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When you connect multiple sensors to a microcontroller, sooner or later you run into an I2C address conflict. Two devices using the same address cannot share the bus, and finding a valid combination by hand is tedious. Ztronics has solved the problem with a free, open-source web tool that searches I2C addresses and checks whether multiple devices can coexist on the same bus without conflicts.

The tool is designed for Arduino, ESP32, Raspberry Pi, and other embedded systems. You can search for a device by name, type, manufacturer, or hexadecimal address, then select the ones you want to connect. The tool compares the available addresses of each device and checks whether it is possible to assign a unique address to every component.

The backtracking algorithm and conflict solutions

If a conflict exists, the tool looks for a valid alternative configuration using a backtracking algorithm. This method tries different address combinations until it finds a solution that assigns a unique address to each device. In particular, it works well with sensors that have multiple configurable addresses, such as the MPU6050 which uses 0x68 or 0x69, or the BMA400 which uses 0x14 or 0x15.

When the conflict cannot be resolved, the tool suggests practical solutions. You can change a device’s address, use another I2C bus, switch to software I2C, or add an I2C multiplexer such as the TCA9548A. The I2C bus uses only two communication lines, SDA and SCL, so conflicts are the only real obstacle to connecting many sensors.

• MPU6050: addresses 0x68 or 0x69
• DS3231: address 0x68
• BMA400: addresses 0x14 or 0x15
• SSD1306: addresses 0x3C or 0x3D

The device database and browser-based operation

The entire tool runs in the browser, with no backend, account, or API. The device database is in JSON and the logic is in JavaScript, with HTML5 and CSS3 for the interface. This means you can use it offline once the page is loaded, and your data never leaves your computer.

The Ztronics repository contains all the source code, so you can examine it, modify it, or add new devices to the database. Moreover, the modular structure makes it easy to integrate the tool into other projects or use it as a reference for your own tools. The Ztronics repository collects the complete code and instructions for using it.

For those working with ESP32 or Arduino, this tool eliminates a recurring problem. Instead of manually checking the datasheets of each sensor, you can verify in seconds whether a combination works. Additionally, the suggestions on multiplexers and alternative buses help you design more robust schematics from the start.

Source: https://github.com/webzf/i2c-address-compatibility-checker

The post Find I2C addresses without conflicts with this web tool appeared first on Open Electronics.

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South Korean company INFAC Corporation has introduced an innovative 800V electric vehicle battery pack design that integrates a high-power, isolated and regulated 800V-to-48V DC-DC converter. The new design improves vehicle performance and energy efficiency and extends range thanks to the reduction in high-voltage cables and connectors.

Instead of treating the DC-DC conversion stage as a separate, distributed subsystem, INFAC designed the power to be stepped down at the source for SELV distribution throughout the vehicle. This simplified approach enables cleaner, more flexible and more modular 48V zonal architectures across various EV platforms, without relocating battery cells or reducing vehicle range.

Reimagining what is possible between battery packs and DC-DC converters

INFAC recognised that traditional approaches to power delivery were not aligned with the industry’s goals for weight reduction, cost optimisation and design simplicity.

High-voltage DC-DC converters are typically mounted outside the high-voltage (800V or 400V) battery assembly system (BAS), requiring additional safety and thermal management systems. EV motor and inverter systems require hundreds of kilowatts of power and need high-voltage cabling, dedicated mounts and enclosures. Other powertrain subsystems and body and chassis electronics require 3.5 to 12 kW and can easily be powered from a 48V SELV zonal distribution network.

Figure 1: The Vicor DC-DC converters (BCM and PRM) fit inside the 800V battery pack. This saves space and weight in the vehicle design and takes advantage of the battery’s cooling system to handle thermal challenges. This innovative approach eliminates the cost and weight of an additional cooling system for a remote DC-DC converter. The DC-DC module is compact enough to be integrated without relocating the battery cells. The high-density Vicor BCM6135, combined with the PRM3735 regulator, met the strict space constraints, allowing INFAC to realise the new battery pack design.

Physically separating the high-voltage source and the DC-DC conversion system needlessly duplicates the power distribution and management systems, wasting space, weight and cost and doubling the liquid cooling systems required.

The innovation: moving DC-DC conversion inside the battery pack

INFAC’s key insight was that the battery pack already incorporates a robust liquid cooling system for managing thermal loads. By integrating the high-voltage DC-DC converter inside the battery and leveraging the existing infrastructure, INFAC eliminated the separate cooling system that traditionally sits outside the battery pack for the DC-DC converter. This departs from traditional “silver-box” design approaches, positioning the battery pack as a central, intelligent energy hub rather than a passive energy source.

Figure 2: To be positioned inside the car’s battery pack, the system’s form factor had to be extremely small. The INFAC system measures 215 x 45 x 82 mm (L x W x H). The volume is 793 cm3 and it weighs about 1.5 kg.

