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An E-ink e-reader can become a printer. Nishant Joshi has modified the CrossPoint firmware to run the IPP protocol on a reader based on the ESP32-C3. This way the device announces itself to the operating system as a printing peripheral and receives documents without any dedicated software. You just select the printer from your computer and hit print.

The reader presents itself as a monochrome, single-sided printer at 300 DPI on A5 paper. It accepts Apple raster and PWG raster formats, so system drivers need no special translation. The fork also works on the Xteink X4 and Xteink X3, two models built around the ESP32-C3 microcontroller. The project page on Hackaday.io collects Nishant Joshi’s work.

Pixel by pixel, row by row, to avoid saturating RAM

The raster images of an A5 page at 300 DPI are too large for the microcontroller’s memory. So the firmware does not hold them all in RAM. It receives the pixels one row at a time and writes them immediately, both directly to the screen and to the SD card. As a result, the document remains available even after printing.

The fork of the CrossPoint firmware implements the IPP protocol on the ESP32-C3.

The reader announces itself as a monochrome, single-sided printer at 300 DPI on A5 paper.

It accepts Apple raster and PWG raster formats.

The pixels are received and written row by row, because the whole image does not fit in RAM.

Each row ends up both on the screen and on the SD card.

A folder on the SD card acts as the printer’s output tray.

Anyone who wants to rebuild the project starts from an ESP32-C3 module, the same microcontroller that powers the reader. For those looking for a compact board with integrated wireless connectivity there is the SuperMini form factor development module, suitable for IoT and embedded projects. Alternatively, for those who prefer to start from a more complete kit, the catalogue offers a kit based on the ESP32-C6-Zero with Wi-Fi 6 and integrated peripherals.

The folder on the SD card as an output tray

Every printed document ends up in a folder on the SD card. That folder works as an output tray: anyone who wants to re-read a page finds it there, without having to print it again. Moreover, writing to the SD card happens in parallel with writing to the screen, so the reader is not left blocked waiting for the save to finish.

The flow is linear. The operating system sees the printer, sends the job in raster format, the firmware breaks it down into rows and deposits them. Finally the document appears on the E-ink screen and stays archived on the card. No application is needed on the computer, because the IPP protocol is the standard one for network printers.

For those who want to follow the work closely, Nishant Joshi’s project page on Hackaday.io is the starting point. There you will find the fork of the CrossPoint firmware with the IPP implementation and the references to the two supported hardware versions.

Source: https://hackaday.io/contests

Related products

• Mini ESP32-C3 module
• ESP32-C6-Zero Wi-Fi 6 IoT Development Kit

The post Printing from an E-ink e-reader: the IPP firmware on ESP32-C3 appeared first on Open Electronics.

Global automation leader Emerson today announced new radio frequency (RF) test capabilities that help engineers characterize increasingly complex devices and scale automated measurement workflows. The new NI PXIe-5633 four-port vector network analyzer (VNA), together with synchronization-focused software enhancements for the NI PXIe-5860 vector signal transceiver (VST), expands RF validation capability to new multiple-input multiple-output (MIMO) and multiport S-parameter applications within the scalable, software-driven NI PXI platform.

New capabilities will enable engineers to:

• Expand RF validation capacity with four-port vector network analysis capabilities that address more complex device architectures and emerging multiport measurement requirements.
• Address next-generation wireless test challenges through enhanced NI PXIe-5860 synchronization capabilities that support advanced MIMO, multichannel and timing-sensitive RF applications.
• Consolidate measurements within a single system by combining network analysis and modulated RF measurements in one PXI architecture, reducing system complexity and simplifying test workflows.
• Build adaptable test systems for growth with a scalable, software-connected platform that extends automated measurement workflows from design validation through production.

“RF test is no longer only about validating individual components in isolation. As wireless systems become more distributed, software-defined and antenna-intensive, engineers need test architectures that evolve as quickly as the technologies they are validating,” said Kevin Schultz, NI chief technology officer with Emerson’s test and measurement business. “These capabilities reflect our commitment to helping engineers build adaptable test infrastructure that supports system-level validation and future wireless innovation.”

The NI PXIe-5633 extends NI’s existing vector network analysis capabilities to four ports, helping engineers characterize more complex RF devices. As wireless systems increasingly rely on multiple RF paths, synchronized radios and distributed architectures, engineers must characterize interactions across entire systems rather than individual components in isolation. NI PXIe-5860 software enhancements support synchronization-focused and MIMO workflows for increasingly complex wireless test requirements.

Engineers can also perform S-parameter characterization alongside modulated RF measurements through a single RF connection. Combining these measurements in one software-driven PXI system can reduce the need for separate instruments and test setups while maintaining a consistent path for automation.

The new capabilities are being featured in booth A40B at European Microwave Week in a 4×4 MIMO demonstration using the NI PXIe-5860 and the four-port VNA. Applications span semiconductor validation, advanced wireless research, aerospace and defense systems and other RF-intensive technologies increasingly built around multi-antenna, multichannel and software defined architectures.

The post Emerson Extends RF Test Capabilities to New Multiport and MIMO Test Applications appeared first on ELE Times.

​AI is no longer just creating chatbots; now it can plan, use external tools, scrape data, and perform multi-step processes. Frameworks for creating AI agents are making this possible. AI agent frameworks are software suites for designing, prototyping, orchestrating, debugging, and deploying AI agents.

These frameworks mean that in 2026, you will be able to rapidly prototype new AI agents or create operational AI agents for complex workflows, multi-agent workflows, and enterprise-scale applications. Here are 10 popular frameworks and why to use them.

1. LangGraph

Created by LangChain, LangGraph is a framework for building stateful AI agents with granular control over execution. It incorporates a graph-based structure that manages new execution flows with conditional logic, persistence, human approval, and the ability to recover from interruption. It’s ideal for enterprise use and long-term agents.

2. CrewAI

CrewAI is a collaborative, role-based platform for managing multi-agent artificial intelligence. For multi-AI applications, coders can assign each agent its own role and then group them into teams to conduct research, analysis, content creation, or run a business. It has a very simple product design and user interface for fast prototyping.

3. OpenAI Agents SDK

The OpenAI Agents SDK allows you to construct agents that invoke functions, hand off tasks between agents, and utilize guardrails. It includes tracing, which allows you to observe agent behaviour and debug your application’s flow. A good choice for applications with tool use and multiple agents in collaboration.

4. Google Agent Development Kit (ADK)

The Google Agent Development Kit (ADK) can be used to develop, test, and deploy your AI agents- or multi-agent systems. The kit has a modular architecture, supports integrations with popular tools and coding languages (Python, TypeScript, Go, and Java), and may be useful for people building Gemini apps or using Google Cloud.

5. Microsoft Agent Framework

Microsoft Agent Framework offers the combined features of AutoGen and Semantic Kernel. It can implement single agents, multi-agent workflows, memory, tool integration, and human-in-the-loop execution. It supports Python and .NET​, the latter being useful for organisations building AI in the Microsoft ecosystem and on Azure.

6. LlamaIndex

LlamaIndex is a great fit for document-focused or enterprise knowledge agents. Its indexing and retrieval features, as well as data connectors, allow developers to create search functions over internal data or information, as well as generate context sensitive responses. It’s a good choice for RAG and document analysis tools or research assistants.

7. Mastra

Mastra is a TypeScript framework for creating AI agents and applications. It supplies tools to developers for creating agents, workflows, and integrations in the JavaScript/TypeScript ecosystem. This is mainly for teams creating AI based mostly web apps without a separate Python agent layer.

8. Pydantic AI

Type-safe application development for AI with structured data validation. Pydantic AI empowers developers to author and verify expected output, compose tools, and develop applications where the interface to the language model is more explicit and predictable than standard software. It is particularly relevant for Python teams looking for predictable data validation and maintainability.

9. Haystack

Haystack, a deep-set built tool, is a modular framework designed for developing AI applications with pipelines, retrieval, and agent components. Document search and processing, search, and RAG are all features of the tool. Developers can leverage search with tool use to develop knowledge-intensive chatbots and business information systems.

