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In my local maker space, someone asked about taking a project from a breadboard to a prototype board and wasn't quite ready to go buy a printed PCB quite yet. I think I've seen this topic come up a few times so I figured I'd share how I handle this. Basically, I place all my parts on the prototype board and then draw out where I think all the traces need to go, then I remove all the components. Then I take copper tape and tape over the connections. Then, I place all the parts back on and double check that everything looks good before soldering everything. NOTE: In my my examples below, I made the copper tape thick so it'd show up easier on camera, but if you are doing this for real, you want to copper tape to be thinner than the pads so that the solder connects the tape to each pad along the way. Here is a demonstration (without any components) that I did on a scrap prototype board: Place the copper tape down on the board and press it firmly in place. Using a craft knife, cut away the extra portion (thinner than what I've done here) so that you can see the pads along the length of the trace. You can reuse the portion that you've cut away, but be careful not to touch the adhesive side because it is very weak and the oil from you hands will prevent it from sticking. Finish trimming your trace, then add flux and solder over it. Normally when you solder a component, you want to tin your tip and then add solder to the pad (not your iron), but when doing these traces, you just need the solder from your tip. Ensure that each end that connects to a part is firmly soldered like normal. There are a log of reasons that I like this method. First, it is very intentional. You get to map out all of the work ahead of time and double and triple check your work before soldering anything to the board. Second, if you are planning on designing a PCB, you can use this intermediary step to test your assumptions and figure out if there are any problems. Finally, this method allows you to use both sides of the prototype board for your traces. This can be especially helpful when connecting components to ground without crossing over other traces. This isn't a "You should do this and sell this on the market" thing, but a quick "try it before you buy it" approach to transition from your breadboard to a prototype board and all it costs is a little time and some copper tape. submitted by /u/FrequentlyVeganBear [link] [comments]

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Bel Fuse Inc., a worldwide manufacturer of reliable components, systems and solutions for mission-critical applications in the industrial, medical and data solutions markets, has announced the XRCM 200 and 300 series, the second generation of DC-DC converters with an ultra-wide input voltage range. Designed for the most demanding rail and transportation applications, the new converters combine a compact form factor, an ultra-wide input voltage range and high efficiency to help engineers simplify system design while meeting the strict reliability and safety requirements of the rail sector.

“Rail operators and system designers need power solutions they can rely on, and that is at the heart of our work at Bel,” said Silvan Mueller, Business Development Manager for Rail Power Products. “This new generation of the XRCM series was designed to simplify installation, combining support for a wide input voltage range, a reduced footprint and the protection features technicians need to build resilient transportation systems.”

Key features

The dual-output XRCM 200 converter delivers 200W at ±15V and is a more compact and cost-effective option with low power consumption. The single-output XRCM 300 converter delivers 300W at 24V and offers an extended temperature range for continuous operation at an ambient temperature of up to 85°C. Both feature high power density, low inrush current and full protection against overheating, overvoltage, overcurrent and short circuits. Both are also designed to meet the latest safety standards, including EN 50155, EN 50121-3-2 and AREMA.

Shown at InnoTrans 2026

The converters were presented at the Bel Fuse booth (Hall 17, Stand no. 310) at InnoTrans 2026 in Berlin, held from 22 to 25 September, together with other power conversion solutions designed for modern rail and transportation systems. Both converter families support DC input voltages from 16.8V DC to 137.5V DC and are compatible with all the most common rail battery systems, allowing OEMs to standardise on fewer power platforms across multiple vehicle architectures.

Together, the XRCM200 and XRCM300 converters give engineers versatile power conversion solutions that reduce integration effort, improve system reliability and support the evolving needs of modern rail electronics.

Summary

• Product series: XRCM
• Applications: rail sector
• Distribution availability: to be defined

For more information on the XRCM series, visit the dedicated DC-DC Converters page. Other product lines are available on the Bel Fuse website.

The post Bel Fuse launches XRCM200 and XRCM300 converters for rail appeared first on Open Electronics.

The wireless charging is not working what might be the issue. Im afraid if i pull the board out it will give me trouble to place it back submitted by /u/Jazzlike-Maybe-6424 [link] [comments]

JBR-001 is an open source desktop robot that sees and recognizes the objects in front of it, then reacts with movements, sounds and animations. It was created by syntheticAIdata and built around the Arduino UNO Q 2GB. Inside there are three servos, a camera, a distance sensor, a buzzer and an animated display. So it is not yet another blinking ornament: it is a platform for experimenting with robotics, computer vision and edge AI on a workbench.

Assembly starts from a body of 3D-printable parts that house the board and the rest of the electronics. The head contains the camera, mounted so that it looks straight ahead of the robot. On the back of the same head sits a Modulino Distance, which continuously measures the distance to nearby objects. The arms and the rotation of the head, on the other hand, come from three servos. The mechanics are therefore easy to replicate for anyone who already has a 3D printer at home.

Servo pins and distance thresholds in the code

The three servos split the tasks: one rotates the head, the other two control the arms. In the code the pins are HEAD_SERVO_PIN = 9, LEFT_ARM_SERVO_PIN = 10 and RIGHT_ARM_SERVO_PIN = 11. The rest positions are all at 90 degrees, so HEAD_CENTER = 90, LEFT_ARM_CENTER = 90 and RIGHT_ARM_CENTER = 90. The servos also use a separate external power supply instead of drawing power from the UNO Q: a forced choice when three motors start at the same time.

The distance sensor decides when the robot should notice someone. In the code the detection threshold is DETECTION_DISTANCE = 200.0, while the reset one is RESET_DISTANCE = 250.0. The units are not specified in the project material, so they should be checked on the bench before trusting them. The margin between the two thresholds prevents the robot from switching on and off continuously when someone stays right on the boundary.

Heartbeat on the display, a greeting in notes and the vision model

The robot’s responses go through three different channels. The LED matrix display is driven with bitmaps of 8 rows by 13 columns, the two used for the small and large heart. In the heartbeat animation the delays are 120, 100, 160 and 700 milliseconds, and the last long gap gives the sense of the pause between one beat and the next. The sound greeting playHello instead uses the notes 523 Hz (C5), 659 Hz (E5), 784 Hz (G5) and 1047 Hz (C6), that is a C major arpeggio rising by one octave.

