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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.

The post Microchip Introduces its First Turnkey Touch Controllers for Buttons, Sliders and Wheels with Parallel Sensing appeared first on ELE Times.

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.

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.

Spotwelded a 3s2p 40A Battery custom fit inside the old battery box that had completely different sort of battery cells than 18650, thats why I had to make the pack in a staircase type of way just to get the pack to fit inside the original batterybox, then I ran balance leads out from the box thru a connector that i can take apart so that I can unhook the charger that i made from the battery, the charger is a 3s balance charger that runs on 230VAC and always fully charges eatch string of cells so that the battery will never be unbalanced. Even at about 50% charge the drill has more power now than before with the new 40A pack. Cost me less than buying a new battery thats weaker and lasts a shorter time between charges than the one I made! submitted by /u/Whyjustwhydothat [link] [comments]

As artificial intelligence workloads are redefining the power requirements of modern data centers, increasing GPU performance and rising rack densities are driving a shift toward 800VDC or ±400VDC power supply architectures. To address these requirements, Infineon Technologies AG of Munich, Germany is launching a 27kW three-phase power supply unit (PSU) solution for server ODMs and OEMs...

Livegrid Aura is a wall-mounted digital aquarium that turns air quality into a visual spectacle. A 64×192 pixel HUB75 RGB LED matrix displays virtual creatures that are not merely decorative: their health depends on the environmental values measured by a Sensirion SCD40 sensor. The panel is also interactive, thanks to a time-of-flight depth sensor that detects who is standing in front of it without using a camera.

The project is by Dhruv Kumar and is the natural evolution of Livegrid, the previous version with a 78×78 pixel matrix. The new frame is about three times wider than the original, and the result is an object that combines digital art, environmental sensors and interaction with people. Dhruv Kumar’s crowdfunding campaign gathers the details on costs and shipping for anyone who wants to support the project.

How the virtual aquarium works

The heart of the system is an ESP32-S3 that drives the LED matrix and collects data from the sensors. The SCD40 environmental sensor measures temperature, humidity and carbon dioxide. The virtual fish thrive when these values are within the normal range, while their health worsens if the air becomes stale. This way the panel becomes a visual indicator of environmental quality, far more immediate than a numeric display.

The time-of-flight depth sensor detects the distance and shape of whoever is in front of the panel. The fish follow the movements of a hand or gather around a person standing still. Moreover, the device does not use cameras: the sensor reads only shape and distance, guaranteeing the privacy of those interacting with it. All computation happens locally, without sending data to external services.

• 64×192 pixel HUB75 RGB LED matrix for the display
• Sensirion SCD40 sensor for CO2, temperature and humidity
• Time-of-flight sensor to detect presence and movement
• ESP32-S3 for local processing and control

Connectivity and home automation integration

The ESP32-S3’s built-in Wi-Fi is disabled by default. This reduces power consumption and keeps the device isolated from the network. Those who want to can enable it to unlock advanced features: a REST API, ArtNet and sACN streaming at 30 fps, and MQTT connectivity. The latter makes it possible to integrate the aquarium with home automation systems, for example to change scene when a window is opened.

The redesigned Livegrid is now three times wider, or taller if rotated, and has a built-in depth sensor. This geometry expands the space for animations and makes interaction more natural. The 64×192 pixel matrix offers higher resolution than the 78×78 of the previous version, with a panoramic format that suits a wall well.

Cost and availability

The hardware price starts at £299, about $405, plus shipping, for the first backers of the base model’s Kickstarter campaign. Anyone who wants to build a similar system can start from standard components. For the proximity sensing part, a TOF module with a VL53L0X sensor can be used, offering a solid basis for camera-free interaction experiments.

For those who prefer a more traditional development platform, an Arduino Uno R4 Wi-Fi board with a dual microcontroller can be a starting point for prototyping similar logic, even though the original project uses an ESP32-S3. The choice depends on the need to directly drive the HUB75 LED matrix and streaming protocols such as ArtNet.