10. Claude Agent SDK

The Claude Agent SDK from Anthropic offers a set of primitives to build agents based on Anthropic’s Claude models. It provides support for tool-based workflows and integrations that might be useful for coding, file workflows, and research-based use cases. Review the model provider requirements and tool permissions before selecting this option.

How Should Developers Choose an AI Agent Framework?

The best framework for you will depend on your use case, programming language, provider, and operational needs: complex, stateful workflows might use LangGraph; CrewAI is a good choice for role-oriented, collaborative workflows; OpenAI Agents SDK provides an easy-to-implement path to tool-using agents; Google ADK is best if you’re in the Google Cloud world; for document-centric workflows, check out Llama Index and Haystack.

For TypeScript teams, Mastra; for anyone who loves typed Python, Pydantic AI; and for enterprise solutions, Microsoft Agent Framework. You’ll want to consider documentation, observability, security controls, deployment options, licensing, and ongoing costs. You don’t necessarily need an agent framework- a single model invocation or a traditional ETL-style workflow may be simpler to run.

Conclusion

Frameworks for AI agents are emerging as essential components of applications that require more than text generation. They help developers leverage AI capabilities into functional software by reducing the complexity of tool integration, orchestrating workflows, managing context across multiple agents, and orchestrating multi-agent systems. There’s no single best option. The best framework depends on the scale, complexity, technical stack, reliability requirements, and costs of your project.

The post Top 10 AI Agent Frameworks for Developers in 2026 appeared first on ELE Times.

Rapid technological acceleration and shifting regulatory landscapes warrant robust governance, risk management, and workforce readiness, skill development, essential to build national cyber resilience. Anwesh Koley of ELE Times interacted with key leadership from ISACA, who shared strategic perspectives on India’s evolving cybersecurity posture, the impact of legislative initiatives and the pressing need to bridge the domain’s skills gap.

ELE Times: At the onset, could you brief us on what is currently happening at ISACA regarding cybersecurity, governance, risk, compliance, and related fields?

Team ISACA: This is a pivotal moment for cybersecurity in India. Over the last five years, the government has made multiple moves indicating a clear focus on increasing national cyber posture and resilience.

Crucial legislation like the Digital Personal Data Protection (DPDP) Act has been pivotal. Furthermore, traditional regulators such as the Reserve Bank of India (RBI) and the Securities and Exchange Board of India (SEBI)—particularly within financial services—have consistently remained at the forefront of cybersecurity enforcement. Across sectors, there is expanding interest in skill development, awareness, and building a deeper appreciation of what constitutes a robust cybersecurity program.

However, the ecosystem remains fragmented. A wide spectrum of policymakers and regulators operate with varying levels of understanding regarding how cyber risks impact their specific sectors. While entities like CERT-In and the National Cyber Security Coordinator serve as positive signals, the lack of a single, unifying national body to tie these initiatives together leads to disparate, localised pockets of progress rather than a fully unified national strategy.

On artificial intelligence, the implementation across sectors is similarly experiencing fragmented approaches. While there is currently no plan to introduce national AI legislation in India—keeping the focus intentionally on fostering innovation—the rapid pace of AI adoption makes AI security and enterprise governance frameworks critical.

Finally, addressing the skills gap remains essential. Building a well-qualified workforce requires continuous, innovative development initiatives to establish standard capabilities across all domains.

ELE Times: You raised the important issue of skill development. What specific avenues is ISACA focusing on, and where does India feature in your strategic plans regarding quality skill development?

Team ISACA: India is a critical jurisdiction for ISACA, and our organisation is making significant strategic investments here. India represents our largest concentration of staff outside North America. Beyond building local cybersecurity postures and AI capabilities, India aims to leverage cybersecurity and AI as economic growth levers to position itself as a regional technology leader. We actively support this vision alongside our local network of 10,000 members organised across 12 chapters.

There is an important opportunity for India to make the cybersecurity sector a national economic success story, mirroring its historical success in the software and IT services industry.

Skill development extends beyond pure-play cybersecurity specialists; there is surging demand for cybersecurity expertise in adjacent corporate roles—including procurement, legal, project management, and executive board governance. Beyond specialised operational roles, building a robust national talent pool enables the creation and export of domestic cybersecurity products and services.

To support these goals, ISACA delivers a multi-pronged approach:

• Extensive Credential Portfolio: Offering structured pathways from foundational competencies through to specialist credentials, including new certifications designed as entry points into cybersecurity careers.
• Enterprise Governance & Benchmarking Frameworks: Providing frameworks (such as COBIT) that allow enterprises to review, assess, and benchmark their maturity in adopting AI, IT controls, IT audit assurance, and governance.
• Continuous Learning & Local Framework Alignment: Moving away from static, one-time certifications toward mandatory, continuous professional education (CPE). Additionally, our credentials and workforce frameworks adapt to local regulatory requirements to ensure seamless regional implementation.

ELE Times: How do you assess the current Indian workforce and its preparedness toward accepting cybersecurity norms and building long-term careers in this domain across various industry verticals?

Team ISACA: Workforce preparedness varies across sectors. Industries like financial services have made substantial investments, and critical infrastructure sectors are actively advancing their capabilities. However, operational challenges remain, particularly within public sector capacity and municipal-level infrastructure where personnel manage multiple operational responsibilities.

A central challenge is the sheer speed at which the field evolves. Rather than replacing cybersecurity roles, technologies like AI are reshaping job requirements rapidly. Cybersecurity professionals must continually adapt to keep pace with emerging technology stacks.

Additionally, growth is accelerating in intersecting domains like risk management and regulatory compliance. Navigating this environment demands an agile mindset and a commitment to continuous learning to stay ahead of evolving threats and technological shifts.

ELE Times: Expanding beyond India, how do you see the broader South Asian and regional markets performing in terms of cybersecurity readiness and willingness to adopt scaling frameworks?

Team ISACA: India acts as a primary technology and policy leader across South Asia. While smaller regional jurisdictions can sometimes move faster due to scale, there is a widespread recognition across South Asia that robust cybersecurity adoption is mandatory to participate effectively in the global digital economy.

Beyond South Asia, regions like the Middle East—Saudi Arabia in particular—are moving rapidly to establish strong national cybersecurity profiles. India holds a strong position to offer thought leadership and drive regional dialogue across these markets.

Regarding the willingness to adopt cyber norms and frameworks across the region, industry resistance is rarely the barrier. Instead, organisations face challenges around regulatory proliferation and a lack of harmonisation.

When enterprise teams must navigate overlapping, unaligned compliance requirements from multiple authorities, compliance becomes burdensome. Diverting technical staff and financial resources purely toward administrative compliance exercises can take focus away from vital operational security tasks—such as active risk management, system patching, and continuous monitoring.

The industry is receptive to regulation, standards, and guidance. However, the priority must be regulatory coherence and harmonisation so that compliance directly drives meaningful improvements in an organisation’s overall security posture.

The post Advancing Cyber Posture, Governance, and Skill Development in India and Beyond appeared first on ELE Times.

It lets you understand and be warned acoustically and visually when the concentration of ethyl alcohol in your breath is over the tolerable threshold, optionally activating a relay.

When you say “breathalyzer”, your thoughts immediately run to the roadside checks carried out by the police to identify and fine those driving under the influence; on those occasions a device is used that has an interchangeable mouthpiece (for obvious hygiene reasons) where the person under test blows and the breath is directed against the sensitive surface of an ethyl alcohol sensor. We want to offer you something like that in these pages, but not so that you go and swell the ranks of roadside checks or play at being “rookie cops” – rather because it can always be useful to check, after getting up from the table and before taking the car, that you are not risking being in violation.

In fact, just like the alcohol testers available commercially for that purpose, it lets you measure the blood alcohol level (that is, the specific amount of alcohol in the blood) of the puff of breath directed against the surface of the sensor used. The latter is a device labelled MQ-3, able to detect not only alcohol vapours but also various types of gas and hydrocarbons, although the aerosol compound it is most sensitive to is certainly alcohol, followed by petrol; we will use it in the circuit described below, analysing its electrical schematic. First, though, let’s take stock of the sensor, explaining how it works.

