability for each use case, artfully connect the systems, and retain the right level of human oversight. An enterprise that leverages both deterministic automation and Generative AI with well-governed AI agents will be more agile, productive, and responsible.

The post Agentic AI vs Generative AI vs Traditional Automation: How Intelligent Systems Are Transforming Enterprise Operations appeared first on ELE Times.

Apple branding is front and center in the company’s resurrected smart home “push”. Although broader ecosystem compatibility is also feasible, you’d never know it from the messaging.

Within my 2024 CES coverage, I devoted a section to the industry-developed Matter smart home interoperability standard, which had originated in December 2019, as well as to the associated Thread wireless protocol. I was not encouraged by the then-current industry-adoption status, nor with the implementation robustness of what few implementations did exist, although I clung to a sliver of sooner-or-later “hope springs eternal” optimism.

The Matter smart home communication standard, built on the foundation of the Thread (based on Zigbee) wireless protocol, had no shortage of associated press releases and product demos in Las Vegas this week. But to date, its implementation has been underwhelming (leading to a scathing but spot-on recent diatribe from The Verge, among other pieces), both in comparison to its backers’ rosy projections and its true potential.

Not that any of this was a surprise to me, alas. Consider that the fundamental premise of Matter and Thread was to unite the now-fragmented smart home device ecosystem. If you’re an up-and-coming startup in the space, you love industry standards, because they lower your market-entry barriers versus larger, more established competitors. Conversely, if you’re one of those larger, more established suppliers, you love barriers to entry for your competitors.

Therefore the lukewarm-at-best (and more frequently, nonexistent or flat-out broken) embrace of Matter and Thread by legacy smart home technology and product suppliers. Suffice it to say that I’m skeptical about Matter and Thread’s long-term prospects, albeit only cautiously so. I just don’t know what it might take to break the logjam that understandably prevents competitors from working together, in spite of the reality that a rising tide often does end up lifting all boats…or if you prefer, it’s often better to get a slice of a large pie versus the entirety of a much smaller pie.

I’ve also appropriately scattered Matter and Thread mentions within a series of follow-up pieces, most notably my subsequent CES 2025 summary.

A year later, is the situation better? Not really, candidly. For a more in-depth supplier-sourced perspective, I encourage you to read Aalyia’s coverage of her time spent last week in Silicon Labs’ product suite, including an interview with Daniel Cooley, CTO of the company. Cooley is spot-on when he notes that “it is not unusual for standards adoption to progress slower than desired.” I’ve seen this same scenario play out plenty of times in the past, and Matter and Thread (assuming they eventually achieve widespread success) won’t be the last. I’m reminded, for example, of a quote attributed to Bill Gates, that “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10.”

Cooley is also spot-on when he notes that Matter and Thread don’t necessarily need to go together; the Matter connectivity standard can alternatively use Ethernet (either wireless, aka Wi-Fi, or wired) for transport, along with Bluetooth Low Energy for initial device setup purposes. And granted, there has been at least some progress with both Matter (in particular) and Thread over the past year.

Version 1.4 of the Matter specification, announced last November, promises (quoting from Ars Technica’s coverage) “more device types, improvements for working across ecosystems [editor note: a concept called “Enhanced Multi-Admin”], and tools for managing battery backups, solar panels, and heat pumps”, for example. And at CES, the Connectivity Standards Alliance (CSA), which runs Matter, announced that Apple, Google, and Samsung will accept its certification results for their various “Works With” programs, too. That said, Amazon is notably absent from the CSA’s fast-track certification list

As I write these words in October 2026, we’re nearing two years since Matter 1.4’s release. The spec is now at the v1.6 level, with further enhancements including belated support for cameras and other sensors, along with energy management systems; you can read all the details at the associated Wikipedia entry. And speaking of Silicon Labs, for additional background info I’ll commend to your attention a still-relevant EDN article series published in early 2023 and authored by Rob Alexander, the company’s Principal Product Manager for Matter:

• How can device makers streamline Matter certification
• Why Matter matters
• How to connect existing smart home devices to Matter

That all said, industry adoption still seems like it’s stuck in first gear, that is assuming it’s (arguably) moving forward at all. And specific to Apple, whose re-engagement with the smart home market went public earlier today, Matter seems to be little more than a convenient alternative means of achieving baseline access to Apple’s otherwise proprietary HomeKit ecosystem without need to pony up for a paid license, as was timely exemplified by yesterday’s TP-Link smart plug teardown. Indicative of this seeming irrelevance, Matter and Thread are mentioned only in passing in Apple’s press release covering partnerships with other companies.

Enter the brand-new HomeView

If I had to bet, I’d wager that the HomeView smart home hub has been stuck in the ready-for-production queue for a while now, awaiting a sufficiently robust AI-enhanced successor to the legacy Siri assistant service. The outcome product’s industrial design hearkens back to both Amazon’s latest-generation Echo Show series, released a year ago, and to Apple’s own 2002-2004 era iMac G4 computer.

Shown here is the product variant with a down-firing woofer and two passive radiators built into the base; a wall-mountable version with only the two 7” full-range drivers in the display is also available. Both cost $299, run an app-restricted operating system also branded HomeView, functionally reminiscent of the Apple Watch UI and with an intriguing derivative-of-CarPlay angle, and are based on the Apple Silicon A18 SoC; the DRAM size wasn’t announced (although in the iPhone SoC variant, it’s 8 GBytes). And there’s zero mention of Matter or Thread anywhere in the press release this time, although on-site media inquiries revealed that HomeView can in fact act as both a Matter controller and bridge, along with as a Thread border router.

The above conceptual usage promo video is admittedly compelling (IMHO, at least); I’ll cover shortly the privacy ramifications and implementation of the multi-user scenario shown in it.

Evolutionary advancements also garner announcements

The HomePod mini smart speaker was originally introduced in October 2020, with more colors added to the product line mix a year later (and a teardown by yours truly ~1.5 years after that), so a redesign was arguably overdue. We didn’t get much today, but it was at least something. The SoC inside is now the S10, versus the prior S5, both originally intended for various Apple Watch generations and this time Apple Intelligence-capable.

Then there’s the Apple TV 4K, the newest generation of which candidly and bafflingly seems to have been cut off at the knees by its creator on day one. Its SoC upgrade from the A15 Bionic in the 2022 version of the device to the A18 affords it AI capabilities…but Apple’s also bringing Siri AI to both the 2022 and 2021 versions of the device (presumably predominantly cloud processing-based in these older, latter cases), so if you already own one, you’re good. The newest version embeds a three-microphone array that lets you shout at it from afar…but the remote controls included with legacy Apple TV 4K versions already embed a microphone.

And speaking of remote controls, the newest Apple TV 4K’s version embeds a speaker so you can prompt it to emit a locating sound if it’s stuck between the cushions of your sofa…but that same remote control can also be purchased separately for $59 for use with legacy Apple TV 4K versions. And did I mention that the newest Apple TV 4K costs $299, both a $50 uptick from the current price of its equivalent-capacity predecessor (which itself saw a $100 price increase from the original $149 earlier this year) and twice the price of Google’s TV Streamer 4K (which itself is notably pricier than equivalents from Roku, Walmart, and the like)? The only other notable benefit that the newest Apple TV 4K affords is integrated stereo and surround sound support, but actualizing this particular aspiration necessitates one or two matched pairs of only second-generation HomePods and/or HomePod minis.

Privacy-assuring redundancy

Wrapping up, the aspect of today’s announcements that I actually found most interesting was what Apple referred to at the end of the HomeView press release as “Private and Secure by Design” and I alluded to by the “Distributed identity validation” mention in this piece’s title.

If you haven’t yet seen the conceptual usage promo video I embedded earlier, please go back and watch it before continuing. Two things you’ll likely note straightaway are that:

• The HomeView device dynamically recognizes each user audibly by voice and/or visibly by face, and
• The HomeView device responded by dynamically tailoring its UI to each user’s preferences as well as more generally by replying using that user’s dataset.

Voice and face recognition are admittedly getting more responsive and reliable all the time, but they’re still not perfect. The latter’s not even an option with the camera-less HomePod mini v2 and 2026 iteration of the Apple TV 4K. And when you’re dealing with a user’s personal information—emails and text messages, calendar and contacts data, music and image libraries, etc.—you need to be absolutely sure of who you believe you’re interacting with, no matter that you’re presumably interacting with someone who lives in the home where the device is located.

That’s why personalized AI is only available on today’s announced devices for users who:

• Have registered Apple accounts, and
• Have unlocked FaceID-capable iPhones (translation: not the latest iPhone Duo, for example) and iPads logged into those accounts and either connected to the same Wi-Fi network as the HomeView, HomePod mini v2 and/or 2026-version Apple TV 4K, or recently on Wi-Fi but currently logged in via cellular (I’m assuming this latter exception handles cases where someone’s iPad or iPhone briefly drops Wi-Fi).