The change was achieved using Vicor’s high-density BCM6135 DC-DC converters and PRM3735 regulators, which provide high-voltage-to-48V conversion and regulation and enable a high-power, fully isolated and regulated bus for the zonal architecture. Previously, housing the DC-DC conversion function inside the battery pack was impractical, since dimensional constraints forced designers to deal with power management issues, electrical isolation, safety requirements and the packaging ruggedness of the power modules.

Benefits of DC-DC conversion integrated into the battery

By placing the DC-DC converter inside the battery pack, INFAC achieved a series of benefits that increased system-level performance:

• By leveraging the battery’s liquid cooling network, the power module eliminates redundant cooling circuits and reduces thermal interfaces.
• The length and thickness of high-voltage cables are significantly reduced, simplifying high-voltage wiring routing and layout.
• Fewer brackets, enclosures and cooling components reduce bill-of-materials (BOM) cost and weight.
• Minimising connections and cooling paths reduces leakage points and electrical failure risks.
• Fewer external interfaces translate into faster, more consistent production processes.

Vicor’s high-density modular power architecture delivers the efficiency, scalability and compact form factor needed to make battery-integrated 48V distribution practical on a large scale. In doing so, it positions the battery pack as the central hub for energy management and distribution in next-generation electric vehicles and allows INFAC to align its EV battery pack designs with the industry’s broader transition to 48V zonal power architectures.

The post INFAC integrates Vicor DC-DC conversion into the 800V EV battery pack appeared first on Open Electronics.

An inexpensive robot kit with Arduino UNO Rev3, obstacle-avoidance sensors, and line-following capability becomes a face-tracking robot. The trick is in the control board: just replace the UNO Rev3 with an Arduino UNO Q, which has the same headers and mounts an STM32U585 microcontroller alongside a Linux microprocessor. Iulia Feroli’s project shows how local artificial intelligence can be added to a low-cost robot without touching the mechanics.

The robot starts from the Elegoo kit, with its motor shield and sensors for obstacle avoidance and line following. The UNO Q slots in place of the original board, and the shield moves over without any modification. Thanks to the STM32 microcontroller and the Linux microprocessor, the new board runs machine learning models locally, with no cloud connection. A standard USB webcam is connected to the UNO Q to provide vision.

Video stream and face tracking

The webcam video stream is processed with the face tracking Brick from Arduino App Lab. The code converts the face position in the frame into movement commands for the robot. The robot rotates to center the face and moves toward it, always staying in front of the person. The result is a responsive face tracker that requires no external servers or Wi-Fi connections.

Iulia Feroli’s project is documented in a video showing the robot in action, with an explanation of the assembly and the code. Swapping the board is the core of the intervention: the UNO Q maintains electrical and mechanical compatibility with the UNO Rev3 but adds the computing power needed for AI. In addition, the face tracking Brick in Arduino App Lab simplifies managing the machine learning model, making the code accessible even to those without neural network experience.

What you need to rebuild the project

To replicate the robot you need only a few components, all easily available. The list includes the Elegoo kit, a USB webcam, and the control board. Here are the main steps:

• Remove the Arduino UNO Rev3 from the Elegoo kit and keep the motor shield.
• Mount the Arduino UNO Q in its place, checking that the headers align.
• Connect the USB webcam to the UNO Q port.
• Upload the sketch with the face tracking Brick from Arduino App Lab.
• Power the robot and test it in front of a face.

The UNO Q is the heart of the system: it combines the simplicity of the STM32U585 microcontroller with the power of the Linux processor. This combination allows local machine learning models, such as face tracking, to run without additional hardware. The board is also available in a 4GB version with a full accessory kit, which includes everything needed to get started.

The original Elegoo kit, with its Arduino UNO Rev3 board, remains an excellent base for other projects. However, for this face tracker, the UNO Q is the right choice: it offers the necessary computing power and maintains compatibility with the shield. The overall cost stays low, and the result is a smart robot that impresses with its responsiveness.

Source: https://youtu.be/FIu14vCvGfs?si=8SSW0K6O7J6Y07tz

The post Face-tracking robot with Arduino UNO Q appeared first on Open Electronics.

КПІшники долучилися до освітньо-інноваційного проєкту PolyTECH Project 2026 KPI4U-2 пт, 09/25/2026 - 23:08

Єдність у дії: доступні культурні простори як основа розвитку громад KPI4U-2 пт, 09/25/2026 - 22:44

Перше знайомство нового складу «Формула Студент КПІ» відбулося! KPI4U-2 пт, 09/25/2026 - 18:11

Traditionally, every generation of cellular technology has been mostly about moving data faster. However, 6G is shaping up to be a different kind of upgrade. The industry’s focus is shifting from how fast a network can move data to what new services and experiences it can deliver, with artificial intelligence enabling this shift.

6G is being designed to embed AI deeper into the radio access network (RAN) and core, enabling the network to predict demand peaks before congestion sets in and manage radio resources in real time. That same intelligence could also help the network make use of its own infrastructure and wireless signals to provide data and gather information about the physical environment.