The MQ-3 sensor

This device is made by Hanwei Electronics (www.hwsensor.com) and is based on the well-established metal oxide film structure, long used successfully to detect gases: the sensitive element is an oxide layer that is heated by a filament powered through two dedicated pins (labelled H for heater) so that it reaches the optimal temperature for its operation; the filament has a cold resistance (at room temperature) of about 33 ohm, is made of a nickel-chromium alloy and has no polarity, but should preferably be powered with direct current, at least for our purposes.

The metal film is applied to a tubular ceramic support made of alumina (Al2O3), which is an electrically insulating material; it is made of tin dioxide (SnO2) and its characteristic is that, when heated, it becomes permeable to the atoms of the gases it is sensitive to. Contact with ethyl alcohol and the resulting oxidation-reduction reaction alter the conductivity of the tin dioxide film, which under normal conditions is very low: in fact the resistance measurable at rest is in the order of kohm. To be precise, we are in the order of a few hundred ohm, up to 20 kohm for ethyl alcohol concentrations varying between 10 and 0.05 mg/L, corresponding to the range measurable by the sensor (the higher the concentration, the lower the resistance and vice versa); the reference resistance given by the manufacturer for an ethyl alcohol concentration of 0.4 mg/L is defined as Ro. The oxidation-reduction reaction triggered by the alcohol (taking into account the oxygen content of the surrounding air) causes the release of electrons that increase the conductivity of the sensitive element and therefore lower the resistance.

To detect the variation in resistance of the film, the latter is placed in a resistive divider powered at 5V and having a load resistance (the one terminating to the ground of the measurement circuit) typically of 20 kohm as shown in Fig. 1 ,

even though in our case we are around 1.2 kohm because the circuit requires it; as the concentration of alcohol in the air pushed past the protective grid of the MQ-3 increases, the resistance of the filament decreases and consequently the voltage detectable across the load resistance grows. To give you an idea of the behaviour of the sensor, that is of its thin tin dioxide film, in Fig. 2 we show the logarithmic-scale graph of its response to various types of gas and clearly to ethyl alcohol.

The curves refer to 20 °C ambient temperature and 65% relative humidity, with an oxygen concentration in the order of 21%. In the graph, Ro is the resistance of the sensor at an ethyl alcohol concentration of 0.4 mg/L in clean, dry air; Rs, on the other hand, is the resistance assumed by the sensor element at a given concentration of the compound in the air, which in this specific case is air. You can see that at 0.4 mg/L concentration Rs equals Ro and therefore the ratio is 1; at 10 mg/L Rs becomes practically 1/10 of Ro. The Rs/Ro ratio determines the sensitivity of the MQ-3. From the graph it is clear that the sensitive element of the MQ-3 sensor also responds to the presence of other aerosols and gases, because the tin dioxide film behaves that way, although in the case of the sensor we use the behaviour towards ethyl alcohol is the relevant one (the ratio varies by about 20 times over the measurement range).

Electrical schematic

Well, having established what the sensor is and how it works, we can see how the MQ-3 has been used in the circuit of the alcohol tester proposed in these pages: looking at the electrical schematic you can see that we adopt the configuration mentioned above, where the resistance of the sensitive element is placed in series with a resistance with which it forms a divider; to help you follow the schematic, in Fig. 3 we show the pinout and the internal connections of the MQ-3.

The sensor divider and the 555 timer

Since the sensor is analogue, between the series of resistors R3 and R5 we will get a voltage that varies according to the graph in Fig. 2 as a function of the ethyl alcohol concentration; since R5 is a trimmer, it is possible to vary the amplitude of the voltage obtained, that is the ratio between the potential difference presented at pin 2 of the integrated circuit U1 and ground and the supply voltage of the filament, which is 5 volts DC. In accordance with the manufacturer’s prescriptions, in series with the heater filament we have placed a resistor that limits its current and with it the dissipated power, staying within safe values that guarantee both the correct response to alcohol and long-term durability.

We use the signal provided by the sensitive element, and therefore the voltage across the series R3-R5, in a fairly unusual way: no reading by the DAC of a microcontroller or anything like that, but much more simply we send it to the trigger input of an ordinary 555 timer, whose internal flip-flops we exploit. To understand what role the 555 plays we need to look at what is inside it, with the help of the block diagram shown in Fig. 4 :

The internal comparators and flip-flop

Inside the 555 we find two comparators sharing a ladder voltage divider of resistors that biases the inverting input of the upper op-amp and the non-inverting input of the lower one; of the comparators, the non-inverting input of the upper one is made accessible from outside (the terms upper and lower refer to the reference potential they receive from the multiple divider, so the upper comparator receives the higher voltage), corresponding to pin 6 (THR, that is Threshold), and the inverting input of the upper one, corresponding to pin 2 (TRI, that is Trigger).

The node between the first and second resistor (starting from the positive supply, that is from pin 8) of the multiple reference divider of the two comparators is brought outside through pin 5 (CV, Control Voltage) and this allows the reference voltages of the comparators to be altered, so as to control with an external voltage the operating frequency in the astable configuration (performing the frequency shift as is done in VCOs, that is voltage-controlled oscillators) or the pulse duration in the timer (monostable) one. But this feature does not interest us for the circuit we are describing.

What interests us is what comes next: the outputs of the comparators drive one the RESET (that of the upper comparator) and the other the SET input (lower comparator) of an RS-type flip-flop, which is a logic circuit whose direct output (Q) goes to a high level (corresponding to about the potential of pin 8) when SET is at a high level, that is it takes the logic zero if SET is brought to about zero volts; the output behaves in exactly the opposite way when the RESET input is activated, which, when it is placed at a logic high level, resets the flip-flop, bringing the logic state of the direct output Q to zero.

The flip-flop has a complementary output (/Q), whose logic state is always the inverse of Q; in the 555 it drives the base of an NPN transistor wired in open-collector configuration, whose emitter is connected to ground and whose collector goes to the DIS (Discharge, 7) pin, which we do not use in this application. The direct output, Q, is connected to the OUT (3) pin of the IC through an internal push-pull buffer, capable of sourcing a maximum of 200 mA. The negative supply, that is the 555’s reference ground, corresponds to pin 1, while pin 4 is the flip-flop’s active-low reset: this pin lets you force a reset of the circuit from outside by applying a logic low, but we have tied it permanently high because we don’t need it.

Configuring the alarm

In our application we connected pin 6 to the positive supply, so the upper comparator can never switch and its output stays permanently at a logic high, and with it the flip-flop reset. So we only use the lower comparator, which is tied to the trigger pin; every time pin 2 is pulled to a logic low, the 555’s output (pin 3) goes to a logic high. Therefore, as long as the alcohol concentration is low and the voltage across the resistor chain is below 1/3 of the 555’s supply voltage, the output of the latter stays high; but if the concentration rises enough to bring the voltage between pin 2 and ground above 1/3 of the supply voltage, the lower comparator drives its output low and the flip-flop’s set returns to a low level. In this condition the reset state prevails and the flip-flop’s direct output goes low; this condition powers the piezo buzzer and the positive-test LED labelled LD2, biased through the current-limiting resistor R2.

The relay for use as a gas alarm

But that’s not all: pin 3 of the 555 also powers the coil of relay RL1, which we deliberately included in the circuit so that anyone who wants to can also use it together with an alarm control panel, to detect the presence of gases such as LPG and other gases the sensor can detect, as well as petrol and alcohol aerosols. To protect the 555’s output transistor from the reverse overvoltages that occur across the relay coil when it is de-energised, we included diode D1, connected in antiparallel with it. The relay is very useful if you intend to use the circuit as a gas detector and alarm, but you have to bear in mind that you need a hermetically sealed relay, since even the small spark produced by switching the load could trigger an explosion. Of RL1 we make the changeover available only on contacts C and NO, so it can be used as a switch.