It also, of course, conveniently excludes Android smartphone and tablet owners and the like. Ahem.

That’s “all” I’ve got for you today. Rumors suggest that Apple’s got another event scheduled for two weeks from today. We’ll see; if so, I’ll see you back here then. For now, as always, I welcome your thoughts in the comments!

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

Related Content

• Apple’s question for the developer: Are you up for an AI do-over?
• The 2025 WWDC: From Intel, Apple’s Nearly Free, and the New Interfaces Are…More Shiny?
• The 2024 WWDC: AI stands for Apple Intelligence, you see…
• Apple’s processor cadence: A pending stutter-step for improved long-term edge inference?

The post Apple heads Home: Distributed identity validation, and how much does Matter matter? appeared first on EDN.

Lumentum Holdings Inc of San Jose, CA, USA (which designs and makes optical and photonic products for optical networks and lasers), Astera Labs Inc (which provides rack-scale AI infrastructure through purpose-built connectivity solutions) and Corning Inc are giving a joint demonstration of PCIe-over-optics connectivity for AI rack-scale architectures in Astera Labs’ booth #B34 at the 2026 Open Compute Project (OCP) Global Summit at the San Jose McEnery Convention Center (12–15 October). The rack-level demonstration combines an Astera Labs Scorpio Smart Fabric Switch with Lumentum’s 1060nm vertical-cavity surface-emitting laser (VCSEL)-based optical interconnect platform and Corning multi-mode fiber connectivity...

Rising 5G adoption across industrial IoT, connected automotive, and smartphones is driving demand for application-specific 5G chips and modules. Chipmakers are increasingly targeting 5G-Advanced, satellite non-terrestrial network (NTN) connectivity, 5G fixed wireless access (FWA), and 5G New Radio (NR).

Many of these 5G chips and modules deliver 3rd Generation Partnership Project (3GPP) Release (R) 17 and R18 features with backward compatibility, and now support R19 for 5G-Advanced, while setting the stage for 6G development. Chipmakers also target 5G RedCap (reduced capability) for cost-effective 5G devices in IoT applications.

Many of these solutions focus on higher integration and flexibility options to simplify development as well as reduce power consumption and footprint. Part of the integration effort also includes AI to enhance performance.

Here is a sample of 10 5G chips and modules introduced over the past year with higher performance that drive 5G adoption across a range of applications.

Modems and platforms

Qualcomm Technologies Inc. launched the X105 5G Modem-RF, featuring the industry’s first 3GPP R19-ready modem, which lays the foundation for 6G development and testing. The Qualcomm X105 5G-Advanced platform improves data speeds and power efficiency while claiming breakthroughs in quad-band GNSS and integrated NR-NTN support. Its integrated fifth-generation AI processor uses agentic AI in the modem to detect, classify, and optimize data traffic types based on user scenarios.

Qualcomm claims several industry firsts with the X105 platform. It features the first 6-nm RF transceiver, achieving up to 30% lower power consumption and 15% smaller board footprint than the previous generation; the first quad-frequency (L1, L2, L5, L6) GNSS engine with integrated multi-constellation support; and improved location accuracy.

The 5G Modem-RF also supports video calling, video streaming, data, voice, and messaging over NR-NTN. It delivers 14.8-Gbits/s peak download speeds while achieving up to 13.2 Gbits/s with sub-6-GHz bands. Qualcomm said it addresses the need for more uplink capacity and data speeds, achieving 4.2-Gbits/s peak uplink throughput.

With an R19-ready architecture, the X105 Modem-RF system is designed to accelerate 5G-Advanced adoption across smartphones, FWA, mobile broadband, automotive, XR, PCs, robotics, and industrial IoT.

Qualcomm’s X105 Modem-RF system (Source: Qualcomm Technologies Inc.)

Broadcom Inc. announced an integrated 5G and Wi-Fi 8 FWA platform in partnership with Samsung Electronics Co. Ltd. The broadband-optimized reference platform integrates Broadcom’s BCM6776 Wi-Fi 8 system-on-chip (SoC) with Samsung’s B1320 5G modem. Claimed as an industry-first platform, it combines 3GPP R17 connectivity with the emerging Wi-Fi 8 (IEEE 802.11bn) standard.

The ​​Broadcom BCM6776 is a highly optimized, tri-band Wi-Fi 8 SoC. Key features include a quad-core Arm network processor with support for two-stream 40-MHz channels in the 2.4-GHz band and four-stream 160-MHz channels in the 5- and 6-GHz bands, delivering a complete Wi-Fi 8 platform in a single chip.

Other features include integrated 2 × 2 2.4-GHz and 4 × 4 5-GHz and 6-GHz Wi-Fi 8 MAC/PHY/radio functionality, which simplifies system design and lowers cost; an integrated multi-gig PHY; and on-chip 2.4-GHz power amplifiers and support for third-generation digital pre-distortion, which reduces external components and improves RF efficiency. Dual PCIe Gen3 controllers enable simultaneous tri-band applications with a single additional chip.

The platform reduces bill-of-materials costs, simplifies board design, and reduces active power consumption by 50% compared with previous generations, according to Broadcom.

The Samsung B1320 is a highly integrated, power-efficient, 5-nm chipset solution that supports 3GPP R17. It can achieve 5G downlink speeds of 3.43 Gbits/s and uplink speeds of 1.17 Gbits/s. It supports a 4Rx/2Tx radio chain. The 5G modem with a quad-core Arm CPU and the BCM6776’s network processor can support full Wi-Fi to 5G throughput. The platform also includes a Power Class 1.5 RF transceiver, power management, and a GNSS receiver. It also supports NR-NTN and NB-NTN for satellite communications.

Qualcomm also delivers 5G to wearables with its Snapdragon Wear Elite Platform. The personal AI platform delivers personal, always-on, intelligent wearable computing devices. It works across WearOS by Google, Android, and Linux with a neural processing unit (NPU) for on-device AI and an advanced suite of ultra-low-power connectivity solutions.

Claiming a first‑of‑its‑kind, multimode connectivity architecture, the Snapdragon Wear Elite Platform integrates six advanced technologies:

• 5G RedCap delivers advanced, low-power cellular connectivity for connected intelligent applications.
• Micro‑power Wi‑Fi enables always‑on Wi‑Fi connectivity at significantly lower power for intelligent wearables, according to Qualcomm, which require continuous AI context synchronizing, richer data exchange, and seamless coordination with nearby devices and cloud services.
• Bluetooth 6.0 enables proximity‑aware interactions.
• UWB provides secure, precise, proximity‑based interactions.
• GNSS delivers advanced AI processing for precise location context.
• NB‑NTN enables satellite‑based, two‑way messaging and critical communications from intelligent wearables when cellular and Wi‑Fi coverage are unavailable.

Delivering advanced power efficiency and faster speed, the platform delivers a 5× improvement on single-core CPU performance and up to 7× faster GPU for app launching, multitasking, and smoother rendering. This enables the Snapdragon Wear Elite to run complex, 2 billion–parameter AI models locally, faster, and more securely.

The advanced power management enables 30% more battery use than the previous generation. When recharging is needed, rapid charging powers a device up to 50% in approximately 10 minutes.

Qualcomm’s Snapdragon Wear Elite Platform (Source: Qualcomm Technologies Inc.) 5G SoCs and RF chips

In late 2025, MediaTek introduced its Dimensity 9500 SoC, delivering new levels of on-device AI, console-class gaming, and power efficiency. According to MediaTek, the 9500 is designed to “supercharge the next wave of flagship 5G smartphones.”

The Dimensity 9500 features a third-generation All Big Core CPU design combining a 4.2-GHz ultra core, three premium cores, four performance cores, and four-lane UFS 4.1 storage. It delivers up to 32% higher single-core and 17% higher multicore performance than the previous generation, while the ultra core achieves up to 55% lower power consumption at peak performance, for longer battery life and greater productivity, and claims up to 30% more power efficiency while multitasking in games and social audio call apps.

The platform also features a new cache and memory architecture, claiming the industry’s first support for four-channel UFS4.1. It doubles read/write speeds and accelerates large AI model loading by 40%. In addition, the second-generation Dimensity Scheduling Engine delivers smoother responsiveness and sustained efficiency even under heavy workloads, according to MediaTek.

The Dimensity 9500 integrates the Arm G1-Ultra GPU, providing up to 33% higher peak performance and 42% improved power efficiency, and introduces higher frame rate interpolation, up to 120-fps raytracing, for console-level, quality gaming.

MediaTek said integrating the ninth-generation MediaTek NPU 990 with Generative AI Engine 2.0 doubles compute power and introduces BitNet 1.58-bit large model processing, which reduces power consumption by up to 33%. The Dimensity 9500 is also the first integrated compute-in-memory architecture for its newly added Super Efficient NPU.

Doubling its integer and floating-point computing capabilities enables 100% faster, 3 billion–parameter LLM output; 128K token long text processing; and 4K ultra-high-definition image generation, while reducing peak-performance power consumption by 56%, according to the company.