The most interesting part comes from the camera mounted in the head. It connects through a USB-C hub and gives the robot a front view of its surroundings. The captured images can be processed on the UNO Q with a trained computer vision model: this way JBR-001 does not just understand that something is nearby, but identifies the object and uses the result to decide how to react. Consequently the same hardware can turn its head, move its arms, change the image on the display or play a short sequence.

• Arduino UNO Q 2GB as the robot’s brain
• Modulino Distance for continuous distance measurement
• Modulino Motors for motion control
• Modulino Buzzer for sound sequences
• Arduino USB-C Hub (8 in 1) to connect the camera
• Three servos for head and arms
• Camera mounted in the head
• 3D printer for the body parts

On the software side you need Arduino App Lab and the JBR-001 source code, plus a trained computer vision model for object recognition. The project has 1805 views and 2 respects. Anyone who wants to rebuild it starts here: the maker syntheticAIdata’s site, where the code and the instructions to replicate the build are. For the servos, an RC servo control module simplifies the wiring when the channels to drive grow. The project’s distance sensor is a Modulino, and as an alternative you can consider a ToF laser sensor for close-range measurement. The board everything runs on is the 2GB version, while the catalogue also lists the Arduino UNO Q 4GB in a complete kit: same family, more memory for vision models.

Source: https://projecthub.arduino.cc/syntheticaidata/jbr-001-a-desktop-companion-robot-powered-by-arduino-uno-q-b11c96

Related products

• Arduino shield for RC servo control
• ToF Laser Distance Sensor with VL53L3CX
• Arduino UNO Q 4GB – Complete kit with accessories

The post JBR-001: the open source desktop robot that recognizes objects appeared first on Open Electronics.

Arrow Electronics has announced global access for customers who want to remotely evaluate NXP® Semiconductors’ discrete Ara240 neural processing unit through the Digital Test Drive capability, giving engineers the chance to get early access to advanced AI models and technologies for computer vision, generative AI and multimodal systems, and letting them assess performance and start a project before investing in hardware.

The importance of edge AI

As AI adoption accelerates across industrial, robotics and intelligent vision applications, developers increasingly look to implement more sophisticated AI workloads closer to where data is generated. Working with AI locally at the edge can help reduce latency, improve data privacy and limit dependence on cloud-based processing, particularly in time-sensitive applications.

Features of the NXP Ara240 DNPU

Designed to support these requirements, the NXP Ara240 DNPU delivers up to 40 tera eTOPS (equivalent operations per second), supporting workloads that include LLMs (large language models), VLMs (vision language models), transformers and CNNs (convolutional neural networks). With LPDDR4 memory support up to 16 GB, this device makes it possible to run larger and more complex AI models directly on edge and embedded systems, without having to rely on cloud resources.

Arrow’s Digital Test Drive platform

To help those who want to explore these capabilities, Arrow has made NXP’s Ara240 solutions available through the Digital Test Drive platform, often described as a “digital test platform”: a cloud-based, Arrow-managed remote hardware testing and development environment that lets you evaluate physical embedded boards over the web, without having the hardware on your desk.

This makes it possible to remotely access live systems based on NXP i.MX applications processors paired with the Ara240 DNPU, in order to evaluate real AI applications such as computer vision, large language model inference and vision language AI, without having to rely on specifications or benchmark data. This gives a better understanding of system performance and of a project’s suitability, before committing to hardware investments or architecture decisions.

What the key players say

“Developers need a hands-on way to understand how advanced AI technologies will behave in real applications,” said Justin Mortimer, senior director of product marketing, Secure Connected Edge, NXP Semiconductors. “By providing access to NXP’s Ara240 DNPUs through Digital Test Drive, Arrow lets designers evaluate real edge AI performance more quickly and make design decisions more knowingly.”

“At Arrow we are committed to helping customers accelerate their AI journey by removing the barriers between innovation and implementation,” said Shelby Schnurrenberger, vice president of global semiconductor management and suppliers at Arrow Electronics. “By integrating NXP’s Ara240 AI accelerator into our Digital Test Drive platform, designers can immediately evaluate advanced edge AI workloads, gaining the necessary hands-on experience with the technology before investing in hardware. Together with NXP, we are enabling them to move faster from initial idea to production and to unlock new opportunities in intelligent edge applications. Digital Test Drive extends global access to NXP’s AI portfolio, making it possible to evaluate performance, explore use cases and shorten development cycles anywhere in the world. This collaboration strengthens our commitment to making edge AI more accessible, scalable and practical for customers working in industrial, robotics, computer vision and emerging intelligent systems sectors.”

Availability and access

The NXP Ara240 evaluation environment is now available through Digital Test Drive, which simplifies early-stage testing by removing common obstacles such as kit availability, shipping delays, setup complexity and software installation.

For more information, visit Digital Test Drive – Remote Hardware Testing.

The post Arrow Electronics Offers Digital Test Drive for NXP’s Ara240 AI Accelerator appeared first on Open Electronics.

Power semiconductor start-up Vertical Semiconductor of Cambridge, MA, USA — which was spun out of Massachusetts Institute of Technology (MIT) in 2024 — has signed a letter of intent with the US Department of Commerce’s CHIPS Research and Development Office (CRDO) to receive up to $73m in federal incentives under the CHIPS and Science Act...

STMicroelectronics of Geneva, Switzerland has extended its MasterGaN integrated transistor/driver family with new MasterGaN7 devices that contain 650V enhancement-mode gallium nitride (GaN) transistors with 6A maximum current rating. The new series helps designers to boost efficiency and power density in applications such as power adapters, chargers, industrial lighting equipment, and solar micro-inverters...

Semiconductor transistor materials and technology licensing company Atomera Inc of Los Gatos, CA, USA has entered into a joint development agreement (JDA) with epiwafer and substrate maker IQE plc of Cardiff, Wales, UK to bring high-performance, cost-effective RF gallium nitride GaN to market...

Farnell is widening access to NI Nigel AI from Emerson, an AI technology designed specifically for test and measurement. The new enhancements, now available through Farnell, evolve the technology from a simple AI assistant into a true AI author, thanks to new code generation and automated test development capabilities across the entire NI software portfolio. These improvements help make Emerson’s AI-driven test capabilities accessible to even more engineers worldwide, accelerating development without compromising the rigour, control and validation that modern test and measurement environments demand.