Powering the circuit

We finish the description of the schematic with the power supply, which must be a stabilised DC voltage of 5V maximum and is applied to the power plug observing the polarity shown; to switch the circuit on and off we included a changeover switch used as a power switch and labelled SW1, from whose wiper the supply is taken for the MQ-3 sensor, the 555 and the rest of the circuit, including the relay coil (5V). Between the positive line and ground, at the output of the switch, is LED LD1, powered through resistor R1, which acts as a power-on indicator for the circuit.

Building and using it

Well, now that we have described how the circuit works, we can look at how to build it, starting from the printed circuit board we designed to hold all the required components, sensor included; the PCB is double-sided and, so that you can make it by photoetching, we publish the two copper-side traces to print on tracing paper or acetate to produce the necessary films.

Assembling the printed circuit board

Once the printed circuit board is etched and drilled, you can fit the components starting with the lowest-profile ones such as the resistors and the silicon diode (to be oriented as shown in the assembly drawing visible in these pages), then moving on to the socket for the 555, the horizontal trimmer and the slide switch with right-angle pins. Then come the DC power jack, the MQ-3 sensor, the 5V miniature relay, the buzzer and the two LEDs. Pay attention to the polarity of the two LEDs, whose cathodes (corresponding to the flat on the body) must face the piezo buzzer BZ1; the latter also has a polarity to respect, so when inserting it into its holes on the board keep the + facing the side of the board along which it is positioned. The photos of the prototype and the assembly drawing you find in these pages are helpful during the various assembly stages anyway.

Power supply and calibration

As for use, you need a mains power supply with a stabilised 5 Vdc output, capable of delivering 250 milliamps of current; battery power is also possible using a battery pack with a 5V regulator or, better still, a power bank able to provide a 5V voltage and enough current for good autonomy, considering the circuit’s draw, which even at rest sits at around 170 mA because of the current drawn by the heater filament of the MQ-3 sensor.

To use the product, first turn the switch to ON; after that the buzzer will probably sound, because you will most likely need to adjust the trimmer to set the alarm threshold, that is the sensitivity of our alcohol tester. At this point, if there is no alcohol in the air, turn the trimmer anticlockwise and then release it the moment the buzzer stops sounding. For correct calibration it would be a good idea to leave the device powered for a few minutes (at least 5 minutes) with the trimmer at minimum and only then proceed with calibration, because by that point the sensor, or rather its sensing film, will have reached its operating temperature and its behaviour will be stable. Once calibration is done you can use the device.

Using the device

If alcohol is present, the red LED will light up, the buzzer will sound and the relay will energise, closing the contact on connector CN1. It should be pointed out that this is not a breathalyser, because our circuit does not take measurements: it is an alcohol tester, that is something that warns you if the alcohol concentration is above the set threshold; to align it with the devices used before driving, you should compare it with one of those and adjust the trimmer after breathing into it, checking when it signals excess alcohol.

Alternatively you can refer to the response graph given in Fig. 2 and set the trimmer so that LED LD2 lights up at the desired ethyl alcohol concentration; for example, you can calibrate it by putting a little denatured ethyl alcohol on a table top and holding the sensor a few centimetres away from it with the circuit powered: adjust the trimmer until the buzzer starts sounding and LED LD2 lights up, checking that once the alcohol has evaporated everything returns to rest. When putting the alcohol down, be careful not to spray it onto hot bodies or high-voltage circuits, because it would catch fire.

Related products

• Breathalyzer with LED and Buzzer Alarm – Kit

The post Breathalyzer with audible and visual alarm appeared first on Open Electronics.

Artificial intelligence workloads are redefining the power requirements of modern data centers. Increasing GPU performance and rising rack densities are driving a shift toward 800 VDC or ±400 VDC power supply architectures. To address these  requirements, Infineon Technologies AG is launching a 27kW three-phase power supply unit (PSU) solution for server ODMs and OEMs. The reference design meets the requirements of the OCP Open Rack V3 (ORV3) specification and AI server rack architectures, further expanding Infineon’s comprehensive portfolio for powering AI servers. The solution helps developers shorten time-to-market while improving rack power density, efficiency, and thermal performance.

The design combines Infineon components including 650V CoolSiC and CoolMOS MOSFETs, EiceDRIVER gate drivers, XENSIV current sensors, and PSOC microcontrollers. It achieves a peak efficiency of more than 98 percent at an input voltage of 480 VAC and 50 percent load. The reference design uses a five-level ANPC PFC topology and a three-level LLC converter, reaching a power density of 116 W/in³, compared with the ORV3 minimum requirement of 94 W/in³. An integrated energy buffer provides a hold-up time of 20 milliseconds during short power grid disturbances. It also helps smooth the line voltage during rapid GPU load changes, eliminating the need for a separate capacitor bank unit (CBU). An integrated planar magnetic structure enables a compact, modular, and scalable high-frequency transformer design.

The PFC and LLC stages are digitally controlled to respond to demanding AI load transients. The programmable power control accelerator (PPCA), integrated into the PSOC Control C3 Performance Line microcontroller, supports precise current and voltage regulation with fast dynamic response. The reference design supports a three-phase input voltage range of 311 to 528 VAC and is therefore suitable for a range of global grid standards. It achieves low input-current total harmonic distortion (iTHD) and a power factor above 0.99 across most of the load operating range. The design supports operation at ambient temperatures from -5°C to 45°C.

The post Infineon introduces 27 kW three-phase PSU solution for next- generation AI server power architectures appeared first on ELE Times.

DigiKey, the global distribution leader of electronic components and automation products, announces its agreement with TDK SensEI to serve as the first distributor for edgeRX Starter, a predictive maintenance solution designed to support improved industrial equipment performance.

TDK SensEI’s edgeRX Starter combines AI-driven multi-modal sensing with advanced analytics to enable predictive maintenance, transforming vibration and temperature data into early warning signals of developing issues. By continuously capturing changes in machine behavior, the system uses AI to identify patterns and anomalies that may signal developing equipment issues and delivers predictive insights with prescriptive guidance. This helps teams act early to prevent downtime, extend asset life, optimize performance and keep production lines moving.

“DigiKey is proud to be the first online electronic components distributor of the edgeRX Starter, especially since our team has been using it in our product distribution center ourselves,” said Jason Simoneau, vice president, passives and multi-market semiconductors, for DigiKey. “This cohesive solution’s predictive maintenance capabilities have benefited our team, and we look forward to offering our customers access to the same innovative system.”

TDK SensEI is addressing a critical gap in the market by delivering a solution for industrial businesses that often lack the resources or in-house expertise to implement advanced predictive maintenance (PdM) systems with AI.

“We’ve created edgeRX Starter to make predictive maintenance easier to adopt for industrial teams of all sizes,” said Sandeep Pandya, CEO of TDK SensEI. “With edgeRX Starter, we’re making predictive maintenance more accessible, helping teams transform uncertainty into confidence and reactive fixes into proactive decisions.”

DigiKey has more new product introductions (NPIs) on its shelves than any other electronics distributor, allowing customers to build the machines and devices that accelerate progress in industries such as healthcare, AI, energy, industrial automation, sustainability and IoT.

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India’s premier indigenous video security brand, PRAMA displayed its innovative transportation solutions at the 14 th Edition of Traffic Infra Tech Expo. Prama India had participated in the Traffic Infratech Expo held between 07-08 October at Bharat Mandapam in New Delhi. The PRAMA booth displayed the latest indigenous Transport Security Solutions. These products and solutions including Traffic Control Solution, Toll Plaza Surveillance, Facial Recognition Terminal, Advanced Traffic Management solution, FRT Solutions, Storage Products, UPS, Network & Transmission, ADAS, and PRAMA VMS solutions. PRAMA booth at the Traffic Infratech Expo displayed its indigenously manufactured transportation security products and solutions.