The Dimensity 9500, with the MediaTek Imagiq 1190, supports RAW-domain pre-processing, up to 200-MP capture, 30-fps continuous focus tracking, a new portrait engine, and cinematic 4K 60-fps portrait video. It also provides the latest MiraVision Adaptive Display technology.

The platform’s connectivity suite includes Bluetooth call enhancement, Wi-Fi fast transfer, and multi-network intelligence. The AI-powered communication technologies are reported to deliver up to 10% lower power in 5G and 20% lower power with Wi-Fi, while 5CC carrier aggregation increases bandwidth by 15%. In addition, AI positioning and network selection technologies offer 20% higher accuracy, as well as 50% lower network latency thanks to AI congestion prediction, according to the company.

Qorvo Inc. recently introduced a family of RF switches for 5G radios, designed to simplify multiband radio architectures. The family spans from 50 MHz to 10 GHz. These devices reduce component count, improve signal integrity, and enable more efficient RF system design across 5G infrastructure, as well as industrial, drone, and test applications.

Designers face increasing challenges maintaining isolation and signal integrity without adding size, loss, and complexity, as 5G radios expand to support wider bandwidths and more frequency bands, Qorvo said. Many designs rely on cascaded switch architectures or multiple narrowband devices, which increase insertion loss, degrade linearity and signal integrity, and add board space and design effort, according to the company.

The wideband, high-isolation RF switch family addresses these challenges, eliminating the need for cascaded switches in 5G radios. “This approach reduces insertion loss, maintains signal linearity, and simplifies design, improving receiver performance in applications such as digital pre-distortion feedback,” Qorvo said.

The family comprises three devices:

• The QPC6144 for high-isolation switching is a SP4T wideband switch that delivers greater than 65-dB isolation in a single device, eliminating the need for cascaded switches.
• The QPC6122 (SP2T) for compact wideband switching and QPC6188 (SP4T) for flexible wideband routing provide wideband absorptive switching across 50 MHz to 10 GHz, enabling a single platform approach to RF routing. These devices reduce component count and simplify design. They also maintain low insertion loss and strong linearity across wide bandwidths for calibration paths, general signal routing, and multiband operation.

Qorvo’s wideband, high-isolation RF switch family (Source: Qorvo Inc.)

Sivers Semiconductors announced earlier this year the general availability of its highly integrated 7- to 15-GHz Alpine0715 (Daybreak) multi-beamforming transmit/receive front-end IC for emerging 5G/6G FR3 applications and multifunction defense arrays. The FR3 frequencies are used for 5G-Advanced and 6G network deployments, delivering sub-6-GHz and wider bandwidths at mmWave frequencies.

The Alpine0715, housed in a 7.95 × 7.0-mm WLCSP package, offers high linear output power, low noise figure, high efficiency, and integrated beam-table memory in a single device, the company said, and the reconfigurable multi-beamforming architecture enables smaller, lower-cost antenna arrays

Featuring four dual-polarization TX and four dual-polarization RX elements, the Daybreak beamformer IC delivers 27-dB gain and 20-dBm OP1dB TX performance and 32-dB single-channel gain and <3-dB NF RX performance.

5G modules

At electronica India 2026, Cavli Wireless announced two 5G modules targeting automotive, IoT, and M2M applications. The AQ62 5G Autograde is an automotive-grade 5G NR sub-6-GHz module designed for next-generation telematics and connected vehicle applications, and the CQM211 5G NR Advanced sub-6-GHz module, compliant with 3GPP R16, is designed for IoT and M2M applications.

The CQM211 5G NR module delivers multi-gigabit 5G connectivity with LTE Cat 16 fallback and flexible integration options for new and existing hardware platforms. The module targets high-bandwidth applications such as Industry 4.0 equipment, smart surveillance systems, routers, gateways, mobile Wi-Fi, and other edge applications requiring high-speed connectivity.

Key specs include a Cortex-A7 processor clocked at up to 1.8 GHz, 2× carrier aggregation over NR, dual-band GNSS, a PCIe Gen3 interface, and DBI display support. It is available in LGA and M.2 form factors.

The AQ62 5G Autograde module for connected automotive applications delivers an embedded software environment for developing advanced applications such as telematics control units, ADAS systems, OBD solutions, and hybrid tracking applications. The AQ62, built on Yocto Linux Kernel 5.15 with SDK support, provides high-speed 5G NR connectivity with LTE Cat 18 fallback. It supports 3GPP R16.

The 5G NR automotive module features a quad-core Arm Cortex-A55 processor, L1+L2/L5 GNSS, and dual-SIM dual-standby support. The module also provides optional NG-eCall and NTN band support. The AQ62 is available in five regional variants: worldwide (WW), North America (NA), Eurasia and Japan (EAJ), India (IN), and Eurasia (EA).

Designed to accelerate Android-based IoT applications, Quectel Wireless Solutions announced two Android 16 smart modules: the SE505FE entry-level 5G smart module and the SH602FA high-performance 4G smart module. The modules provide integrated wireless connectivity and multimedia capabilities in compact form factors.

Here, we’ll focus on the 5G module, which provides Android-powered 5G connectivity to a wider range of IoT applications. The module is built on the MediaTek MT8863T chipset with a 2× Cortex-A76 and 6× Cortex-A55 processor architecture and Mali-G57 GPU. It supports 5G sub-6-GHz and LTE Cat 4, together with Wi-Fi 6, Bluetooth 5.2, and GNSS. Typical applications include AR/VR devices, digital signage, smart gateways, tablets, industrial PDAs, robotics, and intelligent video platforms.

The SE505FE series modules support multiple-input/multiple-output technology. Quectel said the concurrent use of multiple antennas at the receiver end on the same frequency band minimizes bit error rate and optimizes data speed, which improves communication quality.

The module supports multi-constellation GNSS (GPS, BDS, GLONASS, Galileo, and QZSS) receiver for fast and accurate positioning and offers a range of interfaces, including LCM, camera, touch panel, microphone, UART, USB, I2C, and SPI. It supports cameras up to 32 MP at 30 fps and 1080 × 2520 video recording and playback at 120 fps.

The module is housed in a 42.5 × 56.2 × 2.95-mm LGA package. The SE505FE series is available in three variants: SE505FE-EM, SE505FE-NA, and SE505FE-WF (supports Wi-Fi and Bluetooth only).

Quectel’s RM255C-GL and RG255C-NA (Source: Quectel Wireless Solutions)

Quectel also expanded its mid-tier 5G family with RedCap 3GPP R17 variants: the RM255C-GL for global markets and the RG255C-NA for North America.

5G RedCap has emerged as the technology to address the mid-tier connectivity gap between high-speed eMBB and narrowband IoT as 5G expands into industrial and IoT domains, Quectel said.

The RM255C-GL is a 5G sub-6-GHz RedCap module in the M.2 form factor, developed in accordance with 3GPP R17. It supports peak data rates of up to 223-Mbits/s downlink and 123-Mbits/s uplink, with 5G sub-6-GHz standalone operation and LTE Cat 4 fallback. It is backward-compatible with R15 and R16.

The RM255C-GL is pin-to-pin-compatible with Quectel’s RM5x0N and RM50xQ 5G series and both pin-to-pin and size-compatible with selected LTE-A and 4G modules, including the EM06, EM12 series, EM160R-GL, and EM05, which helps simplify the migration from LTE to 5G RedCap.

The module integrates Qualcomm IZat Gen 9VT location technology with optional multi-constellation GNSS support (GPS, GLONASS, BDS, Galileo, QZSS, and NavIC) for fast and accurate positioning. It also supports a range of interfaces, including USB 2.0, PCIe 2.0, and PCM, and offers USB drivers for Windows 10/11, Linux, and Android, extending the module into a wider range of RedCap applications.

The RG255C-NA 5G RedCap sub-6-GHz, housed in an LGA module, is designed in compliance with 3GPP R17. Purpose-built for industrial-grade applications, it supports key 5G capabilities including URLLC and network slicing. The module delivers peak data rates of up to 223-Mbits/s downlink and 123-Mbits/s uplink. It operates in 5G sub-6-GHz standalone mode with LTE Cat 4 fallback and is backward-compatible with R15 and R16.

The module is compatible with Quectel’s compact EG2x 4G module series, simplifying design upgrades while meeting mid-speed application requirements for higher capacity, low latency, and enhanced reliability in industrial and commercial deployments, the company said.

The RG255C-NA also integrates Qualcomm IZat Gen 9VT location technology, supporting multi-constellation GNSS including GPS, GLONASS, NavIC, BDS, QZSS, and Galileo. The integrated GNSS receiver streamlines product development and provides fast and accurate positioning, Quectel said.

The module also features a suite of industry-standard interfaces, including USB 2.0, PCIe 2.0, PCM, UART, SGMII, and SPI, along with USB driver support for Windows 10/11, Linux, and Android.