Livegrid Aura shows how a decorative object can become an interactive environmental sensor. The combination of LED matrix, air quality sensors and depth detection creates an experience that changes with the environment and with the people around it. A project that looks to the future of digital art at home.

Source: https://www.kickstarter.com/projects/livegrid/livegrid-aquatic-environment-monitor/description

Related products

• TOF (Time Of Flight) Module with VL53L0X Sensor
• Arduino Uno R4 Wi-Fi with Renesas RA4M1 and Espressif ESP32-S3

The post Livegrid Aura: the digital aquarium that lives with air quality appeared first on Open Electronics.

Supplier “push” and consumer “pull”. When their efforts coordinate, moving the market in the same direction, the result can be meaningfully impactful.

Toward the beginning of last month’s introductory small form-factor pluggable (SFP) module tutorial, which was followed by a series of module teardowns, the first of which also appearing in September with another arriving later this month, I also noted the following observation.

I’ve subsequently become intrigued (also with pending editorial-coverage consequences) with the increasing (and dramatically so) cost-effectiveness of mainstream network switches, routers, and other devices based on 2.5 GbE technology.

Foreshadowing

I followed that statement with several paragraphs’ worth of past and present product examples suggestive of the dramatic advancements in performance and feature set at various price points in recent times. And I concluded that section of the piece with the following “teaser” prose.

Why 2.5 GbE (along with, to a lesser extent, 5 GbE) has become mainstream is a topic for another post another day (soon). Similarly, I’ll save for the near future more discussion on why 10 GbE SFP+ ports are appearing on mainstream gear like this.

That time is now. I’ll start with a brief reintroduction to the two unmanaged-switch case studies I highlighted last time. This one has eight 2.5 GbE RJ-45 ports, alongside two 10G SFP+ ports, and sells at Amazon for only a bit more than $40.

And this one, with four fewer 2.5 GbE ports, is priced $10 less than its larger-allotment sibling.

Admittedly, these specific devices are trendsetters from price standpoints at their associated feature sets. But not by much versus alternatives, if at all. Further to that point, neither Davuaz nor NICGIGA is a widely known brand, at least right now. But even established network equipment suppliers are selling now similar-featured products in the same price ballpark.

And admittedly, too, “vanilla” 1 GbE 5- and 8-port switches are still less expensive than these higher performance alternatives (does anyone else also remember when even those garnered significant markups over their 10 and 10/100 Mbit precursors?). But only by a factor of 2x, if that. Versus the 10x (or more) price premiums that network equipment manufactures were charging only a few years back. What’s changed, and why? Glad you asked.

Supplier “push”

The 1 GbE networking equipment market is effectively saturated at this point, at least from a wired-connectivity standpoint (hold that thought for wireless subsystem comments to come later in this piece). The only notable motivations for consumers to buy new gear are if existing hardware dies and needs to be replaced, or if it runs out of spare ports for newly added LAN clients.

What do you do, then, if you’re an equipment developer, or a supplier of silicon and software building blocks for that equipment? You rely on the longstanding “bigger numbers is better” marketing mantra in striving to convince consumers to upgrade their existing gear.

Take MaxLinear, for example. Ahead of the 2023 Computex conference, the company introduced a family of chipsets containing up to eight 2.5 GbE PHYs per device along with two optional 10G SerDes transceivers capable of implementing SFP+ ports, and supporting both unmanaged, “smart” and fully user-managed switch configurations.

At that same show, the company demonstrated functional prototype networking hardware based on those same chipsets. Subsequent teardowns suggest that system designs based on single-chip MaxLinear offerings have captured a not-insignificant percentage of the cost-effective 2.5 GbE equipment market, joining legacy multi-chip implementations leveraging Realtek and other IC suppliers.

These low-cost switches reportedly don’t hold up solidly to the heavy, multi-protocol and multi-speed packet payloads created by “power user” testers, although the degree of correlation (if any) between such worst-case traffic and more modest typical real-world data profiles also bears consideration. And I’ve also seen some online commentary suggestive of high operating temperatures and associated questionable long-term reliability, particularly of the devices’ SFP+ subsystems. To at least some degree, this discussion is likely reflective of the equipments’ fan-less designs; online forums’ comment traffic domination by those with an “axe to grind” versus those with more positive experiences should also be factored into any determination.