The two-day ITS India Congress 2026 was concurrently organized along with the Traffic Infratech Expo event, it covered eclectic mix of contemporary themes on transportation, traffic management, logistics, Mobility, Smart Cities Parking Management. In the ITS India Congress, Conference theme was ‘Connected, Integrated & Intelligent Mobility for Viksit Bharat 2047’. Prama India representative Prashant Hegde, Vertical Head- PPOG, Heavy Industries & Transportation, participated in a panel discussion on the theme ‘Citizen Centric Intelligent Transportation Systems for Viksit Bharat’. He said “PRAMA offers advanced solution for Indian Traffic Scenarios. PRAMA’s ATMS integrates traffic monitoring, AI analytics, incident detection, ANPR, speed enforcement, vehicle classification, driver behavior analytics, through a centralized management platform.”

Traffic Infratech Expo- 2026 provided a networking forum for government representatives, experts, project heads, system integrators and service providers to collectively find solutions to urban transport challenges. The exquisitely designed PRAMA booth got good response from the security professional community, transportation industry leaders, government representatives and delegates. The event got concluded with relevant insights on the latest developments in the India’s transport sector.

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Automotive safety has grown up in layers. Passive safety, things like seatbelts and airbags, reduces harm once a crash is already happening. Active safety, such as anti-lock braking and electronic stability control, steps in while you drive to help prevent the crash in the first place. The newest layer, predictive safety, uses driver assistance and automated driving to remove the mistake before it occurs. Electrification adds one more dimension, because the battery itself now needs surveillance.

Behind every one of these layers sits sensing: a small device measuring pressure, acceleration or angular rate and passing it to a control unit that decides what to do. This article looks at where that sensing is advancing across the vehicle, from the tire and crash detection to vehicle stability and the battery in an electric car.

Safety Starts at The Tire

Tires are easy to overlook, but an under-inflated tire handles worse, takes longer to stop and is more likely to fail at speed. On an electric car it also cuts range. That is why tire-pressure monitoring is mandated in many markets and has become one of the most widespread passive-safety features on the road.

The job is harder than it sounds. A sensor sits inside each tire, measuring pressure and temperature and sending the reading to the vehicle by radio. It has to run for years on a small battery, so it spends most of its life asleep, and an accelerometer wakes it once the wheel starts turning and helps the system work out which wheel it is fitted to.

ST’s NTM88 family covers this across vehicle types, from passenger cars and two-wheelers to light trucks, buses, heavy trucks and trailers, and even agricultural equipment, with pressure ranges up to around 1500 kPa. Each device packs the pressure sensor, an 8-bit microcontroller with a dedicated TPMS firmware library, a dual-axis accelerometer for wake-up and localization, and the radio interfaces into a 4 by 4 mm package. The sensors are AEC-Q100 qualified, rated to +125 °C, draw as little as 180 nA in sleep, and are in mass production.

Passive Safety: Airbags And Restraints

Passive safety is about limiting the harm once a crash can no longer be avoided, and that depends on recognizing the impact fast enough to act on it. Small inertial sensors placed around the vehicle sense the sharp deceleration of a collision and signal the airbag control unit to fire the airbags and tension the seatbelts at the right instant. The same kind of sensing supports pedestrian protection, where the system responds to an impact at the front of the car, and rollover detection, where it senses the vehicle beginning to tip. ST’s crash-detection inertial sensors, including the NXLS95322AES and NXLS95422AES, are built for these roles.

Active Safety Keeps the Car Stable

Active safety works while the car is moving. Anti-lock braking came first; electronic stability control (ESC) built on it and is now standard in many regions. ESC and traction control track how the vehicle is behaving, its rotation and acceleration, its wheel speeds and steering input, and if they sense the car starting to slide or a wheel losing grip, they brake individual wheels or trim engine torque to bring it back into line. Rollover prevention works the same way.

All of this depends on inertial sensing that stays accurate in a demanding chassis environment, close to heat and vibration, and that is dependable enough to sit inside a safety function. ST’s combo sensors family targets these vehicle-stability and traction-control roles, the kind of sensing that lets a stability system read the vehicle’s motion precisely enough to step in at the right moment.

A New Frontier: Protecting The EV Battery

Electrification brings a safety concern that combustion cars never had: thermal runaway, where a failing lithium-ion cell overheats and can set off nearby cells. One of the earliest physical warning signs is a change in pressure inside the battery pack, which can appear before the temperature climbs far. Detecting it early buys time to warn the driver or take protective action.

A battery pressure monitoring sensor watches for those abnormal changes. ST’s NBP8 family does the work at the edge. Each integrates an 8-bit microcontroller with firmware and user-selectable detection algorithms, a fixed threshold, a change in pressure, or a rate of change over time, so the sensor itself can flag an anomaly and let the rest of the system stay powered down until something happens. The parts cover an absolute pressure range of 40 to 250 kPa with accuracy within about 1.2%, are AEC-Q100 qualified and rated to +125 °C.

Toward Predictive Safety

Starting with the safety of the tire, the newest layer, predictive safety aims to prevent the mistake altogether, through driver assistance and automated driving. Part of that is active prevention: monitoring the driver, and combining that with data from the navigation, active-safety and tire systems so the car can respond to a developing risk early. It also relies on fusing several sensing types, cameras, radar, lidar, satellite positioning and inertial units, into a single picture of the car and its surroundings.

The same forward-looking approach reaches the powertrain, where a vibration sensor on an electric vehicle’s traction inverter can catch the early signs of wear and trigger predictive maintenance before a fault develops. It is a large field, but the underlying direction is the same as everywhere else: sensing that is more accurate, and closer to the source, makes the whole system safer.

A Common Thread

Across all these layers the pattern repeats. Safety keeps moving toward smaller, smarter sensors that measure a physical signal, whether pressure, acceleration or angular rate, and act on it early. Tire pressure, crash detection, vehicle stability and battery health are all current examples, and more will follow as vehicles electrify and automate.

Explore ST’s automotive MEMS and sensors portfolio to see the full range across these safety functions.

The post The Evolution of Automotive Safety and the Sensors Driving It appeared first on ELE Times.

As appliance and equipment manufacturers look for ways to reduce system costs, many are replacing displays with Human Machine Interfaces (HMIs) using high-end decoration and illuminated capacitive-touch buttons, sliders and wheels. These interfaces often require a large number of touch sensors which can increase acquisition time and reduce the signal-to-noise ratio (SNR) when using conventional controllers. To address these challenges, Microchip Technology has expanded its turnkey capacitive-touch family with the launch of MTCH3380P and MTCH3240P controllers, helping manufacturers create visually appealing, display-free HMIs at lower system cost and with less design complexity. The first in Microchip’s touch turnkey controller portfolio to incorporate parallel sensing for buttons, sliders and wheels, the devices support IEC/UL 60730 Class B functional safety certification and operation up to 105°C, making them well suited for home appliance, outdoor and industrial applications.

By acquiring data from multiple sensors simultaneously, the MTCH3380P and MTCH3240P can support a large number of buttons, sliders and wheels while maintaining fast response times, a high signal-to-noise ratio (SNR) and reliable touch detection in noisy operating environments. This performance is enabled by 12 on-chip analog-to-digital converters (ADCs) operating in parallel. The MTCH3380P supports up to 38 sensors in self-capacitance mode and 64 sensors in mutual capacitance mode, while the MTCH3240P supports up to 24 and 32 sensors, respectively, without the acquisition-time penalties often associated with high sensor counts.

Enhanced touch sensitivity and noise immunity enable reliable touch detection through thick cover materials and when users are wearing gloves. These capabilities are especially valuable in-home appliances such as ovens, cooktops, refrigerators and washing machines, where moisture and gloved operation are common, as well as in outdoor applications including EV chargers, parking meters and kiosks that require durable protective covers and dependable operation in cold-weather conditions.

“Manufacturers are increasingly looking for ways to reduce system cost while still delivering intuitive and visually appealing user interfaces. As display-free HMIs incorporate more touch controls, maintaining responsive and reliable touch performance becomes increasingly challenging,” said Giovanni Fontana, senior director of Microchip’s human machine interface division. “Our MTCH3380P and MTCH3240P controllers address the touch-sensing challenges associated with display-free interfaces by delivering fast touch acquisition and high signal-to-noise ratio, helping enable responsive, reliable operation even in demanding applications.”