The RM255C-GL and RG255C-NA have completed the required regulatory and industry conformance certifications. The RG255C-NA is certified to FCC, IC, and PTCRB standards, while the RM255C-GL has achieved CE, RCM, FCC, IC, GCF, and PTCRB certifications.

The post 5G chips and modules power phones, wearables, IoT, and vehicles appeared first on EDN.

OVODYO is a desk clock that shows the time by rolling and flipping two 3D-printed spheres shaped like icosahedra. The hours sphere has 12 faces carrying the 12 hours, while the minutes sphere has 12 faces at 5-minute intervals. Six RGB LEDs in the centre indicate the intermediate minutes. The project is by ekaggrat singh kalsi, who describes the build on the project page on Hackaday.io.

The two icosahedral spheres are raised above the clock base by a brass support shaft. An internal drive shaft runs through the centre of the support shaft and drives a set of bevel gears. The gears rotate the outer shells around two axes, periodically cycling through all twelve faces. Only one set of numbers appears upright at a time.

How the bevel gear transmission works

The central shaft drives the central bevel gear. This in turn rotates the 90-degree bevel gear on the lower bevel gear with a 1:1 ratio. The rotation of the 90-degree bevel gear makes the inclined bevel gear rotate on the main bevel gear, with a ratio that flips the sphere.

The two movements follow different cadences. The hours sphere stops every one and a quarter turns, equal to 450 degrees. The minutes sphere stops every 90 degrees, equal to a quarter turn. So the hour advances with a long movement, while the minutes change much more often.

• Hours sphere: 12 faces, stops every 450 degrees
• Minutes sphere: 12 faces, stops every 90 degrees
• Outer diameter of the spheres: about 78 mm
• Overall dimensions: 383 x 78 x 163 mm
• Motors: 15 mm stepper motors with 1:99 gearbox
• Intermediate minute indication: 6 RGB LEDs in the centre

Electronics, Arduino firmware and Hall sensors

The electronic heart is an ATmega8, which drives the motors through a DRV8833. The code is written in the Arduino environment. The base has 3 parts: left and right house the motors and the drive shaft, the centre holds the electronics.

The motors find their starting position using a magnet and a 3144 Hall effect sensor. This way the firmware knows where to restart after every rotation. Since the minutes sphere turns every 5 minutes, to show the intermediate minutes there are 6 RGB LEDs in the centre that light up in sequence and blink. When the last LED finishes blinking the sphere turns and the LEDs reset.

Anyone wanting to rebuild OVODYO has to allow for two stepper motors with gearbox, a dual H-bridge driver and the position sensors. A compact stepper motor with gearbox is the kind of actuator needed to move the spheres without excessive bulk, while for the lighting part you could consider effect RGB LED modules to adapt to the minute indication.

The mechanical assembly remains the most delicate part. The bevel gears must be aligned precisely, because any play adds up along the chain and shifts the numbers relative to the expected position. In addition, the drive shaft must stay centred inside the brass support shaft, otherwise the sphere wobbles as it rotates.

3D printing, dimensions and calibration of the spheres

The two icosahedral spheres and the outer shells come from 3D printing. The outer diameter of the spheres is about 78 mm, so each face must stay readable from across a desk. Calibration of the starting position relies on the magnet and the Hall effect sensor, which give a fixed reference on every cycle.

The overall dimensions of the clock are 383 x 78 x 163 mm. The base is divided into three parts: on the left and right are the motors and the drive shaft, in the centre the electronics. This division simplifies both assembly and maintenance, because the drivers can be reached without dismantling the spheres.

Anyone wanting to follow the project will find the build story signed by ekaggrat singh kalsi on the project page on Hackaday.io. The starting point, however, remains the mechanics: without accurate alignment of the bevel gears, no firmware keeps the numbers upright.

Source: https://hackaday.io/project/206586-ovodyo

Related products

• NEMA 11 stepper motor – 1 A
• LED DJ Flower Effect 4 x 3 W RGBA

The post OVODYO: the desk clock that rolls two icosahedra appeared first on Open Electronics.

Науково-технічна конференція «Технічний день автоматизації» KPI4U-2 вт, 10/13/2026 - 15:36

«Твій перший рік у КПІ» — зустріч із ветеранами й ветеранками, які вступили до магістратури в межах другого набору програми «Майбутнє протезування України» kpi вт, 10/13/2026 - 15:24

Knowing the mains frequency is important in many industries; the speeds of AC motors, for example, are dependent on it. The motor speed= (120*F)/P, where F is the mains frequency and P is the number of poles. Since P is a fixed number for any motor, speed varies in proportion to mains frequency. This frequency can be easily measured by MCU-based projects within a small gating time. The period is measured and 1/T is computed to obtain the frequency.

For non-MCU projects, the mains frequency can be measured with a simple counter within a gating time of 1 second. For 50 Hz (as followed in countries like mine, India), this will show 50 ± 1. If you increase the gating time to 10 seconds, the reading will be 500 ± 1. Finally, if you set the gating time to 100 seconds, the counter will show 5000 ± 1 which can be made to read 50.00. This accuracy is acceptable, but the gating time is too high for the measurement to update within a reasonable period.

What is the alternative? If you can multiply the frequency by 100 and then feed it to the counter, the counter will read 5000 within a gating time of 1 second, which can be made to show 50.00. You will get a resolution of 0.01 Hz while keeping the gating time as 1 second. This is what’s done by the circuit in Figure 1, which implements a by-100 frequency multiplier based on a modified PLL (phase lock loop).

Figure 1 This circuit multiplies the AC mains frequency by 100 and displays the result using a simple counter scheme. The component values shown assume a 50 Hz mains source.

How does it work?

• Transformer T1 gives an output of 6V AC; this same transformer can be used to produce 5V DC required to power the circuit. U1A is used as zero cross detector, whose output is the mains waveform converted to a square wave with a 50% duty cycle.
• The 6V AC output routes to pin 1 of U2A, an exclusive OR gate. This IC acts as a phase detector.
• U3, a 555 timer, functions as 5,000 Hz square wave generator.
• This frequency is divided by 10 using U4A, a 4518 counter.
• U4B, another 4518 counter, and U5, a 4585 comparator, operate as a frequency divide-by-5 circuit. In digital comparator U5, another 4585, inputs A0, A1, A2 and A3 are connected to the outputs Q0, Q1, Q2 and Q3 of the U4B previous stage. Input B3, B2, B1 and B0 are hardwired as 0101 (5). When the count in U4B reaches 5, its A=B output sends a pulse to U6A (another 4518 counter) and resets U4B so that it starts counting from zero again.
• U6A’s Q0 output divides the input frequency by 2.
• Thus, the combination of U4A, U4B, U5 and U6A operates as divide-by-100 circuit.
• This divide-by-100 output is connected to pin 2 of U2A, again acting as phase detector.
• U2A’s output feeds a low-pass filter circuit formed by R2 and C1, and is then connected back to pin 5 of the 555, circuit which works as voltage-controlled oscillator (VCO).
• Thus, the PLL circuit closes and the 555 output is then read in the counter as the AC mains frequency multiplied by 100.

If the mains frequency variation is in a narrow range of, say, 48 to 52 Hz, this PLL circuit works nicely. The power supply and ground pins of the digital ICs are not shown in the circuit diagram.

Note that since the divide-by-10 output of the 4518 IC is not a square wave with 50% duty cycle, the divide-by-10 second stage was done in two parts: divide-by-5 and divide-by-2, thus solving the challenge of the closing of the phase lock loop.

60 Hz operation notes

Countries such as the United States instead use a 60 Hz mains frequency. With some component changes, this circuit will also measure 60 Hz ± 2 Hz accurately.

• Transformer T1 should be a 110V/6V AC, 60Hz variant.
• The 555 multivibrator should be tuned to oscillate at 6000 Hz. R3=R4=11K and C2=0.01uF.
• For the low pass filter, R2=110K and C1=2.6uF.

The following video shows the operation of this circuit for 49.55 Hz and 50.55 Hz simulated mains frequencies.

Jayapal Ramalingam has over three decades of experience in designing electronics systems for power & process industries and is presently a freelance automation consultant.

Related Content

• Frequency division by any even number, with a 50% duty cycle output
• Unmasking the ghosts in your grid with power quality analyzers

The post Measure the AC mains frequency with an ordinary counter appeared first on EDN.

Повідомлення про додаткове публічне громадське обговорення проєкту Професійного стандарту "Оператор з обслуговування та експлуатації когенераційних установок" kpi вт, 10/13/2026 - 14:54

Рейтинги Національної системи дослідників України 2026 року KPI4U-2 вт, 10/13/2026 - 14:26

STMicroelectronics has highlighted its latest miniature 6-axis motion sensors designed to deliver accurate motion tracking and context-aware sensing for smart devices, drones and other electronic applications. Developed under the company’s Sustainable Technology framework, these sensors combine enhanced precision with compliance with stringent sustainable-materials requirements.