The challenge of modern test environments

As increasingly software-defined and connected engineering systems become more widespread, test engineers face growing pressure to manage ever more complex development and validation flows without sacrificing quality or extending development times. Unlike general-purpose AI tools, NI Nigel AI was developed by Emerson specifically for engineering test environments and offers contextual assistance able to understand code and engineering workflows, existing test systems and application data.

AI-assisted authoring features

Integrated directly into NI software, Nigel AI helps engineers speed up tasks such as code generation, debugging, sequence development and system comprehension. With the latest updates introduced by Emerson, Nigel AI moves beyond simple operational support to offer AI-assisted authoring features, including prompt-based code generation in NI LabVIEW and automatic sequence generation in NI TestStand. This lets engineers move more quickly from design to validation while retaining full control over the review, validation and deployment of their work.

A concrete demonstration at NI Connect 2026

During NI Connect 2026, Emerson engineers demonstrated the concrete impact of Nigel AI by reducing a task that, using conventional AI coding tools, had taken around 20 hours of work spread over two weeks, to a workflow completed in about 20 minutes, with working results on the very first attempt.

Statements

Andy Weatherill, Global Head of Product and Technology at Farnell, said: “AI has enormous potential to improve engineering productivity, but its real value emerges when it integrates naturally with the way engineers work every day. By making Emerson’s NI Nigel AI available through Farnell, we help customers adopt an artificial intelligence designed specifically for the sector, capable of enhancing engineering expertise, accelerating development and supporting the high standards of quality and validation required by modern test environments.”

Siddarth Sethi, Vice President and General Manager, NI Test and Analytics Software, Test & Measurement, Emerson, added: “The future of AI in engineering is not about replacing engineers, but about improving the way they work. Nigel brings artificial intelligence directly into the systems, workflows and data engineers rely on every day, combining speed and rigour and helping teams confidently tackle increasingly complex challenges.”

An AI-ready NI platform

Built on the AI-ready NI platform, Nigel AI connects hardware, software, data and engineering workflows to provide intelligent assistance across the entire test lifecycle. Rather than operating as a standalone AI tool, Nigel AI integrates into existing engineering environments, supporting development, validation, troubleshooting and deployment, while helping to reduce repetitive tasks and simplify the most complex workflows.

Global access through Farnell

Through Farnell’s global distribution network, engineers can more easily access the latest NI innovations and products, enabling organisations of any size to adopt AI-based test technologies within the workflows and tools they already use and trust. (https://uk.farnell.com/sc/ni-emerson/software-campaign?ICID=I-SF-NI-QUARTERLY-SOFTWARE-FEB26-WF4836310)

The post Farnell Widens Access to Emerson’s NI Nigel AI for Test appeared first on Open Electronics.

Проєкт «Енергоефективність: від ідеї до дії» KPI4U-2 пт, 10/23/2026 - 15:56

👍 Запрошуємо на вебінар: "Більше, ніж пошук: інформаційна грамотність у добу ШІ" kpi пт, 10/23/2026 - 15:43

📰 Газета "Київський політехнік" № 35-36 за 2026 (.pdf) Інформація КП пт, 10/23/2026 - 15:35

Cadence has announced that Nokia is using its AuraStack AI Super Agent on the Cadence Millennium M2000 AI Supercomputer to improve the design of complex electronic systems. The platform combines agentic AI, Cadence engineering software and high-performance computing in a secure system installed on-site. According to Cadence, this technology helps Nokia complete key design and analysis tasks up to five times faster while keeping sensitive engineering data and intellectual property under its own control.

The deployment introduces a new approach to AI-powered engineering for large technology companies. Nokia can use Cadence’s AuraStack AI Super Agent to automate engineering tasks, evaluate different system designs and analyse how multiple physical factors, such as heat, electrical performance and mechanical effects, interact in complex electronic systems. The modular Millennium architecture also allows Nokia to expand computing capacity as its needs increase and run multiple AI-driven engineering tasks simultaneously.

“Nokia’s deployment demonstrates how agentic AI can transform the way the world’s most advanced systems are engineered,” said Michael Jackson, corporate vice president of R&D for System Design and Analysis at Cadence. “Cadence uniquely brings together AI, computational software and accelerated computing in one platform, enabling Nokia to automate complex workflows, accelerate design convergence and protect critical IP.”

The Cadence platform integrates three layers of AI-driven engineering:

1. Agentic AI: The Cadence AuraStack AI Super Agent, built with NVIDIA Nemotronopen models, automates and coordinates electronic system design tasks, including schematic capture, placement, routing and multi-objective optimization.
2. Computational software: Cadence EDA and SDA technologies use GPU-optimized NVIDIA CUDA-X libraries, including cuDSS, and NVIDIA Omniverse to accelerate power integrity, thermal analysis, electromagnetic characterization, and circuit simulation workflows.
3. Accelerated computing: Cadence Millennium M2000 systems provide a shared, scalable foundation for the AuraStack AI Super Agent and Cadence engineering applications, powered by NVIDIA Blackwell GPUs.

Together, these technologies help Nokia compress design and analysis cycles from months to days and accelerate targeted workflows by 5X, increasing engineering throughput and enabling teams to evaluate more design options. The self-contained deployment keeps sensitive data and IP within Nokia’s environment, while the modular architecture provides a path to scale capacity with demand.

“Cadence is helping us rethink how we approach electronic system design and heterogeneous integration,” said Derek Urbaniak, Head of SoC Development at Nokia. “The integrated platform enables our teams to evaluate complex tradeoffs and close multiphysics loops in a fraction of the time, while keeping sensitive engineering data and IP under our control. This helps us move advanced systems toward production faster.”

“AI gives engineering teams the speed to explore more designs and the insight to make better tradeoffs, while keeping proprietary data and expertise under their control,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “Nokia is bringing that capability inside its own environment, with Cadence agents built with NVIDIA Nemotron open models and simulation powered by NVIDIA accelerated computing and NVIDIA CUDA-X libraries like cuDSS. Together, they connect design automation with the power, thermal and electromagnetic analysis needed to evaluate each iteration, while keeping proprietary data and IP under Nokia’s control.”

The post Cadence AI Super Agents Help Nokia Accelerate Electronic System Design by 5X appeared first on ELE Times.

There are several ways to boost a relatively low voltage by a factor of two or three, or even hundreds—but there are also many risks.

Part 1 of this high-voltage article series discussed configurations that boost the voltage, but they can only go so far. Instead, what are called voltage “multipliers” can push voltages to much-higher values. The design and considerations change dramatically when the output voltage starts going about 100 V and then into the >1000 V range.