Consumer “pull”

I plan to upgrade my LAN from 1 GbE to a mix of 2.5 and 10 GbE spans and am accumulating equipment in anticipation of this pending transition. I’ve also set aside several other pieces of gear with my own teardown aspirations in mind. But regarding my LAN-update plans, you might reasonably ask “why”? After all, as my recent Comcast debug coverage series made clear, I don’t enjoy higher-than-1 GbE broadband downstream speeds and won’t for the foreseeable future. And putting aside my longstanding “why not” early-adopter curiosity tendencies, of course …

Part of the answer, likely unsurprisingly, is that WAN-sourced and -destined traffic is only a portion of the overall packet profusion on a typical LAN. Client-to-client traffic flows also bear consideration. To wit, I’ve happily already confirmed that although 10 GbE technically requires Cat6 Ethernet cable at a minimum, the several-dozen foot long archaic Cat5e span running from the furnace room LAN nexus to my office above it seemingly reliably handles 10 GbE speeds, too.

Still, unless I were shuttling ultra high-resolution video and other large-payload files and streams across the LAN (as I actually already do to a degree and intend to do more of in the future), and/or if the network was in use by a large family versus just myself and my wife, a 1 GbE LAN would continue to suffice. I might still be tempted to upgrade, but at lower equipment prices (therefore supplier profit margins) than otherwise if my update need was more acutely felt.

The solution to this dubious-demand predicament is multifold. For one thing, present company excepted, an increasing number of broadband customers do have multi-gig WAN tiers available to them courtesy of DOCSIS 3.1 Extended. Plus, in addition to Ethernet cable, several other easier-to-implement LAN connectivity options are also now capable of multi-gig speeds. Take MoCA, for example. My multiple recent editorial mentions of it are non-coincidental; in fact, it was the key impetus that sent me down this multi-gig “rabbit hole” in the first place.

A few months back, I was poking around the storage closet and came across the remaining two MoCA 1.1 units that had survived their product-generation siblings’ lightning-induced demises. I decided to put them on the to-donate pile and then recalled that I also had two sets of two MoCA 2.0 transceivers in my possession…which led me to then remember that these had also subsequently also been technology-superseded, by MoCA 2.5 successors supporting (as the spec version number suggests) up-to-2.5 Gbps transfer rates.

Initial skepticism as to the viability of such offerings for the mainstream volume consumer market led to surprise once I did a bit of research and realized how inexpensive 2.5 GbE switches and the like had become.

Or take Wi-Fi. Right now I’m running a Google Nest Wifi Wi-Fi 5 (802.11ac)-based mesh wireless network, with eventual plans to migrate to either a Wi-Fi 6 (802.11ax) successor comprising open source-converted, therefore mesh-capable, Linksys LN1301 (MX4300) routers, or a Wi-Fi 6E topology made up of Google Next Wifi Pro nodes.

Although, as Wikipedia notes, Wi-Fi 5 only “has multi-station throughput of at least 1.1 gigabit per second (1.1 Gbit/s) and single-link throughput of at least 500 megabits per second (0.5 Gbit/s)”, Wi-Fi 6 and 6E theoretical speeds are much greater; “Though the nominal data rate is only 37% higher than that of Wi-Fi 5, the throughput increases by at least four times, making it more efficient and reducing latency by 75%. The quintupling of overall throughput is made possible by higher spectral efficiency.” Not to mention Wi-Fi 6E’s added 6 GHz spectrum access.

That all said, both Wi-Fi 6 generations have already been superseded by Wi-Fi 7 (IEEE 802.11be) for high-end gear, complete with claims that “in a single band, throughput reaches a theoretical maximum of 23 Gbit/s.” And initial Wi-Fi 8 (802.11bn) hardware is already being demonstrated, complete with integrated 10 GbE RJ-45 wired ports.