The new sensors combine six-axis motion sensing technology to allow electronic devices to identify and measure motion A 6-axis motion sensor generally includes both a three-axis accelerometer to sense linear acceleration and a three-axis gyroscope that measures the rate of rotation. These measurements combine to help devices recognize changes in position, orientation and movement.

“By maximizing the signal strength and resilience in an extremely compact form factor, the architecture of our latest 6-axis motion sensors will drive powerful new features in smart applications,” said Simone Ferri, Executive Vice President, MEMS Sub-Group, STMicroelectronics. “Moreover, meeting today’s leading sustainable-materials codes such as BPA-free, Pb-free and others helps smart-tech innovators address environmental and regulatory expectations as these become progressively tougher, encompassing additional classes of materials.”

In smart devices, this sensing ability can provide motion sensing, activity tracking, tilt and gesture-based control. In drones, the clear motion data can enable flight control systems to detect movement, track tilt and respond to tilt changes. The sensors can also enable other applications that require accurate motion sensing and event reconstruction.

“ST’s new LSM6DSK320X sensor delivers unprecedented vibration immunity that we just had to integrate and take advantage of immediately in our ongoing reference platform development,” said Jay Blackman, Leader, Betaflight.

“The LSM6DSK320X IMU is the new performance leader for smart and context-aware consumer technology, including UAVs,” said Dr. Zebo Zhou, Chief Technology Officer, Tianhe (Chengdu). “Drone flight controllers place extremely demanding requirements on an IMU: the sensor must not saturate during aggressive maneuvers, must maintain low noise under severe vibrations, and must be compact and power efficient. Of all the available devices we have evaluated, ST’s LSM6DSK320X was the best match.”

Technical Specifications

• The super-symmetric architecture comprises two mirrored electromechanical structures, acting in counter phase to each other, thereby cancelling the effects of external vibrations and shocks. Combined with a fully differential signal chain, this structure maximizes detected-signal strength and resilience.
• The internal structure is also extremely compact, enabling each IMU to contain one gyroscope with two separate accelerometers in a single package. The dual accelerometers allow simultaneous measurement of low-range disturbances up to ±16 g and high-g events up to ±320 g, ensuring these IMUs never miss an event. This contrasts with single-accelerometer IMUs that cannot perform measurements while switching between low-range and high-range modes.
• The consumer-oriented LSM6DSK320X and industrial-grade ISM6HGK256X also contain ST’s machine-learning core (MLC) that embeds AI processing directly in the sensor to reduce the application’s energy consumption and save system resources.
• Developers can leverage extra libraries including ST’s Motion XLF, and sensor-fusion and context awareness software, in addition to basic libraries, that enhance power and flexibility to address diverse applications.
• The new IMUs are supported within the ST MEMS Studio development environment. The STEVAL-MKI257A adapter board for the LSM6DSK320X and STEVAL-MKI258KA adapter board for the ISM6HGK256X will be available in the eSTore and from distributors by November 2026.
• The LSM6DSK320X for consumer applications operates over a broad temperature range, from -40°C to +85°C. The industrial-grade ISM6HGK256X handles temperatures from -40°C to +105°C and is included in ST’s 10-year longevity program that assures long-term component availability.
• The IMUs come in 2.5 mm x 3 mm x 0.83 mm packages and available from $4.6 for the consumer-oriented LSM6DSK320X and $5.1 for the industrial-grade ISM6HGK256X for orders of 1000 pieces.

The post STMicroelectronics Introduces Sustainable 6-Axis Motion Sensors for Smart Devices, Drones appeared first on ELE Times.

Built around Arduino and a special breakout board, it drives a relay when it detects and recognises a previously paired RFID tag; it is also suitable for reading contactless NFC cards.

RFID and NFC: a technology that is now widespread

Recognition and access control performed by turnstiles, gates and motorised doors driven by RFID electronic systems is now an established reality in many fields, involving both companies and apartment buildings; by now many of us carry in our pocket a passive wireless tag (transponder) that lets us enter the workplace, buy a drink from a food vending machine, get into the gym, but also open the front door of the house and clock in at work. The same technology, known as RFID, is the basis of the most recent and now widespread contactless electronic identification and payment systems, also known as NFC, built into debit cards or into mobile devices such as smartphones and tablets.

We devote the project you find in these pages to RFID/NFC technology; it is both a complete application for access control and a development and prototyping platform for very short range identification systems based on passive or active transponders (tags).

How a passive RFID tag works

Before getting into the heart of the project, let us make a preamble that helps in understanding how RFID works: systems of this kind behave as true transponders and are typically passive, meaning that the portable identification device (called a tag) only acts when it is excited by the fixed exciter system and has no autonomous power source of its own. Here is how it works: the detection system has an exciter circuit that generates an electromagnetic field, typically at low frequency (normally around 125 kHz, but systems operating at higher frequencies exist) and radiates it through a coil over a short distance; this ranges from a few cm for contactless payment systems and NFC (where transmission must be kept as narrow as possible to avoid fraudulent interception by hackers and malicious people) to a few tens of cm for pedestrian and vehicle access points.

Induction and identification of the tag

When a tag enters the electromagnetic field, an alternating voltage is induced in the coil inside it; this is rectified by a diode and charges a capacitor, between whose plates enough energy remains to run the tag’s logic. That logic is based on an encoder which, once powered, generates an identification code (usually unique) that drives the base of a transistor; at every logic high level the transistor conducts and electrically “loads” the coil, causing an induced reaction in the coil of the exciter circuit, as a result of which each conduction increases the current draw.

Through a sense resistor the exciter circuit detects the pulses and reconstructs the code produced by the encoder, thus managing to identify the tag. During the periods when the transistor loads the coil, the diode prevents the capacitor from discharging, so that the energy stored in it keeps the encoder running for the time needed to complete identification.

The project

That said, let us look at what the RFID identification system consists of; essentially it is a programmable transponder electronic lock with a user interface for easy use.

The project is based on an Arduino Uno fitted with an LCD Shield, a 5 volt relay module and a module specifically for reading RFID tags and NFC in general. The module in question ( Fig. 1 ) is the PN532, a NFC/RFID tag reader/writer (writing applies to active tags) with a coil integrated in the PCB, I²C, SPI and HSU (High Speed UART) communication interfaces, plus an indicator LED. The board, measuring 43x41x4 mm, can work as an RFID card reader/writer, as an NFC reader for Android smartphones (it only works with some types) and in wireless peer-to-peer mode between two similar devices.

The PN532 module: interfaces and supported tags

The circuit works with a voltage from 3.3 to 5 V and the communication interface, available on a row of pads on one side of the board, is selected via dip-switches as shown in Table 1 . In reality the board, as you can see in Fig.1 , carries two rows of contacts: one with four pads and one with 8; together they form both the data interfaces and the service connections, that is, power supply and control signals.

The row of four pads carries the I²C bus, namely SCL, SDA, common ground and power supply; the 8-pad row carries the complete SPI bus, so SCK, MISO, MOSI and SS, plus Vcc, GND, reset (RST0) and IRQ, which are pins to be used for direct control from a microcontroller: in particular IRQ provides a signal every time a tag detection is made, to request the microcontroller’s attention.

Supported tags and reading distance

Among the tags supported by the module are Mifare 1k, 4k (Mifare is a standard created by NXP Semiconductors and widely used in RFID and NFC cards), Ultralight and DesFire cards, as well as ISO/IEC 14443-4 cards such as CD97BX, CD light, Desfire, P5CN072 (SMX), Innovision Jewel cards such as IRT5001 and also FeliCa cards such as RCS_860 and RCS_854. The ISO/IEC 14443 standard provides for operation with an excitation electromagnetic field at a frequency of 13.56 MHz.

The maximum tag capture and communication distance for our PN532 module is typically between 30 and 70 mm. The module can be used with Arduino to build access control devices, etc., thanks to the library provided for managing it.

The library for managing it is supplied by Adafruit and can be downloaded both from the manufacturer’s site and from the page www.futurashop.it/NFC-RFID?search=RFiD (the library is called adafruit pn532).

Practical build

Good, let us now move on to assembling the system components; to sum up, the hardware as a whole consists of:

• Arduino UNO;
• LCD Keypad Shield (1686-SHIELD-LCD);
• RFID module (2846-PN532);
• 5V-10A relay module (2846-RELAY1CH).

The whole set is available from Futura Elettronica (online shop www.futurashop.it) and the product codes are those shown in brackets. Once the hardware has been obtained, it must be connected with Arduino jumpers following the connections given in the wiring diagram you find in these pages.

As for the RFID module, the connections to Arduino, or rather to the LCD Shield, are:

• the SCK contact goes to pin 13 of the shield;
• the MISO contact goes to pin 12 of the shield;
• the MOSI contact goes to pin 3 of the shield;
• the SS contact goes to pin 2 of the shield;
• the VCC contact goes to the 5V pin of the shield;
• ground (GND) goes to the GND pin of the shield.