Fortunately, many of these higher-voltage systems need only modest current (100 mA or less) which simplifies the problem somewhat. Applications for these truly high voltages include X-ray machines, physics experiments, beam steering, electric-field shaping, lightning simulation, electrostatic tests, and many more – it’s more common than many people realize.

The obvious question is why can’t you just use a transformer with suitable turns ratio to multiple the input voltage by 10× or 1000×, or whatever is needed? In theory, you could. But as the turns ratio and secondary voltage increases, there are issues of additional inefficiency due to transformer losses (perhaps acceptable), and high-voltage breakdown of the wire insulation, among others. This does not mean it can’t be done, but it can be a difficult approach due to practical considerations.

The transformer approach been used commercially in many applications with a configuration called a flyback converter (Reference 1), such as for the cathode-ray tube (CRT) of the mass-market TVs that dominated the video world for over 50 years until the LED/LCD flat panels took over) The flyback design uses a special transformer which is a wound as a combination of a transformer, plus an inductor to store energy.

However, there are various topologies which use passive components to multiply an input voltage. In general, they rely on a combination of diodes and capacitors in a series of stages to leverage the voltage of the previous stage. These voltage multipliers are specialized rectifier circuits which develop an output voltage which is (in theory) an integer times the AC peak input, for example, ×2, ×3, or ×4 times the AC peak input value. They are usually configured as a stack of half or full-wave rectifiers.

For example, the circuit of Figure 1 shows a three-stage multiplier. The action is as follows: each cycle of the AC input charges the various capacitors, and each capacitor is eventually charged by successive AC cycles to the peak value of the input sine wave. Thus, for a 120 VAC rms line voltage, the peak is 170 V. By doing this across the multiple capacitors – each charging independently – and then allowing these capacitors to be stacked in series, the output voltage is the sum of the individual capacitor voltages.

Figure 1 In the basic voltage-multiplier configuration, a ladder of capacitor-diodes arranged as rectifiers is used with the capacitor charging from the AC line and then adding their voltages in series at the output. (Image source: Spellman High Voltage Electronics Corporation)

There is no tangible or visible switching in this arrangement, yet it goes from charging to stacking of stored voltage in a standard rectifier arrangement. The diodes act as passive switches and prevent discharge of the capacitors when the input voltage starts to drop. They allow current flow to the capacitors, but not flow from the capacitors back to the input source. And if you need a negative high voltage from a multiplier, that’s only easy thing about high-voltage multipliers: simply reverse the polarities of all the multiplier diodes and capacitors to produce a negative version.

These multiplier configurations seem easy enough to implement but they are not. First, the diodes and capacitors must have a minimum reverse-breakdown voltage rating of at least twice the peak voltage across them. Even if you are starting with a 120 VAC line (170 V peak), you’d need components rated for at least 1700 V if you are building a 10× multiple (plus a factor of ×2 or more for safety margin, of course).

There are also other issues in a high-voltage designs such as these. First, they can deliver very little current at that high voltage: once you begin to draw current for the for the load, the stored charge and thus the voltage drops. In many cases, this is too restrictive, since many higher-voltage applications need only small amount of current, for example, to create an electric field.

Also, the physics of the high-voltage world has many points at which “strange” things happen; increasing the voltage is not a linear situation. Think of it as analogous to water crossing the temperatures where freezing or boiling occurs. For example, high voltage circuits will have corona discharges from sharp points. These are both hazardous in themselves, and they also cause pitting of metal contacts and can punch through insulators.

Even “small things” such solder joints are an issue for corona. You might assume a standard, well-made solder joint would be OK, but it will act as a starting point for corona discharge. Therefore, “ball soldering” is frequently used instead in the high-voltage implementation using large, spherical solder connections (Figure 2). The large-diameter, smooth spherical shape of the solder connection helps to control the local E-field, thus suppressing the generation of corona.

Figure 2 In the high-voltage word, every aspect of the physical construction is critical: a standard solder joint will encourage corona discharge, so the joints must be made as balls without corners. (Image source: Maker Forums)

If you are tempted to build your own high-voltage circuit, think about it very carefully. It’s probably not a good idea, because there is so much more to it than just the schematic diagram and the bill of materials (BOM), and that BOM itself has truly unique parts.

It is a truly dangerous world where just “being careful” is not enough, and even the slightest mistake or oversight has deadly consequences. Physical spacing, component ratings, insulation properties, pinhole imperfections, and material breakdown are just some of the many issues. Circuit protection is also unique: specialty fuses and other overvoltage/overcurrent devices are essential.

Not surprisingly, that doesn’t stop people from doing so, as seen in the posting which shows someone building a multiplier going into the tens of kilovolts (Reference 5).  Don’t try this at home (or even in the lab) unless you have someone with you is experienced in high-voltage construction (and can call for help – although it usually too late at that point!). Finally, there are the complicated regulatory issues if you intend to produce and sell the product.

There are many examples of high-voltage multipliers used in attention-getting projects. For example, a team at MIT recently built and tested an aircraft with a wingspan of a few meters but with no moving parts (Figure 3, details are in References 6 and 7). Instead, it uses the flow of negative ions attracted by high-voltage, positive electrostatic field across the wing rear edge.

Figure 3 High voltages are key to some very unusual applications, such as this MIT aircraft, which is propelled by a stream of ions across the wing; the ions are attracted to the 20-kV “fence” at the trailing edge of the wing. (Image source: MIT)

This non-tethered, free-flying aircraft has an on-board battery (nominally at 200 V DC) for its 600-W power supply (Figure 4, designed by the Power Electronics Research Group in the MIT Research Laboratory of Electronics). It uses a switching regulator followed by a 1:15 step-up transformer, and then a six-stage, full-wave “Cockcroft-Walton” voltage multiplier to deliver 20 kV DC (see References 8 through 10).

Figure 4 Within the MIT aircraft is a DC/DC power subsystem which converts 200 V DC from on-board batteries into an AC waveform, then transforms it to 20 kV DC using the Cockcroft-Walton multiplier topology. (Image source: MIT)

Cockroft-Walton multipliers play a very large role in advanced physics experiments, often used as initial “injectors” for various particle accelerators. The 750-kV unit of Figure 5 was commissioned in 1975 at CERN and was recently retired. Larger units can now reach up to the single-digit megavolt range.