Wikipedia wisely notes, regarding Wi-Fi 6 peak bandwidth estimates and equally relevant to other Wi-Fi generations, that “actual results are much lower”. But as I said earlier, don’t underestimate “bigger numbers are better” allure.

What about 5 GbE, and why 10G SFP(+)?

You likely already noticed that both switches showcased earlier included not only multiple 2.5 GbE RJ-45 ports but also two 10G SFP+ module cages.

The latter’s existence is more of a “stretch” to rationalize, particularly in consumer applications. This is likely why although their presence is common, it’s not pervasive, and some 2.5 GbE switches make do with one SFP+ port as an interim step. They’re often referred to as “uplinks”, reflective of their employ as connections to higher-layer network gear gear such as routers.

That said, I’m not personally aware of any consumer (versus enterprise) routers with SFP connectivity. However, they can also more generally find use in high-speed tethering multiple switches together to expand the locational topology if, as noted earlier, wired client counts grow in the future. And if you just need another RJ-45 port in your switch, a SFP+ Ethernet adapter will neatly solve your issue, too.

And more generally, what about 5 GbE? Right now, gear prices versus both 1 GbE and 2.5 GbE alternatives remain high, with associated equipment availability correspondingly limited (both as measured by overall volume and supplier and product options). That said, just as is happening now with 2.5 GbE vs legacy 1 GbE, I have no doubt that a few years down the road, once 2.5 GbE demand starts to level off with increasing market saturation, silicon suppliers and their system partners will decrease costs and fire up the 5 GbE hype machine next.

Have you already begun phasing in 2.5 (or 5, for that matter) GbE and 10G SFP+ equipment to your residence or business? Or do you have plans to do in the future? Please share your motivations, hesitations and planned timeframes in the comments!

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

Related Content

• SFP: Add-in module delivers diminutive performance, flexibility
• SFP: The single-mode module autopsy
• Debugging intermittent Comcast, parts 1, 2, 3 and 4
• Malfunctioning Ethernet cable comes up short
• The whole-house LAN: Achilles-heel alternatives, tradeoffs, and plans

The post The multi-gig Ethernet migration: Motivations and implementations appeared first on EDN.

Tiny Engineer is an open-source, 3D-printable desktop robot that gives a physical body to an AI coding agent. Krzysztof Jamroz’s project turns monitoring the agent from a passive on-screen activity into a tangible experience. Instead of watching the IDE to figure out what the AI is doing, you see it nod, gesture and type on an imaginary keyboard. The robot translates the agent’s activities, such as reading, thinking, writing code and finishing the job, into physical movements and gestures. When the task is done, it rings a bell.

Operation is simple and relies on an integration that turns the AI agent’s activities into events. These events are sent to the robot’s REST API over the LAN. Krzysztof Jamroz’s repository contains the code and files needed to rebuild the project. The Waveshare ESP32-C3-Zero board receives the commands and handles the robot’s logic and animations. The ESP32-C3 drives 5 servos through the PCA9685 PWM controller for the head, neck, hands and body movements.

Robot components and circuit

The component list is essential and well defined. The robot uses 5 PowerHD HD-1370A servos for the movements. A 0.91-inch, 128×32-pixel SSD1306 OLED display shows the status, the face and information. Audio is handled by a MAX98357A amplifier with an 8 Ω, 1 W speaker. Everything is powered at 5 V with at least 2 A of current.

The firmware is compiled with PlatformIO and the serial port runs at 115200 baud. The configured Wi-Fi network must be 2.4 GHz. The robot runs specific animations such as ‘typing’, ‘reading’, ‘thinking’ or ‘ring’ based on the commands received. For connectivity, the project uses an ESP32-C3-Zero board, but those who want to experiment can consider an ESP32 development board with Wi-Fi and Bluetooth to get closer to the project.

3D printing and customisation

An overview of the project shows how carefully designed it is. The structure is 3D-printable and the project is meant to be modified. You can change the CAD, modify the animations or connect a different agent. The repository contains everything you need to get started.