Relay connections and user interface

The relay module, on the other hand, connects with its 5V to the 5V of the Arduino (together with the power supply of the RFID module) and GND to a free GND pin of the Arduino (or of the LCD Shield); the relay’s control input must be connected to pin D5 of the Arduino (of the Shield).

To manage the LCD shield, which forms the user interface of the system, the sketch includes the appropriate library (also downloadable from www.futurashop.it), which allows the alphanumeric display to be controlled and the buttons to be read.

Speaking of buttons, in the project they have the following functions:

• UP and DOWN let you move between menu items;
• SELECT is used to choose a menu item;
• DOWN starts interrogation of the transponder;
• LEFT, from the display’s initial screen, lets you test the output relay;
• RST; returns you to the initial screen.

To use the system we recommend enclosing all the electronics in a plastic case machined so that the buttons protrude and the LCD display remains visible; the RFID board can be glued with silicone sealant just below the front panel of the case, which, being plastic, does not interfere in any way with the electromagnetic field generated by the coil or with detection of the induced reaction.

That said, we can look at the system’s features.

Start-up and main menu

So let us take a look at how the whole thing works, starting by saying that it must be powered through the DC jack of the Arduino Uno with a mains power supply able to deliver at least 500 milliamperes and a voltage of 9 to 10 volts.

As soon as the device is powered up, the LCD display shows the words “Access Card Reader” ( Fig. 2 ).

The message “Lettura: Lettura in corso” (Reading: reading in progress) is then shown. The system will then read any tag present within range (the electromagnetic field) of the module, replying with a message granting or denying authorisation. Of course, all this only makes sense if the system has previously stored one or more tags, otherwise authorisation will always be denied; when authorisation is granted, the relay is activated.

From the main screen, pressing the “DOWN” button takes you to the menu, which offers the following options:

• Read Mode (Fig. 3), corresponding to the initial tag reading procedure;
• Edit Mode (Fig. 4), corresponding to the procedure for learning a tag and writing it to the Arduino memory;
• Delete Mode (Fig. 5), which lets you delete all the tags already learned whose code is in the Arduino memory.

Use the “UP” and “DOWN” buttons to scroll through the options, and press “SELECT” to select one. Press “RST” to return to the initial procedure at any time. While the “Main menu” screen is displayed you can press the “LEFT” button to test that the relay works correctly.

Edit mode: registering a new tag

Let’s briefly look at the modes, starting with Edit Mode: once selected, it lets the user register a new tag.

The procedure, which starts when the display shows the screen in Fig. 4, requires you to pass the tag to be learned over the module at a maximum distance of 7 cm, at which point the system automatically acquires its code and saves it among the authorised ones. If the tag is not detected (you will notice because the display content does not change), try passing it at a shorter distance until it is detected.

Bear in mind that the module can recognise certain types of tag, which are also the most common ones, so the tags you want it to learn must be among those summarised a few paragraphs back.

In any case, if the storage operation succeeds, the display will show the message “TESSERA SALVATA” (Fig. 6).

At this point, if you stay in Edit Mode and pass the same tag you have just learned again, its code is removed from memory; this deletion state is confirmed by the message “TESSERA RIMOSSA” (Fig. 7).

Delete mode and read mode

Let’s move on to Delete Mode, which starts when the display shows the screen already seen in Fig. 5; from there, to remove all the previously learned tags from memory you have to press the SELECT button; once you do, the display shows the screen visible in Fig. 8.

When the operation is complete you have to press the “RST” button to return to the main menu. Once the memory has been cleared, the display shows the screen visible in Fig. 9.

We finish with Read Mode: when you enter it from the main menu (with the screen of Fig. 3 on the display) and pass a tag in front of the module, what is shown in Fig. 8 appears.

In practice, when the module detects a tag within its field of action it interrogates it, and during this phase the display shows the message of the aforementioned figure. When the read is complete, two conditions shown in Fig. 9 can occur: if the tag is one of those already learned in Edit Mode, it is recognised and the display shows the message Acc. Consentito, together with the relay switching on. If instead the tag is not recognised as one of those already learned, Acc. Negato is shown and nothing happens to the relay.

Customising the firmware

These are all the functions provided, but of course by “tinkering” with the firmware you can implement others; for example, you can add various kinds of local signalling to the relay output, such as a buzzer that sounds when a tag is recognised or an LED that lights up when the aforementioned condition occurs.

If you want, without “tampering” with the Arduino sketch you can add an LED or a buzzer by connecting them in parallel (the LED requires a limiting resistor, typically 270 ohm…) to the relay coil, that is in series with the collector of an NPN transistor connected in common emitter with its base driven by the Arduino Uno output that controls the relay board.

The firmware

Having looked at the hardware and described how it works, let’s spend a few lines on the firmware that manages the electronic lock.

The sketch to load into the Arduino Uno board is quite bulky, so in Listing 1 we only reproduce the most significant portion, the one dealing with normal operation. You can download the whole sketch (.ino file) together with the files for this issue of the magazine.

Listing 1

if (success) { // Display some basic information about the card if(debug) { Serial.println(“Found an ISO14443A card”); Serial.print(“ UID Length: “);Serial.print(uidLength, DEC);Serial.println(“ bytes”); Serial.print(“ UID Value: “);nfc.PrintHex(uid, uidLength); } cardid = getCardIdAsInt(uid,uidLength); //is MASTER if (cardid == uid_master) { master_mode = 1; master_mode_counter = 0; if(debug) Serial.println(“MASTER detected, going into MASTER MODE “); } // is not MASTER else { int32_t findUid = findRfidInEeprom( uidLength, uid); // is MASTER MODE, include or exclude RFID from storage if(master_mode == 1) { // card rfid already exists so exlude it from storage if( findUid != -1) { if(debug) { Serial.println(“removing card from eeprom”); Serial.println(“ “); lcd.setCursor(0,1); lcd.print(“Tessera Rimossa “); delay(2000); lcd.setCursor(0,1); lcd.print(“Modifica… “); } deleteRfidfromEeprom(findUid, uidLength); } // card rfid not found in storage so include it else if (findUid == -1) { // check if space to store rfid available int32_t storageAddress = getEepromStorageAddress(uidLength); // storage available if(storageAddress >= 0) { if(debug) { Serial.print(“storing card in position = “);Serial.print(storageAddress);Serial.println(“ “); } // storing card writeRfidToEeprom( storageAddress, uidLength, uid); lcd.setCursor(0,1); lcd.print(“Tessera Salvata “); delay(2000); lcd.setCursor(0,1); lcd.print(“Modifica… “); } else { // no storage space available or error if(debug) Serial.println(“memory full or error”); } } master_mode = 0; } // no MASTER MODE, authorise or deny door access else { // card authorised if( findUid != -1) { // open door // write routine for door access, example set an output to high if(debug) Serial.println(“Card authorised”); lcd.clear(); lcd.setCursor(0,0); lcd.print(“Lettura:”); lcd.setCursor(0,1); lcd.print(“Acc. Consentito”); digitalWrite(11,HIGH); delay(2000); digitalWrite(11, LOW); lcd.setCursor(0,1); lcd.print(“Lettura in corso”); } else { // deny access if(debug) Serial.println(“Card not authorised”); lcd.clear(); lcd.setCursor(0,0); lcd.print(“Lettura:”); lcd.setCursor(0,1); lcd.print(“Acc. Negato”); delay(2000); lcd.setCursor(0,1); lcd.print(“Lettura in corso”); } } } delay(500); }

As usual the firmware starts with the definition of the I/Os and the inclusion of the libraries, of which there are two in this project: the one for the RFID reader and the one for the LCD Shield.

The setup follows and then we come to the portion shown in the aforementioned Listing 1, where, depending on the mode we have set using the shield’s buttons, the card (the RFID tag) will be stored, deleted or, in the case of “normal” operation, simply read to compare its identification code with the data in memory; in this case, if the code read is already present in the Arduino Flash memory, the relay on the board is activated.

The relay is activated in pulse mode, that is monostable, and the time it stays in the “energised” state (and therefore with the relay contact closed) is 2 seconds, as defined by this portion of code:

digitalWrite(11,HIGH); delay(2000); digitalWrite(11, LOW);

where digitalWrite (11, HIGH) is the instruction that activates digital line 11 (which controls the relay board), delay(2000) sets the time it stays at high level through the delay with which output D11 returns to low level and, finally, digitalWrite (11, LOW) brings the aforementioned Arduino line back to logic low.

If you want, you can change the relay activation time by changing, in the sketch, the value in brackets after delay, remembering that it is in milliseconds.

Conclusions

So we have reached the end of the description of this project, which, although it was born as an electronic lock with a transponder key, can be used for a variety of applications and, with a few tweaks to the firmware, can find use in identification via NFC thanks to the full support provided by the sensor board and its pairing with a Personal Computer.