Figure 5 This Cockroft-Walton generator at CERN produces up to 750 kV; it’s definitely not a “desktop” unit, as it is about 12 feet (~3 ½ meters) tall. (Image source: CERN)

The world of high-voltage boosters and multipliers is a strange yet important one. Some of these architectures result in modestly higher voltages, and some reach into kilovolt range. As voltages go beyond about 60 V, issues of safety become a concern. As they go into the hundreds and thousands of volts, additional issues of material characteristics and electrical phenomena show that design and construction for these levels is a very different world.

References:

1. Wikipedia, “Flyback converter”
2. Spellman High Voltage Electronics Corporation, “High-Voltage Reference Manual”
3. All About Circuits, “Voltage Multipliers (Doublers, Triplers, Quadruplers, and More)”
4. Instructables, “High Voltage Multiplier”
5. MIT, “MIT engineers fly first-ever plane with no moving parts”
6. MIT Electric Aircraft Initiative, “MIT engineers fly first-ever plane with no moving parts in propulsion system”
7. Georgia State University Hyperphysics, “Cockroft-Walton Accelerators”
8. Wikipedia, “Cockcroft–Walton generator”
9. Wenzel Associates, TechLib, “Cockcroft-Walton voltage multipliers”
10. CERN, “The 750kV Cockcroft-Walton generator and Faraday cage”

—Bill Schweber is a degreed senior EE who has written three textbooks, hundreds of technical articles, opinion columns, and product features. Prior to becoming an author and editor, he spent his entire hands-on career on the analog side by working on power supplies, sensors and signal conditioning, and wired and wireless communication links. His work experience includes many years at Analog Devices in applications and marketing, and he also developed significant mechanical-engineering insight while designing control electronics for large materials-testing systems.

Related Content

• High-Voltage Design: Living Long and Still Prospering
• Pulsed high-power systems are redefining weapons
• Learning to like high-voltage op-amp ICs
• Rich voltage, poor voltage: My incandescent tale

The post Creating higher voltages, part 2: Voltage multipliers appeared first on EDN.

Міжнародне партнерство в дії: КПІ отримав портативну зарядну станцію KPI4U-2 пт, 10/23/2026 - 13:00

Display the concentration of CO2 and TVOC in the air of an enclosed space, such as a school classroom or an office, and signal through LEDs whether the air quality is good or poor.

Why monitor air quality

In recent years, unfortunately, the quality of the air we breathe has become a problem that cannot be ignored. It has been one for indoor environments for a long time, especially in winter, when we stay shut in with many people in a poorly ventilated room, but also outdoors, especially in cities, whose air is increasingly contaminated by the polluting emissions of motor vehicles, heating systems (let us not forget that natural gas is not pure methane, but contains other gases plus additives for odour and for colouring the flame…), factories and thermal power plants, and so on; and where the scarcity of greenery does not help the oxygenation of the air, which therefore has a low oxygen content and does not favour the natural processes of the human and animal body.

In indoor environments, air quality is compromised by elements such as the carbon dioxide we exhale, but also by fine dust and possible pathogens that we can emit by breathing or coughing; think for example of COVID-19 infection, by now a long-standing problem we live with, for which the air in enclosed spaces is one of the main transmission vehicles. The spread of this virus, like others, in enclosed spaces is essentially a problem linked to air quality: the more “stale” the air, the greater the concentration of droplets (droplets of saliva and organic liquids from the mucous membranes) potentially carrying pathogens, capable of spreading disease. The solution to mitigate contagion is obviously to carry out an air exchange, to reduce the concentration of these pathogens.

But when should you do it, given that, both in summer and in winter, we tend to keep the windows closed to reduce the consumption of air conditioning and heating systems?

Well, in this article we propose a simple device that, by reading a CO2 and TVOC sensor (that is, the concentration of organic particles suspended in the air, also known in Italy by the acronym COV – Volatile Organic Compounds), lets us get an idea of the air quality in a room and, through LEDs, signals when it is time to carry out an air exchange, allowing us to keep air quality under control and minimise energy losses.

The hardware of the project

To build the device we have introduced so far, we decided to use a Heltec WiFi Kit 32 module as the control unit, a Sensirion SGP30 sensor as the element that detects the concentration of carbon dioxide and TVOC, and two common LEDs (one red and one green) as the signalling elements. The conceptual diagram of the system is shown in Fig. 1.

Heltec WiFi Kit 32 module

The Heltec WiFi Kit 32 module, which is ultimately the heart of our system, is a small electronic board that integrates an ESP32 microcontroller and a 0.96-inch OLED display; you can see it in Fig. 2. The hardware features of the WiFi Kit 32 module, with which we will work in this project, are:

• 0.96″ blue OLED display;
• 32-bit Tensilica LX6 dual-core microcontroller;
• Clock frequency up to 240 MHz;
• 520 kB internal SRAM;
• 32 MB flash memory;
• Integrated 802.11 b/g/n Wi-Fi transceiver;
• Integrated dual-mode Bluetooth (classic and BLE);
• Power supply: 3.3V to 7V;
• 28 GPIOs (some used to drive the OLED display);
• Integrated lithium-polymer battery charger;
• Operating temperature: -40°C to +90°C.

This module is perfect for our purpose, since it already integrates an OLED display that we will use to show the air quality readings and user messages, as well as several GPIO channels that we need to control our signalling devices; in this first version we plan to use only 2 LEDs, but the number of available GPIOs (28 in total) would easily let us add other elements, such as a buzzer for acoustic alerts.

Besides these features, the module has 520 kbytes of on-board RAM and a full 32 Mbytes of flash, characteristics that let us rest easy in terms of memory resources for our control software. In addition, the module carries a standard 802.11 Wi-Fi transceiver, which we can use for future evolutions of this device (for example to publish the data on CO2 and TVOC concentrations directly on a web page, or to send any alarms via Telegram), as well as a Bluetooth radio, which we can use to interface with external devices such as smartphones and PCs, which we could eventually use to configure the device itself.

Finally, the board also integrates an on-board LiPo charger, which would let us connect a single-cell battery, very useful for guaranteeing power to the device even when mains voltage is absent. Fig. 3 shows the pinout of our module.

The SGP30 sensor

To obtain the concentration of carbon dioxide and TVOC in the air, we decided to use a breakout board based on the Sensirion SGP30 sensor. This tiny sensor lets you know the concentration of carbon dioxide (so as to tell us how stale the air in an enclosed space is) and the TVOC (Total Volatile Organic Compounds), that is, the concentration in parts per billion of suspended particles. An image of the breakout board is shown in Fig. 4.