The robot is designed to be a desktop object, so its dimensions are compact. The 5 PowerHD HD-1370A servos are distributed between the head, neck, hands and body. The 0.91-inch SSD1306 OLED display is small but readable. The 8 Ω, 1 W speaker is enough for the notification sounds.

Customisation is one of the strengths. Beyond 3D printing, you can work on the firmware to change the animations. The source code is available and well organised. Communication with the AI agent happens through the REST API over the LAN, so you can adapt it to different systems.

Power and network requirements

The required power supply is 5 V and at least 2 A. This ensures the 5 servos can move without current problems. The configured Wi-Fi network must be 2.4 GHz, a common choice for IoT devices. The 115200-baud serial port makes debugging easier during development.

For those who want to dig deeper, Krzysztof Jamroz’s repository is the ideal starting point. You will find the code, the STL files and the instructions. The project shows how the work of an AI agent can be made tangible, turning monitoring into a physical experience.

Source: https://github.com/jamro/tiny-engineer

Related products

• OLED Display I2C 0.96” – VCC/GND/SCL/SDA
• ESP32 – Development board with Wi-Fi, Bluetooth and USB Type-C connector

The post Tiny Engineer: a desktop robot that gives your AI agent a body appeared first on Open Electronics.

Wolfspeed Inc of Durham, NC, USA — which makes silicon carbide (SiC) materials and power semiconductor devices — has received a conditional loan commitment letter from the US Department of War (DoW), through its Office of Strategic Capital (OSC), for up to $1.5bn of long-term (30-year) financing to support the domestic development and production of silicon carbide (SiC) materials and wide-bandgap power devices, with an additional focus on US national security applications...

DigiKey, the global distribution leader of electronic components and automation products, is hosting a webinar on how to use the company’s time-saving My Lists parts management tool entitled “Empower your everyday work with My Lists,” scheduled for Tuesday, Oct. 13, 2026, at 10 a.m. CDT. The free 60-minute virtual event will cover My Lists’ comprehensive list management and quoting features that allow users to make informed purchasing decisions, collaborate with coworkers and lock in pricing.

Managing parts across multiple sources can create complexity and inefficiency. With DigiKey’s My Lists tool, users have access to a single, centralized hub for part information, pricing, supplier details and real-time inventory status. “As a high-service distributor, DigiKey is continually improving the customer experience, and My Lists is an important tool for enhancing the sourcing process,” said Jillian Switts, senior director for e-commerce at DigiKey. “Whether you are managing a bill of materials, evaluating alternatives, checking price and availability, or coordinating with suppliers, My Lists brings everything together in one organized platform to make the product procurement process smarter and easier.”

Beyond organization, My Lists offers users measurable business benefits. The online tool generates quotes directly from product lists and locks in pricing for up to 30 days, giving users budget certainty. With immediate access to critical data that eliminates manual searching, reduces errors and accelerates decision-making, users and sourcing teams can improve pricing visibility, increase procurement cycles and deliver strategic purchasing outcomes.

During the webinar, attendees will learn to:

• Select and save substitute and alternate parts
• Use the tool to collaborate with team members or share information publicly
• Generate quotes and lock in pricing quickly
• Identify cost-saving opportunities
• Streamline bill of material (BOM) management and procurement workflows

The post DigiKey Webinar Shows How My Lists Tool Simplifies Parts Management and Quoting appeared first on ELE Times.

DigiKey, the global distribution leader of electronic components and automation products, proudly announces the opening of its new Bengaluru office, home to DigiKey India’s Global Capability Center (GCC), supporting innovation, technology enablement and business operations for teams worldwide. The company held an inauguration ceremony on Oct. 5, 2026, at its new office at Sumadhura Capitol Towers, Whitefield, Bengaluru.

“Our new Bengaluru office strengthens DigiKey’s long-term growth strategy by providing capacity to attract top talent, support business expansion and foster greater collaboration and innovation among our global team,” said Shane Zutz, vice president of human resources for DigiKey. “It also reinforces our commitment to delivering value to our customers, suppliers and team members. We are thrilled to continue this commitment in India and invest in the exceptional engineering talent and innovation ecosystem this region brings to the world.”