Related products

• RFID NFC 13.56 MHz Read-Write Module
• LCD keypad shield for Arduino
• NFC RFID reader with two transponders

The post NFC/RFID Lock with LCD appeared first on Open Electronics.

Nuvoton Technology introduces the NSP2340BA24E Smart Buzzer, integrating voice capabilities and buzzer driving functions into a single IC. Designed to extend the capabilities of conventional piezo buzzers beyond simple beep sounds, the NSP2340BA24E enables direct playback of voice and sound effects, bringing more intuitive and distinctive audio interaction to home appliances, security systems, healthcare devices, industrial equipment, and other smart applications.

Conventional buzzers have long been widely used in electronic products for audible alerts and operation feedback due to their simple architecture and proven reliability. However, as smart devices become increasingly sophisticated, simple beep patterns alone may not provide the intuitive and informative user experience required by modern applications.

Built around the concept of “Beyond the Beep,” the NSP2340BA24E expands the role of the conventional buzzer by enabling richer and more expressive audio feedback.

From Simple Beeps to Voice & Melody

The NSP2340BA24E Smart Buzzer integrates embedded Flash and a buzzer driver, together with a voltage booster and Class-AB amplifier, enabling direct drive of a piezo buzzer for voice and sound-effect playback. This transforms the buzzer from a simple audible alert component into a more informative audio interface.

With voice and diverse audio content, products can communicate operating status, notifications, and warning messages more clearly. Instead of relying solely on different beep patterns to indicate different conditions, the NSP2340BA24E enables more recognizable voice or sound feedback, helping users understand device status more intuitively. This approach allows developers to build upon familiar piezo buzzer designs while creating greater product differentiation and a more engaging audio experience.

Highly Integrated Design for Easier Implementation

The NSP2340BA24E integrates audio content storage, voltage boosting, and buzzer driving capabilities into a highly integrated solution. The NSP2340BA24E supports booster output of up to 24V, along with UART, I²C, and Two-Wire interfaces for host MCU control. It also supports In-System Programming (ISP), allowing audio content to be updated through the host MCU.

With this integrated architecture, developers can more easily introduce Voice & Melody capabilities into products traditionally designed around buzzers, while maintaining flexibility to customize audio content for different products and application requirements.

Expanding the Value of Buzzers Across Smart Applications

The NSP2340BA24E can be applied across a broad range of products requiring audible feedback and status notification. From operation and completion notifications in home appliances to warning alerts in security systems and status feedback in industrial equipment, the NSP2340BA24E allows sound to do more than simply attract attention—it can help users better understand what a device is communicating.

With the NSP2340BA24E Smart Buzzer , Nuvoton extends the conventional piezo buzzer beyond simple beep sounds to richer Voice & Melody experiences, providing developers with greater flexibility in audio design and enabling more intuitive audio interaction for the next generation of smart devices.

The post Nuvoton Introduces NSP2340BA24E Smart Buzzer, Redefining the Buzzer Sound Experience appeared first on ELE Times.

Polar Semiconductor LLC of Bloomington, MN, USA (the only US-owned foundry specializing in high-voltage, power and sensor semiconductors) and IC-Link by imec (the design and manufacturing division of nanoelectronics research center imec of Leuven, Belgium) are collaborating to provide 8-inch production of enhancement-mode (e-mode) gallium nitride (GaN) devices to serve critical markets such as AI data centers, automotive, aerospace & defense, and the industrial grid...

Infineon Technologies AG introduces the XDP8HD40S-1, a high-voltage hot-swap controller designed for digital power path protection and monitoring in next-generation AI server power architectures operating at ±400 Volt and 800 Volt direct current (VDC). As AI clusters scale from 48 V toward higher rack power levels, reliable hot-swap functionality becomes increasingly important for maintaining uninterrupted system operation. The XDP8HD40S-1 enables power modules, converters, GPUs, and server subsystems to be connected and disconnected safely while preserving system integrity, extending Infineon’s power semiconductor portfolio, spanning silicon, silicon carbide (SiC), and gallium nitride (GaN), further into the protection and monitoring layer required for scalable AI infrastructure.

“Future AI data centres require power delivery systems that combine high efficiency, maximum reliability, and comprehensive protection capabilities,” said Magdalene Boebel, Senior Vice President and Business Line Head of Power System ICs at Infineon. “The XDP8HD40S-1 enables system designers to implement reliable hot-swap solutions for ±400 V and 800 V power architectures while providing the monitoring and protection features needed to maximize server uptime and operational efficiency.”

The XDP8HD40S-1 is designed for a range of high-voltage applications, including two-wire 800 V or ±400 V intermediate bus converters and GPUs, 1.500 V intermediate bus converter systems, high-voltage rack and server power delivery, battery backup systems, peak shaving, and 400 V to 800 V single- or dual-side hot-swap implementations. The device is compatible with Infineon’s power semiconductor portfolio, including OptiMOS MOSFETs and CoolSiC JFETs, allowing designers to build efficient, rugged protection stages using the same underlying technologies Infineon has already applied in its 800 VDC AI data center reference designs.

Operating at higher system voltages increases the complexity of controlling inrush current and protecting infrastructure from transient events. The XDP8HD40S-1 integrates digital Safe Operating Area (SOA) control with programmable and preconfigured active protections. Together with a variable pulse SOA control mode (Boost Mode), the device supports optimized startup behavior while maintaining safe operation of the power switch. An integrated gate driver supports external SiC cascode implementations based on a MOSFET and JFET combination, enabling efficient control of high voltage power stages. To further enhance system robustness, the controller incorporates surge immunity protection and supports fast power-switch shutdown with a configurable 1.5 A pull-down current, helping protect AI compute infrastructure during abnormal operating conditions.

The XDP8HD40S-1 supports a broad operating voltage range, with VDDB from 4.5 V to 24 V and VDDA from 4.5 V to 13.2 V, allowing integration into a variety of server and rack-level power systems. For system management and remote diagnostics, the controller includes a PMBus 1.5 interface with enhanced security features for integration into data centre management infrastructure. A built-in black-box recording feature captures critical event information to help accelerate fault analysis and system debugging. Additional protection features include programmable input and output overvoltage and undervoltage protection, external temperature sensor support, and overtemperature protection.

Modern AI server platforms increasingly require real-time visibility into power consumption and system health. The XDP8HD40S-1 addresses this through 12-bit current and voltage analog-to-digital converters, providing measurement accuracies of approximately 1 percent for voltage, 1 percent for current, and 3 percent for power, with analog power monitoring reporting accuracy of 1.5 percent and integrated energy metering for continuous system monitoring. These capabilities support system optimization, predictive maintenance, and power management strategies in high-density AI computing platforms, complementing the reference designs Infineon has already demonstrated for 800 V-to-48 V and direct-conversion architectures now emerging as AI data center rack power continues to rise.

The post Infineon Introduces XDP8HD40S-1 High-Voltage Hot-Swap Controller, Extending its Power Semiconductor Portfolio From Grid to Core for 800 VDC AI Server Architectures appeared first on ELE Times.

HARTING Technology Group, a leading global provider of industrial connectivity, today announced it has joined the NVIDIA MGX ecosystem as a power system component provider for the NVIDIA Vera Rubin NVL72 platform.

HARTING delivers power whip solutions – cable assemblies that distribute power from the building to each rack – as growing AI compute demands reshape how power is distributed within data centres. The solutions span the transition across two generations of NVIDIA MGX power architecture, from 480 VAC to the NVIDIA 800 VDC architecture, designed to support the rapidly increasing power demands of AI factories.

As AI workloads grow and compute density increases, data centres must manage significantly more power within increasingly dense environments without compromising reliability, efficiency or scalability. That shift is placing new demands on the connectivity systems that deliver power to AI compute infrastructure.

“AI is fundamentally changing data centre infrastructure, and HARTING has the engineering expertise to help enable that transformation,” said Philip Harting, Chairman of the Board and CEO, HARTING Technology Group. “Our inclusion in the NVIDIA MGX ecosystem brings our decades of connectivity experience in high power environments, from rolling stock to automotive, to the infrastructure supporting the next generation of AI.” For the NVIDIA MGX platform, HARTING provides a 100A/480VAC power whip for the 3RU 110kW power shelf for NVIDIA Vera Rubin NVL72, supporting the transition to 800 VDC with a next-generation 157A/800 VDC DC-DC power whip connecting the power infrastructure to the compute rack.

“As AI compute becomes more powerful and rack densities increase, the industry has to rethink power distribution from the ground up,” said Christian Gysin, Chief Technology Officer and Member of the Executive Board, HARTING Technology Group. “The transition from 480VAC toward 800 VDC is a significant step in that evolution. Connectivity must support higher power levels while delivering the reliability, safety and scalability these environments demand. That is precisely where HARTING’s engineering expertise comes into play.”