The detailed hardware features of the breakout board are these:

• sensor: Sensirion SGP30 (TVOC and CO2);
• TVOC (Total Volatile Organic Compounds) detection: 0 to 60,000 ppb (parts per billion);
• CO2 detection: 400 to 60,000 ppm (parts per million);
• Sampling frequency: 1 Hz;
• Interface: I2C (address 0x58)
• Power supply: 3.3V or 5V.

Wiring the hardware

Let us now see how to build our apparatus in practice, starting from the physical construction and therefore from the wiring of the components. The connection between the sensor board and the LEDs is quite simple, so much so that it can be done with male-female jumper wires like those used for Arduino, or by resting on a breadboard, as you can see in these pages.

The SGP30 sensor is a breakout board interfaced via an I²C bus, so it needs a total of four connections, because in addition to the clock (SCL) and data (SDA) lines of the I²C bus, we also have to connect the two power lines (GND, that is the negative, plus the 3.3V positive).

The two LEDs, on the other hand, are interfaced through two standard GPIOs of the WiFi Kit 32 module; these LEDs cannot be driven directly from the board’s lines, to avoid overloading the GPIOs, but rather the appropriate current-limiting resistors must be placed in series with them, which we calculated at 220 ohms each.

To make the connections easier for you, on the next page we propose the wiring diagram to use if you want to interconnect the various elements with soldered wires or male-female jumpers. In addition, Table 1 lists the connections between the WiFi Kit 32 module and the SGP30 breakout board. Table 2, instead, lists the pins of the WiFi Kit 32 module used for the two signalling LEDs.

Software implementation

After looking at the hardware details of our system, let us move on to the software. For the development of the control software we decided to use MicroPython, and specifically the port for the ESP32. MicroPython is a powerful system for software development of applications for modern, IoT-oriented microcontrollers such as the ESP32, and moreover the large number of existing libraries for managing sensors, actuators and interfaces greatly simplifies this phase of development.

Installing MicroPython

To use MicroPython on the WiFi Kit 32, the first thing to do is install the interpreter, since the WiFi Kit 32 does not include it by default. To do this you first need to get a version of the binary to flash from the following web address: https://micropython.org/download/esp32/. The version used for the development of this application is v1.19 (2022-06-18). Download the binary and save it in a folder on your PC.

At this point you also need esptool, a tool used to flash the ESP32’s flash memory and distributed free of charge by Espressif (the company that makes the ESP32 and the ESP8266). You can download esptool from the following web address: https://github.com/espressif/esptool. At this point you have to erase the device memory and then “flash” (load into the Flash Memory) the MicroPython binary.

Erasing the memory

First, connect the WiFi Kit 32 to your PC with a micro USB cable. A virtual COM port should be assigned to the device (for example COM3, but the exact number depends on how many ports you already have on your system).

Now put the board into programming mode by pressing and holding the PRG button and pressing and releasing the RST button. Now all you have to do is use the esptool.py script to erase the memory. You can add the script’s path to your system path, or work directly from that folder and erase the memory with the following command (valid in a Windows environment):

python esptool.py –port <port_numb> erase_flash Replace <port_numb> with the virtual COM port assigned to your device (for example COM3).

Flashing the MicroPython interpreter

At this point you can flash MicroPython (on the ESP32 the interpreter must be flashed at address 0x1000). You can use the following console command, again under Windows:

python esptool.py –chip esp32 –port <port_numb> –baud 460800 write_flash -z 0x1000 esp32.bin As before, replace <port_numb> with the virtual COM port assigned to your device and esp32.bin with the name (and possibly the path, if it is not the same one you are working from) of the binary downloaded earlier. Fig. 5 shows the result of this operation.

The control software

At this point we can move on to managing the control software for our board. As we mentioned, what we want to do is acquire the data relating to the CO2 and TVOC values provided by the SGP30 sensor, display this data on the OLED display; read the user thresholds for the CO2 limit values (with their hysteresis) from the file system. Manage two indicator LEDs (red and green) to show whether the CO2 level is above the maximum permitted threshold.

To implement this software we first of all need suitable drivers for managing the OLED display and the SGP30 sensor. Fortunately both drivers are available, developed by Adafruit. You can get them by downloading them from the following web links: for the OLED display driver: https://github.com/adafruit/micropython-adafruit-ssd1306/blob/master/ssd1306.py; for the SGP30 driver: https://github.com/alexmrqt/micropython-sgp30.

The architectural diagram of the software we intend to build is shown in Fig. 6. The complete project therefore consists of these three files: Air_quality.py (main application file); Ssd1306.py (OLED display driver); Adafruit_sgp30.py (sgp30 sensor driver).

The Thonny IDE

As the development IDE for this application we will use Thonny, one of the most widely used IDEs for Python, which lets you develop both standard Python applications and MicroPython ones. Thonny can be downloaded freely from the following link: https://thonny.org/.

Interfacing with MicroPython is very simple: after connecting the WiFi Kit 32 board to the PC’s USB port, open Thonny and click on the bottom right corner: in the drop-down menu that opens after clicking, among the various available interpreters select “ESP32”, as shown in Fig. 7.

If the interpreter is not present, select “Configure Interpreter…” and manually specify the interpreter and the COM port to use, as shown in Fig. 8.

System Setup and Main Loop

Let’s now look at the details of the application software, contained entirely within the air_quality.py file, which is divided into two distinct phases: System Setup and Main Loop. The first phase, as you can easily guess from the name, is the system initialisation phase, while the second is the main loop in which the measurement takes place, along with printing the values on the OLED display and indicating the status via the LEDs.

The first phase, whose code is shown in Listing 1, has the task of initialising all the system peripherals, such as the LEDs, the two I²C buses (one used for interfacing the OLED display, the other for managing the SGP30 sensor), and retrieving from the file system the SGP30 sensor baseline values and the user-set CO2 threshold. These files are text files that the software expects to find in the file system. The sensor baseline values are calibration values that improve reading accuracy and are automatically created by the software if they are not present. The files containing the alarm threshold for the CO2 value, on the other hand, must be created by the user, and if they are not present the default values are loaded. To set this threshold you need to create the following two files: co2eq_th.txt = contains the alarm value for the CO2 reading, in ppm (parts per million); hist_th.txt = contains the hysteresis of the previous threshold, again in ppm.