With support for customer driven IEC 60730 Class B functional safety certification, the MTCH3380P and MTCH3240P devices help manufacturers work towards meeting regulatory requirements. Microchip’s touch-sensing ecosystem and turnkey controllers are designed to provide the hardware, software and development resources needed to accelerate design, evaluation and testing. Support includes the mTouch Studio development environment and evaluation hardware, including host drivers for Linux and Zephyr.

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A self-built robot arm is usually controlled by programming its movements or by holding a controller. Kelton Serra took a different route: puppet-style control. A scaled-down input device replicates the real arm and physically mirrors its structure. Move a joint on the device and the robot moves in exactly the same way. The project is a redesigned version that is cheaper, easier to build and better performing than the original.

The advantage of puppet control is how intuitive it is. There is no need to write a sequence of angles or to learn the kinematics of the manipulator. You pick up the input device and move it, and the arm follows. That makes a project which would otherwise demand complex programming accessible even to beginners. On top of that, the design is meant to be 3D printed and assembled easily.

Potentiometers instead of motors in the input device

The input device is a scaled-down replica of the arm. Instead of servo motors it carries potentiometers, one per joint. The analog voltage output by each potentiometer corresponds to the angle of that joint. So every movement of your hand on the device translates into a voltage value read by the brain of the system.

The brain of the robot arm is an Arduino Nano ESP32 board, which reads the potentiometers as inputs. The Arduino then commands the arm’s servo motors through a servo driver board, making them match the position. The cycle is continuous: read, compute, command. As a result the real arm chases the input device without any programming required.

• Arduino Nano ESP32 as the control unit
• Servo driver board to drive the motors
• Servo motors for the arm joints
• Potentiometers mounted on the scaled input device
• Belt on the wrist and rack-and-pinion on the gripper

The mechanics have been updated compared with the original. The wrist joint now uses a belt drive, while the gripper adopts a rack-and-pinion system. These two choices improve the arm’s performance and simplify construction. The structure is also designed for 3D printing, so anyone who wants to rebuild it can start from the files and assemble it piece by piece.

Anyone wanting to take on the project will find the starting point on the page with the printable files. There you’ll find the files for 3D printing, so you can begin with the mechanics and then move on to the electronics. The components do the rest: the Arduino Nano ESP32 board, the servo driver board, the servo motors and the potentiometers. For controlling traditional three-pin RC servos there is also a shield solution, handy if you would rather not wire everything by hand.

If you are looking for the control board, the compact board with integrated Wi-Fi and Bluetooth is the one the project adopts as its brain. To drive the servos, on the other hand, the shield that controls up to six traditional RC servos is an option worth keeping in mind during assembly.

The beauty of puppet control is that it requires no complicated software. There are no trajectories to plan, no torque sensors to read. You move the input device and the arm copies it. The project therefore stays within reach of anyone with a bit of manual skill and a 3D printer, without having to master advanced robotics.

For anyone who wants to rebuild it, Kelton Serra’s page with the printable files is the place to start. There you’ll find the files for 3D printing, so the mechanics of both the arm and the input device. From there you move on to the electronics and assembly, following the same logic as the original project.

Source: https://www.printables.com/model/1834841-arduino-robot-arm-and-controller-v2-rad

Related products

• Arduino NANO ESP32 with Wi-Fi and Bluetooth
• Arduino shield for RC servo control

The post DIY Robot Arm with Puppet-Style Control appeared first on Open Electronics.

CML Microsystems Plc of Maldon, UK — a designer and fabless manufacturer of mixed-signal, RF and microwave semiconductors for communications applications — has launched the CMX90A002, a compact power amplifier for next-generation ISM-band radio designs...

AI is transitioning from answer-providing computer programs to the software we refer to as AI agents, which can choose a goal, reason, come up with a plan, access external resources, and act semi-autonomously. Whereas a chatbot is only activated when a user submits a question, an AI agent can autonomously choose a next step, collect data, communicate with software, and update its plan.

Google Cloud says that these AI agents use reasoning, planning, and memory to accomplish the specific goal on behalf of the user. Companies that leverage AI agents in various fields such as software development, cybersecurity, customer support, research, finance, IT operations, enterprise automation, and many others will be more efficient and innovative.

What Is an AI Agent?

An AI agent is an AI-powered system capable of perceiving, reasoning about the goal, planning, and executing the task that needs to be done by sensing the environment. Usually, an agent would use a large language model (LLM) as the reasoning brain. The LLM, however, isn’t enough on its own, and a real-world agent needs a model, plan orchestration, knowledge sources, tools, permissions, and monitoring.

For instance, rather than merely responding to a customer inquiry about an order, an agent can extract the order number, access the latest information from an enterprise system, verify the shipment, and relay the information back to the customer. If there’s additional work to do, such as generating a support ticket, the agent can employ the right business tool. Agentic AI has the capability to go from “answering” to “acting.”

How AI Agents Work

Most AI agents work in a loop of reasoning, planning, acting, and observing. The agent starts off with a goal and some context and reasons about the current state and what actions may be taken. The planning layer can decompose the goal into subgoals and plan a sequence of actions to achieve those subgoals.

It thinks about what tool/step/action to take, takes the action, watches the outcomes, interprets the data, then takes the next step. This cycle continues until either the goal is reached, the agent hits a terminal state, or makes the last step before human input. This paradigm is similar to the ReAct (reason + act) pattern, which interleaves reasoning and acting to allow external information to inform later inference. Google Cloud and AWS both detail agent architectures based on this iterative process.

Planning: Breaking Complex Goals into Steps

This enables an AI agent to pursue a multi-step goal rather than addressing every prompt as a singular question. Imagine a business requests an agent to research a network issue. The agent might want to review system logs, recognize abnormal activity, analyse recent configuration updates, review security alerts, and draft a recommendation.

Rather than doing it all at once, a planning layer can break the goal into sub-goals, or re-plan if a tool reveals something unforeseen. The latest generation of agent platforms are starting to include support for these kinds of workflows. For instance, Microsoft’s modern Agent Framework has notions of tools, sessions, persistent memory, multi-step workflows, and agents that plan, execute, and follow up on tasks, so planning becomes the task management layer of the agent.

Memory: Giving Agents Context and Continuity

Memory is the other significant aspect in which a simple AI app differs from more advanced agents. An agent usually requires some short-term working memory, which keeps the overall context of the current task, the conversational context, intermediate results, the tools’ results, and the current state of the workflow.

Long-term memory has another function. It can store relevant information for several sessions, like preferences, previous conversations, history of tasks performed, or important lessons. Google Cloud’s existing agent architecture also distinguishes between short-term memory and long-term knowledge and memory. Its Agent Platform includes a Memory Bank that can remember information on a personal level for several sessions.

OpenAI’s agent tooling already enables persistent memory between runs, so agents can hold on to reusable data from their previous work rather than just re-running an entire conversation. That said, memory introduced new engineering trade-offs. Companies have to think through issues of data correctness, privacy, storage, access, and the risk of using stale data.

Tools: How AI Agents Act

An AI model can produce text, but an agent requires tools to act in the real world. The set of tools an agent can use might consist of APIs, databases, search, enterprise applications, code execution environments, browsers, file systems, and business apps. With tool calling, an agent can access current information or perform actions that are beyond the capabilities of a model. For instance, a software-development agent could examine a code repository, run tests, analyse an error, and generate a recommended code update.

An accounts agent could access approved financial data and generate a report. Forthcoming enterprise IT architectures are also converging on standards for interoperability, including the Model Context Protocol (MCP), which AWS says enables discovery and interoperability with other tools. A similar architecture exists for accessing tools through the MAC. But since the range of tools that agents could use would be vast, access to tools must be carefully regulated; AWS suggests, for example, authorisation, input/output validation, access to a trusted tool registry, monitoring and human review for high-risk tasks.