The office move marks an important milestone in DigiKey India’s growth journey, providing a modern workspace that supports collaboration, innovation and future expansion. India plays an increasingly important role in DigiKey’s global operations, providing access to highly skilled talent and supporting the company’s long-term innovation and digital transformation initiatives.

Since launching as a five-person prototype team in 2022, DigiKey India has grown to nearly 300 employees, reflecting the company’s continued investment in the region. Today, the high-performing Global Capability Center (GCC) serves as a strategic hub for innovation, collaboration and technology enablement, supporting DigiKey’s global operations while driving impactful solutions for the future of electronics and automation distribution.

India continues to be an important growth market and innovation center for DigiKey, and the new Bengaluru office reflects the company’s long-term commitment to the region. The investment expands access to exceptional talent, supports business growth and enhances organizational efficiency while providing a strong foundation for scaling operations and strengthening collaboration and alignment across global teams.

Bengaluru is widely recognized as one of India’s leading technology and innovation hubs, making it a strategic location for DigiKey’s continued growth. The Bengaluru GCC continues to create opportunities for talented professionals who want to contribute to a global technology organization.

The post DigiKey India Opens New Bengaluru Office appeared first on ELE Times.

Keysight Technologies, is bringing agentic AI to its software, enabling engineers to direct AI agents to automate complex design tasks. Customers can use their large language models (LLMs) to generate and optimize designs, with Keysight simulation validating the agents’ progress. This shifts how engineering is done, as teams can evaluate significantly more options in the same development time.

More than 60% of organizations expect to deploy AI agents by 2028, yet radio frequency (RF) engineering has been slower to follow. The discipline relies on specialist expertise and uses schematics and layouts that LLMs cannot read in a consistent, deterministic way, so the process remains largely manual. Keysight Advanced Design System (ADS) 2027 addresses this by letting teams record workflows as macros their agents can learn from and convert graphical designs into code they can read. Model Context Protocol (MCP) servers connect agents to the software, guiding LLMs and agents in their interaction with ADS.

When an engineer makes a request in natural language, their agent completes the task in ADS. The MCP servers provide the agent with documented skills and tools built by Keysight that execute specific RF work consistently, reducing variability caused by AI inference models generating answers statistically. The agent then runs Keysight simulation to validate its output.

Key benefits include:

• Creates an open ecosystem for agentic workflows: The MCP servers work with AI assistants and LLMs that organizations are already using, and with tools from multiple vendors in the same workflow.
• Speeds and expands design cycles: Agents handle repetitive setup and simulation steps in ADS, so engineers can cover more scenarios and find more optimal designs while reducing time to market.
• Shares engineering knowledge: Macros recorded in ADS capture an experienced engineer’s methods for colleagues and agents to reuse across teams.

Niels Faché, Senior Vice President, Keysight Design Engineering Software, said: “Agentic engineering is reshaping every stage of design from concept through verification. Our decades of simulation and domain expertise validate AI results before they reach hardware. Over time, agents will turn prior projects into organizational intelligence, so a customer’s best work is the foundation for each new design.”

The post Keysight Advances its Design Software with Agentic AI appeared first on ELE Times.

Murata Manufacturing Co., Ltd. is pleased to announce it will exhibit at CEATEC 2026, taking place from October 13th to October 16th, 2026, at Makuhari Messe, booth No. 7H340.

Advances in AI, robotics, and IoT are changing what is required from sensing technologies, creating new opportunities to detect, interpret, and act on physical conditions, material states, and biological signals with greater accuracy and efficiency.

Addressing these evolving requirements, under CEATEC 2026’s theme ‘Transformation’, Murata will showcase its latest technologies alongside innovative solutions developed in collaboration with partners. Together, the exhibits will highlight new approaches to sensing, monitoring, and detection that can help support a more connected, sustainable, and prosperous society of the future.