Listing 1 – System initialisation

# ————————————————- # – SYSTEM INIT # ————————————————- # LEDs led_green = Pin(2, Pin.OUT) led_red = Pin(17, Pin.OUT) led_green.on() led_red.off() # Initialize I2C buses s_i2c = I2C(scl=Pin(18), sda=Pin(19), freq=100000) d_i2c = SoftI2C(scl=Pin(15), sda=Pin(4), freq=100000) # OLED enable pin oled_sts = Pin(16, Pin.OUT) oled_sts.on() # OLED initialization oled_width = 128 oled_height = 64 oled = ssd1306.SSD1306_I2C(oled_width, oled_height, d_i2c) # Air quality sensor init sgp30 = adafruit_sgp30.Adafruit_SGP30(s_i2c) # Initialize SGP-30 internal drift compensation algorithm. sgp30.iaq_init() # Wait 15 seconds for the SGP30 to properly initialize user_message(‘Sensor Init…’) time.sleep(SENS_INIT_DELAY_S) # User threshold for LEDs indication get_user_threshold() # Sensor Calibration get_sensor_baseline() # Store the time at which last baseline has been saved baseline_time = time.time() Creating these files is very simple: in the Thonny environment, with the board connected via USB, select the file-shaped icon or File > New. Repeat this operation twice, to create two files, and write the values inside them (for example 450 in the co2eq_th.txt file and 10 in the hist_th.txt file, so as to have a CO2 threshold of 450ppm and a hysteresis of 10ppm).

Now click on the floppy disk-shaped icon (or select File > Save) and in the pop-up that appears, select “MicroPython Device”, as shown in Fig. 9.

This operation lets you access the device’s file system and save the files you created earlier. In this way you can set the CO2 threshold value for your case. Once the initialisation phase is complete, the software moves on to the main loop, shown in Listing 2.

Listing 2 – Main Loop

# ———————————————— # – MAIN LOOP # ———————————————— while True: # Get CO2 and TVOC data co2eq, tvoc = sgp30.iaq_measure() # Print values on Display oled_data(co2eq, tvoc) # Manage LEDs if (co2eq > co2eq_th): led_green.off() led_red.on() elif (co2eq < (co2eq_th – hysteresis)): led_green.on() led_red.off() # Update sensor baseline update_sensor_baseline() # Wait for the new sampling time time.sleep(SAMPLING_TIME_S) Here the following operations are performed in sequence. Through the iaq_measure() function of the adafruit_sgp30 library (sgp30_iaq_measure()), the CO2 and TVOC values are acquired and saved respectively in the two variables co2eq and tvoc.

At this point, on the next line, the values are printed on the OLED display through the oled_data() function. Then, through a series of if…elif statements, the indicator LEDs are handled. In particular, if the value stored in the co2eq variable is higher than the co2eq_th threshold set by the user, the red LED turns on and the green LED turns off; otherwise, if the value is lower than the threshold minus the hysteresis (co2eq_th – hysteresis), then the red LED turns off and the green one turns on. Once this phase is complete, the update_sensor_baseline() function is called to update the sensor baseline.

To ensure optimal sensor operation, the software updates the baseline for the first time after 12 hours of operation and then once every hour, as stated in the SGP30 manufacturer’s datasheet. At this point the loop pauses for 60 seconds before running a new data acquisition cycle.

The complete software project for this application, together with a description and all the required MicroPython libraries, can be downloaded from the following GitHub repository: https://github.com/ffich/IndoorAirQuality.

To test the software you will need to save the two files containing the drivers into the memory of the WiFi Kit 32 board, using the same procedure described for saving the user files, taking care to keep the same file names and not to forget the .py extension. The main file can be run directly from Thonny, or saved to the device memory under the name main.py, which is necessary if you want the device to work in standalone mode, that is, not connected to the PC (in fact main.py is the file the MicroPython interpreter automatically looks for at boot).

Fig. 10 shows a photo of the device wired on a breadboard while it acquires air quality data and drives the LEDs.

Configuration

The software also offers a few configuration options that the user can change, all of them found in the air_quality.py file. In particular, you can:

• change the default values of the CO2 alarm threshold and its hysteresis by editing the two parameters co2eq_th and hysteresis, set by default to 450 and 10, as shown in Listing 3 (by acting on the default values there is no need to save these two parameters to the file system);
• change the initial wait time for sensor stabilisation and the sampling time (SENS_INIT_DELAY_S and SAMPLING_TIME_S), set by default to 15 and 60 seconds, as also shown in Listing 3 (we recommend leaving these settings unchanged).

Listing 3 – Configuration parameters

——————————- # – USER TH DEFAULT VALUES ——————————- co2eq_th = 450 hysteresis = 10 ——————————- # – TIMING CONST # ——————————- SAMPLING_TIME_S = 60 SENS_INIT_DELAY_S = 15

Conclusions

In this article we have described in detail how to build a device for air quality monitoring. By reading an air quality sensor (SGP30), the device can show the CO2 and TVOC concentrations on a display. Moreover, by reading a user file that contains the tolerance threshold for the CO2 concentration, it can give a visual indication through an LED that the room needs to be aired out.

Given the wealth of additional peripherals on the platform used, this device can be expanded with IoT features, such as a web page that lets you check the data remotely and alerts by e-mail or Telegram notification.

Related products

• CCS811 Air Quality Module

The post Indoor Air Quality Meter: Keeping CO2 and TVOC Under Control appeared first on Open Electronics.

Представники КПІ взяли участь у фіналі PolyTECH Summit 2026 kpi пт, 10/23/2026 - 12:55

Renesas Electronics Corporation has announced a pair of 32-bit MCUs that achieve up to 600 MHz processing speeds, the first MCUs in the family to utilize a 22nm process, and also designed with embedded magnetoresistive random access memory (MRAM). The first in the RX Family for Renesas using a 22nm process technology, these microcontrollers feature embedded magnetoresistive random-access memory (MRAM). Both devices outperform the previous models, RX72M and RX72N (240 MHz), by more than two times. The new solutions are the RX74M for industrial and factory automation, including AC drives, industrial robots and programmable logic controllers (PLCs), and the RX74N for wider industrial applications, including smart meters, power conditioners and heating, ventilation and air conditioning (HVAC) systems.