Reasoning: The Decision-Making Engine

Reasoning relates to what an agent will do, given the information to hand. By knowing how reasoning-enabled models make decisions, we can compare alternatives, evaluate potential courses of action, consider constraints, and understand how tools may be of value.

But reasoning alone does not make an agent right. An agent may not be aligned to a goal, rely on false information, adopt the wrong tool, get caught in an inefficient loop, and much more. That’s why production-safe agent systems are combining model reasoning with grounding and evaluation, observability, policy controls, human oversight, and more. 2026 Agentic AI guidance at AWS is for dependable, secure, observable, cost-conscious, bounded autonomy in production.

AI Agents vs Traditional Automation

The most basic type of automation is the X then Y type. This is the beauty of AI. It can go further than that. It can understand what it’s targeting, determine how it needs to behave, and make alterations to the sequence in light of incoming data.

For instance: A traditional automated can order system would send an email informing of the delayed can order. A smarter AI agent would explore the cause of the delayed can order, investigate the system, find out that it is a customer who needs to be informed, write the message, and escalate the matter if it is outside the domain approved by the system. But this level of sophistication introduced even greater complexity and risks. Greater autonomy demands greater ownership over permission, diagnostics, and failover.

Where AI Agents Are Being Used

AI agents are being applied in nearly every enterprise application. In the customer-service domain, they can research queries, fetch account details, and carry out sanctioned actions. In the software engineering space, coding agents can explore repositories, author or update code, test, and debug programs. Cybersecurity agents can research alarms, relate data, and trigger incident-response workflows.

IT-operations agents can research fault domains and suggest or carry out resolution actions. Other areas include research, supply-chain management, financial analysis, healthcare management, and business-process automation. As an example, Google Cloud currently has an agent platform that offers agents tailored to enterprise workflows, discovery of information, and content creation.

The Security Challenge

These same properties that make AI agents helpful could also make them risky. An agent that has access to databases, APIs, or company software can perform harmful things if it’s not tightly controlled. Today’s agent security design therefore involves least-privilege access, identity and role management, use authorisation, input sanity checks, audit trails, speed restrictions, and human approval for high-impact tasks. AWS’s newest guidance advocates limited freedom and clear controls of agents’ activities, while its guidance on secure use of tools advocates policy-based authorisation and verification of tools before they’re used.

The Future of AI Agents

AI agents aren’t simply going to be standalone assistance, but multi-agent systems that can perform more and more complex workflows. A multi-agent architecture is naturally distributed across specialised agents, with some having separate roles (for research, analysis, and execution, for example). The architecture therefore isn’t simply LLM + chatbot. It’s a wider stack of models, reasoning, planning, memory, tools, orchestration, identity, security, and observability.

The real opportunity isn’t to give AI the ultimate in autonomy, but rather the right amount of autonomy for the task at hand. As enterprises consider how to bring agents from experiment to production, reliability, governance, and measurable business impact will be as critical as model intelligence.

Conclusion

AI agents are a new type of software agent that uses one or more types of artificial intelligence. An AI agent is made up of a mixture of reasoning, planning, memory, and tools, which makes the agent capable of reaching goals, responding to change, and completing long-term tasks.

The technology is still evolving, but the trend is clear: Future AI-beyond the ones and zeros of a cloud model will manifest as digital workers that will be able to understand objectives, coordinate activities, and operate within applications. Their success will rely on more than the intelligence of the model behind them; it will depend on the design of systems for safety and reliability.

The post AI Agents Explained: How Planning, Memory, Tools and Reasoning Work appeared first on ELE Times.

Stable states: Two stable states Working idea: • Press Trigger (S₁) —> Output goes high • Press Reset (S₂) —> Output goes low • Output remains in that state until next input. submitted by /u/SpecialistRare832 [link] [comments]

A stacked-output topology, combined with a weighted feedback network, improves cross-regulation in multi-output flyback converters. This approach allows the main regulated output and the stacked auxiliary output to contribute to the feedback loop, reducing the voltage deviation typically observed in semi-regulated flyback outputs.

This article applies that methodology to a complete offline auxiliary power supply based on a flyback controller. The design generates four output rails from an 85 VAC to 265 VAC input: a main regulated +5 V output, a stacked +12 V output, a post-regulated +3.3 V output, and a low-current -12 V auxiliary output.

The main design steps are presented, including the input bulk capacitor, transformer turns ratio, magnetizing inductance, current-sense resistor, weighted feedback network, and selection of the post-regulation stages.

The objective is not only to calculate the main power stage parameters, but also to show how the stacked-output topology and the weighted feedback network can be implemented in a practical converter. Experimental results are then used to evaluate the achieved cross-regulation, output voltage accuracy, and overall converter performance.

System architecture overview and design specifications

This section will present the complete system architecture designed to implement a stacked-output methodology. We’ll cover a high-level overview of the power stage partitioning and introduce the key ICs selected for each functional block.

Figure 1 shows the block diagram of the complete four-output power supply.

Figure 1 The above block diagram highlights the complete four-output flyback converter. Source: Monolithic Power Systems

The architecture is centered on a main flyback converter controlled by the MPX2002. This primary converter directly generates two positive rails (+5 V and +12 V) and a negative rail for the low-dropout (LDO) regulators. The final +3.3 V output is derived using a high-efficiency post-regulator. The specific roles and interactions are described below.

• Main regulated output (+5V): This is the primary regulated rail. The solution is based on the MPX2002, an all-in-one flyback controller with integrated 650-V primary control circuitry and a secondary 150-V synchronous rectification (SR) driver. Its key advantages are its integrated capacitive isolation, which replaces the traditional optocoupler, and quasi-resonant (QR) operation, which minimizes turn-on losses in the primary MOSFET.
• Stacked output (+12 V): This output is generated using the stacked topology, with its winding return path connected to the +5 V rail. Its regulation is achieved through the combination of passive tracking and active weighted feedback.
• Post-regulated output (+3.3 V): A high-efficiency synchronous buck converter steps down the regulated +12 V rail to +3.3 V. This approach is ideal for powering digital logic that requires a well-regulated voltage at a potentially high or dynamic current. The solution used in this example is the MP2332H, a fully integrated, high-frequency, synchronous buck converter.
• Low-current auxiliary output (-12 V): A dedicated winding from the flyback transformer feeds a simple LDO to generate the -12 V rail. This is a highly cost-effective solution to provide a stable negative voltage for low-power analog circuitry, such as operational amplifiers, where efficiency is not the primary concern. The suggested solution is based on the MP2015A, an LDO that can withstand a wide 2.5 V to 24 V input range.

Detailed design process

Step 1: Design inputs

Before starting the detailed design of the multi-output flyback converter, it’s essential to establish the core electrical specifications. These requirements dictate component selection, control strategy, and all subsequent calculations.

Table 1 Here is a summary of the design inputs. Source: Monolithic Power Systems

The following steps calculate the main parameters that make up the multi-output flyback converter.

Step 2: Flyback design

This section details the design of the core AC/DC flyback converter stage. The following key parameters will be explained and calculated:

• The required input bulk capacitance to maintain a stable DC bus voltage
• The turns ratio for each of the three secondary outputs, implementing the relationship from Equation (2)
• The transformer’s magnetizing inductance
• The primary current-sense (shunt) resistor
• The RMS currents that the primary and secondary power switches must withstand for proper dimensioning at a 90°C operating temperature

Step 2.1: Input bulk capacitor

The first design step addresses the input stage. Given the universal AC input range, which extends down to 85 VAC, the converter requires a bulk capacitor to hold up the DC bus voltage. A common design guideline suggests a minimum capacitance of 1.5 µF per watt.

However, for this design, the high maximum ambient temperature of 90°C introduces a critical constraint. To avoid excessive primary root-mean square (RMS) current and the associated thermal stress at low-line conditions, the minimum DC voltage after the diode bridge (VBULK_MIN) is explicitly limited to 85 VDC.