Visitors are invited to explore these technologies firsthand, with key exhibits at the Murata booth including:

Ultrasound Transmission Metamaterial

Murata’s ultrasound transmission material enables ultrasonic detection through materials such as metal and resin, which are normally difficult for ultrasound to penetrate. Applied to these obstructing materials, the metamaterial increases ultrasound transmission, extending ultrasonic sensing to applications where internal conditions have traditionally been difficult to assess, including nondestructive testing, flow meters and biomedical applications.

Wearable Device for Capturing Trace Biomarkers

A wearable technology patch under development that captures substances such as glucose and alcohol. Designed for advanced wellness-related applications, its proprietary enzyme-based technology captures target substances　and is being developed with the aim of improving detection in low-concentration ranges.

RFID Sensor Tag

A battery-free, wireless tag capable of sensing temperature, humidity, and strain. The technology enables real-time monitoring of conditions that have traditionally been difficult to assess, including bolt loosening and changes in concrete condition, and is designed to improve inspection and maintenance efficiency.

Neuromorphic Sensing System

Inspired by the information-processing mechanisms of biological nervous systems and sensory organs, this technology enables large-area, high-density sensing with ultra-low power consumption and low latency. Supporting applications like electronic skin for humanoid robots and equipment temperature monitoring, the system provides real-time detection of temperature distributions across a large surface area.

Obstacle detection for AMRs equipped with thermophones

This solution equips an autonomous mobile robot (AMR) with a thermophone, a device that generates ultrasound using heat, to detect nearby obstacles. In current development testing, the technology can measure distances from 1 cm to 2 m and can detect transparent objects such as glass, which can be difficult for optical sensors such as cameras to identify. It can also operate in high-glare environments, supporting obstacle detection for AMR applications.

 

The post Murata to Showcase New Sensing Frontiers at CEATEC 2026 appeared first on ELE Times.

Kelton has redesigned his robot arm, applying three years of experience to improve its mechanical design and accessibility. The new arm keeps the scale-model control scheme, but is easier to print and assemble. All the parts are designed to be 3D printed without supports, which simplifies assembly. The total cost of the required components and hardware is under 100 dollars.

The project is accessible: it costs less than 100 dollars, prints in a couple of days and requires no programming to be used. The video of the original project, published three years ago, has reached 654,000 views on YouTube. This new design grew out of the experience gained with that first version.

How scale-model control works

The robot arm is controlled by a custom input device, which is a scale model of the arm itself. When you move a joint on the model, the corresponding joint on the robot arm moves in the same way. The control is based on an Arduino Nano ESP32 board and a driver board for the servos.

The new wrist joint uses a belt drive. The new gripper uses a rack-and-pinion mechanism. These design choices improve the precision and reliability of the movement. On top of that, printing without supports makes assembly much faster.

• Arduino Nano ESP32
• servo driver board
• servomotors
• drive belt
• rack-and-pinion gear

3D printing and required components

Printing all the parts takes a couple of days. The parts are designed to be printed without supports, so no extra work is needed. This cuts down the time and materials required, making the project suitable even for people with little 3D printing experience.

For the electronics you need an Arduino Nano ESP32 board, which handles the control. Alongside it, a driver board for the servomotors. The servomotors move the joints of the arm. Everything connects to the scale model, which acts as the controller.

Anyone who wants to rebuild the project can find all the files to print on the project page. The page with the files to print collects Kelton’s work and lets you download the necessary parts. The project is meant to be replicated without difficulty, even by someone who has never assembled a robot arm before.

The overall cost stays under 100 dollars, a remarkable figure for a robot arm with these characteristics. The choice of common, easy-to-source components helps keep the price low. What’s more, the modular design allows individual parts to be replaced if they wear out.

A project built to last

The new design comes from three years of experience with the previous version. Kelton listened to feedback from the community and improved the weak points. The result is a sturdier arm that is easier to print and simpler to assemble.

Control through a scale model is intuitive and requires no programming skills. This makes it suitable even for those approaching robotics for the first time. The project shows that excellent results can be achieved with inexpensive components and a lot of care in the design.

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

The post 3D-Printable Robot Arm Controlled by a Scale Model appeared first on Open Electronics.