High-Speed Real-Time Processing Enabled by 22nm Process and MRAM

To improve system control and responsiveness as well as data processing capabilities, industrial devices require MCUs that can operate faster, integrate processing and memory capabilities into a single chip and communicate efficiently. The RX74M and RX74N MCUs, are based on a 22nm process and pack a 600 MHz RXv3e processor core, 1 MB of MRAM, 256 KB of zero-wait-state tightly coupled memory (TCM) and 1.5 MB of SRAM. These features enable the MCUs to handle demanding, time-critical operations while providing consistent performance, even when encountering frequent interrupts and branching operations.

Writing data to MRAM does not require erasing it beforehand, unlike traditional embedded flash memory. This allows the speed of code and data updates to be increased, with a marked reduction in programming time and system downtime. It has applications in industrial systems where settings and operating parameters are regularly revised, as well as log data.

Support for Major Industrial Networks Including EtherCAT, Ethernet TSN and CAN FD

There is increasing demand for seamless connectivity across multiple industrial Ethernet standards, yet supporting diverse protocols has conventionally required multiple devices. The RX74M integrates an EtherCAT slave controller, a TSN-capable Gigabit Ethernet switch and CAN FD onto a single chip. These features allow the device to support major industrial networks such as EtherCAT, PROFINET and EtherNet/IP on a single chip, including deterministic communication via TSN, as well as redundant network configurations such as EtherNet/IP DLR (Device Level Ring), which were not supported in previous generations. As a result, the device provides an IIoT-ready industrial network foundation that connects field devices to higher-level systems.

Hardware-Based Security Features Anticipating CRA and PQC

With the proliferation of IoT devices, regulations such as the Cyber Resilience Act (CRA) are driving demand worldwide for security measures that span the entire device lifecycle.

The RX74M and RX74N enhance hardware-based security features by incorporating ProtectZone into the RX CPU, enabling secure and non-secure regions to be separated within a single chip. Furthermore, the next-generation Renesas Secure IP (RSIP) extends public-key cryptography beyond the conventional TSIP and supports authenticated encryption as well as a broader range of hash algorithms. This enables advanced, forward-looking security implementations, including post-quantum cryptography (PQC), within a single MCU.

“Industrial equipment development requires three key elements: advanced real-time performance, diverse industrial networking and robust security,” said Yoshi Ishikawa, Senior Director of the EP Advanced MCU Business Division at Renesas. “By integrating these requirements into a single chip, the new products simplify the design of increasingly complex industrial equipment and support the evolution of industrial automation.”

Improved Development Efficiency Through a Comprehensive Partner Ecosystem

Renesas collaborates with ecosystem partners to provide solutions for the RX74M and RX74N, helping customers shorten development time.

• Software: Codewerk, RT-Labs
• Development Tools: IAR
• Real-Time OS: eForce, Zephyr RTOS

The post Renesas Launches RX74M and RX74N 32-bit MCUs with 22nm Process and Embedded MRAM appeared first on ELE Times.

Microchip Technology has expanded its L-Series timing product portfolio with the launch of the LX-820 Radiation-Tolerant Quartz Oscillator for Low Earth Orbit (LEO) satellites and other space applications. The L-Series offers satellite developers different types of oscillators with varying levels of frequency stability, phase noise, power consumption and size, allowing them to choose the right solution for their specific mission requirements.

The L-Series sits between standard commercial oscillators and traditional high-reliability space products. It combines commercial- or military-grade components with additional inspections, electrical and thermal testing, and specialised manufacturing processes to meet the needs of space applications. The oscillators have undergone radiation testing as complete, working devices to check their ability to withstand Total Ionizing Dose (TID) and Single-Event Latchup (SEL). They offer radiation tolerance of up to 50 krad and have been tested for SEL at 40 MeV·cm²/mg. Most L-Series products also use specially processed quartz crystals to reduce changes in frequency caused by radiation. The L-Series oven controlled crystal oscillators (OCXOs) feature a rugged design and four-point crystal mounts to withstand launch conditions and the demands of their intended missions.

Designed to address two persistent challenges for satellite developers, the L-Series helps ease supply chain constraints while reducing budget pressure. Many timing solutions qualified for space environments have extended lead times and significant procurement costs. The L-Series is designed to provide a more accessible alternative, with many configurations available from stock or with lead times of less than 20 weeks. Additionally, the L-Series oscillators may lower overall program costs by reducing the need for the component pedigree, documentation requirements and inspection overhead that are typically associated with traditional high-reliability space-grade timing products.

“Satellite developers should not have to choose between dependable precision timing and the cost and schedule requirements of their mission,” said Randy Brudzinski, corporate vice president of Microchip’s frequency and time systems business unit. “Our L-Series oscillators bring together the performance, radiation tolerance and supply availability our customers need to confidently deploy their New Space applications using a precision timing solution that helps reduce both schedule and mission risk.”

The new LX-820 is available in a compact 3.2 mm × 2.5 mm surface-mount package and is designed to operate across a wide temperature range from -55°C to 125°C. The rugged device is engineered to meet MIL-PRF-55310 Level B and provides ±100 ppm performance. Additionally, the LX-820 supports CMOS output frequencies from 1.25 MHz to 135 MHz and low-voltage differential signalling (LVDS) output frequencies from 100 MHz to 250 MHz.

Modern satellite systems rely on highly accurate timing references to support communications, navigation, Earth observation, mapping and emerging lunar missions. As these applications can have different timing requirements, Microchip’s L-Series encompasses crystal oscillators (XOs), temperature-compensated crystal oscillators (TCXOs), oven controlled crystal oscillators (OCXOs) and a pulse-per-second (PPS) disciplined oscillator. These products allow developers to use compact XOs where size and broad frequency coverage are priorities, TCXOs where tighter temperature stability and lower power are needed, OCXOs for more demanding stability and phase-noise requirements, and a disciplined oscillator where PPS synchronization and holdovers are required.

Microchip offers an extensive portfolio of clock and timing systems, ranging from miniature component oscillators, to small plug-in timing server cards, to atomic clocks and multi-rack time scale systems. As a primary contributor to the world’s time, Microchip’s timing solutions are trusted, reliable and resilient. For more information, visit Microchip’s clock and timing solutions webpage.

The post Microchip Launches LX-820 Radiation-Tolerant Quartz Oscillator for LEO Satellites appeared first on ELE Times.