HARTING solutions transmit power, data and signal across mission-critical systems worldwide. The company is drawing on decades of experience in demanding industrial environments and its global engineering and vertically integrated manufacturing capabilities to address the evolving connectivity requirements of AI infrastructure and data centres.

As part of the NVIDIA MGX ecosystem, HARTING will be featured on the NVIDIA AI Factory MGX Ecosystem wall at the OCP Global Summit 2026. The executives from HARTING will honourably attend the NVIDIA MGX ceremony. Besides, two HARTING power whip solutions supporting the NVIDIA Vera Rubin NVL72 platform will also be on display in the HARTING booth (Booth No. C79) demonstrating the power connectivity behind current and next-generation AI infrastructure.

The post HARTING Joins NVIDIA MGX Ecosystem to Power Next-Generation AI Infrastructure appeared first on ELE Times.

As AI workloads drive demand for more scalable and flexible data centre architectures, organizations are looking for faster, more efficient ways to move, store and protect data. Microchip Technology is helping to address these needs by expanding its portfolio of data centre solutions with new innovations in PCIe connectivity, NVMe storage and acceleration that support high-performance computing infrastructure.

“Customers are looking for technology partners that can address multiple challenges across the infrastructure stack while helping simplify design and accelerate deployment,” said Brian McCarson, Corporate Vice President and GM of Microchip’s data centre solutions business unit. “By continuing to expand our portfolio, we’re providing the building blocks needed to create more efficient, secure and adaptable platforms for today’s increasingly distributed and data intensive computing environments.”

Designed to bring PCIe Gen 6 connectivity to space- and power-constrained platforms, the Switchtec PCIe 6.0 Low Lane-Count (LLC) switch provides up to 64 PCIe Gen 6 lanes, 32 ports and flexible x2, x4, x8 and x16 bifurcation options. Integrated Direct Memory Access (DMA) supports efficient peer-to-peer data movement between connected devices while reducing host CPU involvement. Like Microchip’s larger Switchtec Gen 6 devices, the LLC switch features PCIe Multicast and partitioning capabilities along with robust security through hardware root of trust and secure boot technologies. The new device delivers PCIe Gen 6 capabilities with a smaller, lower power footprint to extend support to a broader range of data centre and intelligent edge applications.

To address the industry’s requirements for secure, scalable and AI-optimized storage infrastructure, Microchip is updating its Flashtec SSD controller portfolio with an enhanced version of the PCIe Gen 5 NVMe 5016 controller. Building on the controller’s high-performance, power-optimized architecture, the updated device adds support for Commercial National Security Algorithm Suite (CNSA) 2.0 to help customers prepare for emerging post-quantum security requirements. Dual-port capability enables highly available storage architectures through redundant data paths and load balancing, while advanced virtualization, Quality of Service (QoS) management and flexible programmability help optimize performance. Together, these enhancements extend the Flashtec platform’s ability to address evolving NAND technologies, high-capacity SSD designs and next-generation enterprise, hyperscale and AI deployments.

Microchip is also expanding the capabilities of its Adaptec SmartRAID 4300 family of NVMe RAID storage accelerators with a series of enhancements designed to address practical operational needs. The updates are focused on strengthening storage security, minimizing downtime, expanding data protection and simplifying deployment across a wider range of environments. Self-encrypting drives (SEDs) with both local and remote key management, and RAID 6 and RAID 60 options have been added for increased resiliency and security. Broader platform compatibility is enabled through support for Linux, Debian, Ubuntu and Microsoft Windows 11 environments. Additional updates such as dynamic NVMe configuration for in-service management, persistent event logging and peer-to-peer rebuild support for greater CPU offload help reduce management overhead while minimizing service disruption. StorageCore API support further expands integration flexibility, enabling customers to incorporate SmartRAID management into third-party tools through in-band host applications as well as customized out-of-band BMC management frameworks.

The latest additions reflect the company’s ongoing commitment to advancing the performance, availability and security of next-generation computing platforms. Visit the website to learn more about Microchip’s full portfolio of data centre solutions.

Development Tools

Microchip’s data centre portfolio is supported by a suite of development and management tools, including ChipLink software for configuration, diagnostics, telemetry and debug of PCIe switches. Additional support includes software for device monitoring, configuration and firmware management, Flashtec NVMe controller development resources and Adaptec storage management software.

The post Microchip Expands Data Centre Portfolio to Meet Evolving AI Infrastructure Demands appeared first on ELE Times.

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The JetKVM Mini delivers KVM over IP for 39 dollars. The board captures a computer’s video, encodes it in H.264 and sends it to the host over WebRTC. It also drives keyboard and mouse as if they were physically connected. Compared with the original JetKVM, which relied on Linux running on a Rockchip RV1106G3 SoC, the Mini uses an ESP32-P4X for video capture, H.264 encoding, USB and the rest of its functions. The project therefore moves to RISC-V and to a FreeRTOS firmware based on ESP-IDF.

The Mini variant has a 100 Mbps RJ45 Ethernet port. The Mini W adds dual-band WiFi 6, Bluetooth and 802.15.4 for Zigbee and Thread thanks to an ESP32-C5 module. Pricing is 39 dollars for a single unit and 33 dollars per unit in a three-pack bundle. The Mini W costs 42 dollars, or 36 dollars per unit in the three-pack bundle.

ESP32-P4X: dual-core 400 MHz RISC-V CPU and 32 MB of PSRAM

The heart of the Mini is Espressif Systems’ ESP32-P4X. Inside there is a 32-bit dual-core RISC-V HP CPU running at up to 400 MHz, with an AI instruction extension and single-precision FPU, plus a RISC-V LP MCU core at up to 40 MHz. Memory includes 768 KB of HP L2MEM, 32 KB of LP SRAM, 8 KB of TCM, 32 MB of PSRAM, 128 KB of HP ROM and 16 KB of LP ROM. For storage there is 16 MB of flash and a microSD card slot, used for virtual media.

The VPU supports the H.264 and JPEG codecs, with a Pixel Processing Accelerator and a 2D DMA controller. The GPU is a 2D Pixel Processing Accelerator. The display is a 240 x 240 pixel non-touch IPS panel. The HDMI input goes up to 720p60 or 1080p30, with 4K supported through JetKVM OS Services. The H.264-encoded stream is sent to the host over WebRTC.

USB ports, 5V/500mA power and 42 x 42 x 23 mm dimensions

On the connectivity side, the Mini offers two USB 2.0 Type-C ports. The main one runs at 480 Mbps, the second at 12 Mbps. Power is 5V/500mA through a USB Type-C port. Dimensions are 42 x 42 x 23 mm, so the device fits in a fist. Those who want to rework the firmware development side will find in the catalogue the development board with Wi-Fi, Bluetooth and USB Type-C connector, useful for getting familiar with the ESP32 ecosystem before getting hands-on with the KVM.

• 32-bit dual-core RISC-V HP CPU up to 400 MHz with AI extension and FPU
• RISC-V LP MCU core up to 40 MHz
• 768 KB of HP L2MEM, 32 KB of LP SRAM, 8 KB of TCM
• 32 MB of PSRAM, 128 KB of HP ROM, 16 KB of LP ROM
• 16 MB of flash and microSD slot for virtual media
• 240 x 240 pixel non-touch IPS display
• 100 Mbps Ethernet over RJ45 in the Mini variant
• Dual-band WiFi 6, Bluetooth and 802.15.4 in the Mini W via ESP32-C5

The software runs on FreeRTOS with the ESP-IDF framework. Capture and encoding go through JetKVM OS services, while transport to the browser relies on WebRTC. Those who want to follow development and read the code will find the project’s code repository, where the firmware is published and updated. Open source firmware was planned from the start, so the Mini also suits anyone who wants to modify the capture logic or add features.

What a KVM over IP like the JetKVM Mini is for

A KVM over IP is useful when the computer to be controlled is far away or has no operating system running. The Mini gets into the BIOS, the boot screen or the bootloader, because it works at the level of the video signal and keyboard and mouse emulation. That is why it is used for rack servers, for mini PCs without a monitor and for test machines in the lab. The 100 Mbps Ethernet port is enough for a 1080p30 stream, while the W variant frees you from the network cable.

The 240 x 240 pixel IPS display shows status information directly on the device, without opening the browser. The microSD slot also lets you mount ISO images as virtual media, so an operating system can be installed remotely. Finally, the second USB 2.0 Type-C port at 12 Mbps handles the emulation peripherals. Those who want a starting point for their own IoT experiments will find in the catalogue the development kit for IoT projects with WiFi 6 and integrated peripherals, a way to get practice with the radios in the Espressif family.

Source: https://github.com/jetkvm

Related products

• ESP32 – Development board with Wi-Fi, Bluetooth and USB Type-C connector
• ESP32-C6-Zero Wi-Fi 6 IoT Development Kit

The post JetKVM Mini: $39 KVM over IP Built on ESP32-P4X appeared first on Open Electronics.