Based on this specific voltage requirement, the bulk capacitance (CBULK) can be calculated with Equation (1).

Since 59 µF is not a standard capacitance, the next higher standard value (62 µF/450 V) is selected. This choice provides additional hold-up margin.

Once the 62 µF input capacitance is selected, it’s crucial to calculate the maximum low-frequency input peak current (IIN_PEAK) to properly size the EMI filter. This peak current is composed of two components: the current required to deliver power to the load (ILOAD_IN) and the bulk capacitor’s peak charging current (ICBULK_PEAK).

ILOAD_IN is the average input current required by the converter at the minimum DC input voltage (85 V) and maximum output power (25 W). It can be estimated with Equation (2).

ICBULK_PEAK is the transient peak that occurs at the moment the AC line voltage equals the minimum DC bus voltage. It can be calculated with Equation (3).

The total worst-case input peak current is the sum of these two components, providing a conservative design margin estimated with Equation (4).

Therefore, for the proper dimensioning of the input filter, the differential and/or common-mode choke must be designed to avoid saturation at this peak current of 1.99 A. This ensures that the harmonics generated by the converter under all line conditions are effectively filtered.

Step 2.2: Duty cycle and required turns ratio

Continuing with the flyback design, the MPX2002 controller supports both continuous conduction mode (CCM) and QR mode. Due to the wide input range, the converter operates in CCM at the minimum 85 VAC input voltage.

Because the minimum DC input voltage is 85 VDC and the +5V rail is the main regulated output, the maximum primary-to-secondary turn ratio (NPS) can be calculated. This is done by enforcing a maximum duty cycle limit of 45% (DMAX = 0.45) to prevent subharmonic oscillation. DMAX can be calculated with Equation (5).

Where VFWD is the worst-case voltage drop across the synchronous rectifier. Based on this limit, a standard integer turns ratio of NPS1 = 12 is selected. This choice results in a maximum primary duty cycle of 41.62% at the minimum input voltage (VIN).

Having selected the required turns ratio for the +5 V output and the maximum duty cycle, the required turns ratio for the stacked +12 V output can be estimated with Equation (6).

Where VFWD_D2 is the forward voltage drop of the +12 V output’s Schottky diode (0.6 V), and VFWD_Q2 is the voltage drop across the +5 V output’s synchronous rectification MOSFET (0.05 V).

According to the result, the turns ratio from the primary to the +12 V secondary (NPS2) is a standard integer of 8.

According to the MP2015A’s datasheet, the negative output comes from a low-power linear regulator with a dropout of 0.7 V. The required turns ratio can be calculated with Equation (7).

Therefore, a primary to -12 V secondary turn ratio (NPS4) of 4 (h = 0.25) is sufficient for operation.

Step 2.3: Peak current and magnetizing inductance

Once the duty cycle and turns ratio values required for the application have been obtained, it’s important to design the core of the converter: the transformer. The peak current values and its magnetizing inductance are essential for correct transformer sizing.

Therefore, considering a switching frequency (fSW) of 70 kHz, the primary MOSFET turn-on time (tON) can be calculated using Equation (8).

To ensure CCM across the VIN range and manage the inductor ripple, a current ripple factor (KP) of 0.8 is selected. Given the average input current at low line (IAV = 0.33 A, calculated with Equation 2), the peak primary current in standard CCM (IPEAK) can be estimated with Equation (9).

Once IPEAK is obtained, the magnitude of the current ripple (IRIPPLE) can be estimated with Equation (10).

Finally, the required magnetizing inductance (LM) can be calculated with Equation (11).

Thus, the primary transformer specifications are a magnetizing inductance of 479 µH, and a calculated maximum rated peak current of 1.32 A.

Step 2.4: Shunt resistor and ramp compensation

The voltage on the shunt resistor (VSENSE) can be calculated with Equation (12).

Where VIPK_MAX is the maximum peak voltage limitation in the current-sense pin, and SRAMP is the internal slope compensation ramp. SRAMP helps to damp the subharmonic oscillation due to CCM with a duty cycle close to 50%.

With VSENSE, the shunt resistance (RSENSE) can be calculated with Equation (13).

However, since subharmonic oscillations can occur, designers must check the stability of the converter by calculating the coefficient α with Equation (14).

Since coefficient α is much smaller than 1, this confirms that the converter will remain stable during CCM.

Step 2.5: Weighted feedback resistors

Once the converter’s most important parameters are calculated, designer must determine the maximum achievable regulation for the main outputs. First, the current (I0) through the resistor from the feedback network (R0) must be calculated, since it will determine the maximum allowed current through the resistor divider. I0 can be calculated with Equation (15).

The weighted feedback resistances can be calculated to achieve 70% regulation for the +5 V output and 30% for the +12 V output using Equation (16) and Equation (17).

Thus, excellent cross-regulation can be achieved with weighted feedback and the stacked output structure.

Step 3: Linear regulator design

Once the flyback stage has been correctly designed, it’s important to calculate the main parameters that make up the linear regulator for the -12 V secondary output.

In this case, consider a value of 68 kΩ for the low-side feedback resistor (R4), a trade-off between power loss and stability. The high-side resistance (R3) can be estimated with Equation (18).

Step 4: Buck converter design

A key architectural decision involves the power source for the +3.3 V buck converter. Although the main regulated output is +5 V, a more strategic choice is to power the buck converter from the stacked +12 V rail. This configuration ensures that the buck converter’s own quiescent current provides a minimum load on the +12 V output. This small, constant load is critical for always keeping the +12 V rectifier diode forward-biased, which significantly improves voltage regulation, especially under no-load or light-load conditions.

By looking at the MP2332H’s datasheet, the feedback resistors, inductance, and input/output capacitors can obtain an approximately 1.2 MHz fSW. The MPL-AL4020-2R2 inductor is a suitable buck inductor choice since it provides low AC losses at 1 MHz fSW and some margin for peak current saturation.

Final design

Figure 2 shows the final schematics once the calculations are made for the multi-output flyback converter design.

Figure 2 Here is a view of the complete multi-output flyback converter schematic. Source: Monolithic Power Systems

Figure 3 shows the final solution that considers the schematic above as well as the PCB layout.

Figure 3 The evaluation board is based on the complete multi-output flyback converter. Source: Monolithic Power Systems

Figure 4 shows the regulation results for each output in accordance with the total design that was calculated and presented in this article.

Figure 4 The graph shows regulation results for each output at 230 VAC. Source: Monolithic Power Systems

A practical approach for multi-output auxiliary supplies

This article presented the practical design of a four-output auxiliary flyback power supply. The design covered the main power-stage parameters, including the input bulk capacitor, transformer turns ratio, magnetizing inductance, current-sense resistor, slope-compensation check, and weighted feedback resistor network.

The architecture combines a regulated +5 V rail, a stacked +12 V rail, a post-regulated +3.3 V rail, and a low-current -12 V auxiliary rail. The stacked +12 V output is referenced to the regulated +5 V rail and included in the feedback loop through the weighted resistor network, allowing the flyback controller to regulate a combination of both outputs and improve cross-regulation.

The additional rails are generated with dedicated post-regulation stages. The MP2332H buck converter generates the +3.3 V output from the +12 V rail, while also helping maintain a minimum load on the stacked output. The MP2015A LDO generates the low-current -12 V rail, providing a simple solution for auxiliary analog circuitry.

Overall, the results validate the proposed architecture as a practical approach for multi-output auxiliary supplies, balancing regulation accuracy, efficiency, cost, and circuit complexity without requiring a dedicated isolated regulator for each output.

Joan Mampel is applications engineer at Monolithic Power Systems (MPS).

Related Content

• Snubbing the Flyback Converter
• Flyback transformer tutorial: function and design
• How to simplify AC/DC flyback design with a self-biased converter
• Implementation of the primary-side regulation in flyback converters
• Increasing Multi-Output Flyback Transformer Efficiencies in Switch-Mode Power Supplies

The post Practical design for a multi-output flyback converter with improved cross-regulation appeared first on EDN.

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