AI already exists in 5G networks, but 6G is designed to integrate these capabilities more deeply into the network architecture. 6G is still being defined through the 3rd Generation Partnership Project (3GPP) standards process, and many of these technologies are in the trial stage. Still, the direction is becoming clear as AI, distributed computing, and sensing are becoming as integral as new frequencies, lower latency, and data rates.

6G standardization & 3GPP

3GPP started to study 6G in Release 19, which was completed in December 2025. Release 20 evaluates foundational items for 6G, including studies of AI, sensing, and new architecture of the 6G network, and it is expected to be completed by 2027. Release 21 is now open for contributions and will be the first normative 6G specifications, pending finalization of its scope and timeline, and is expected to build on top of Release 20 studies and be completed by 2030.

Commercial 6G systems are still expected around 2030, although pre-commercial deployments and field trials will appear earlier. Many of the technologies being considered for 6G are already being introduced through 5G-Advanced, giving vendors and operators a chance to test capabilities such as AI-driven network optimization and integrated sensing and communication (ISAC) before committing to larger 6G deployments.

At the same time, 6G development is not limited to consumer networks. Governments and defense departments are also exploring potential use cases, particularly where communications, computing, sensing, and real-time decision-making need to work together. The U.S. Department of Defense is exploring 6G technologies and prototypes for military applications, adding another potential source of demand for early 6G systems.

The U.S. government’s Mission 6G 28 initiative is another example of this push toward early testing. The initiative is encouraging industry-led demonstrations of technologies, including AI-driven networking and integrated sensing, ahead of the 2028 Los Angeles Olympic Games. These demonstrations use pre-standard technology, providing an early look at which concepts can move from research into real-world environments.

Vendors are also developing tools to support these early trials, giving operators and research and development teams a way to test new capabilities before the standards are finalized.

3GPP’s timeline for Release 21 (Source: 3rd Generation Partnership Project)

The standards process provides the industry with key milestones to watch. Release 20 is expected to conclude in 2027, followed by the development of the normative 6G specification in Release 21 and the final protocol freeze in March 2029. After that, the focus shifts to operators and equipment manufacturers to turn those specifications into reliable systems.

AI-RAN

One of the clearest examples of AI moving into the network is in the RAN, which includes the base stations and antennas that connect devices to the cellular network. Traditionally, RAN equipment has been built around proprietary hardware and custom silicon designed for telco-specific functions with long upgrade cycles. AI is starting to challenge that model, but the industry is not taking one single approach.

Ericsson is taking a more evolutionary approach. The company is embedding AI capabilities into the RAN infrastructure that operators already have, with the goal of improving performance without a costly, large-scale hardware replacement. Early trial results point to real gains in efficiency and throughput, particularly around scheduling and radio resource management.

Nokia and Nvidia are pushing something more transformative. Their AI-RAN uses graphics processing unit (GPU)-based computing alongside traditional telco workloads, turning the RAN into a more flexible compute platform.

Nvidia backed this vision with a $1 billion investment in Nokia, and the two companies already have live trials running with T-Mobile. Putting more GPUs into the RAN brings additional computing capacity but also higher power consumption, cooling requirements, and infrastructure costs. Operators will ultimately have to decide whether the revenue from running AI workloads at the edge is enough to justify those costs.

If operators mostly want better network performance, adding AI to existing RAN infrastructure may be enough. However, the case for GPU-based AI-RAN becomes stronger if operators see an opportunity to turn that additional compute into a new business around distributed AI workloads.

The 6G core

AI is also starting to reshape the network core, the part of the network responsible for routing traffic, managing connections, and allocating resources. In May 2026, 3GPP advanced two competing approaches for integrating AI into the 6G core, with both now being studied in parallel.

The first, Solution Variant #18.1, puts AI directly into the core through new, agent-based network functions. These functions could interpret an intent from a user or application and orchestrate existing network functions to carry it out.

The second, Solution Variant #18.3, keeps AI separate from the core. A dedicated AI domain would interact with existing network functions through a translator function, allowing AI to optimize and orchestrate the network without fundamentally changing the core itself.

Two approaches for integrating AI into the 6G core (Source: ABI Research)

Variant #18.1 is being pushed primarily by Chinese vendors and operators, including Huawei, ZTE, and the major Chinese carriers, while Variant #18.3 has support from Western vendors and operators including Nokia, Ericsson, AT&T, T-Mobile, Qualcomm, and Google. SK Telecom is notably involved in both, reflecting the fact that the industry has not settled on a single path.

For now, 3GPP is keeping both options open. Where AI ends up sitting in the core will shape network architecture and vendor influence for years.

Integrated sensing and communication

As AI makes the network more capable of making decisions and managing itself, sensing can give it more information about the physical environment around it. ISAC uses its existing wireless signals for both communication and sensing, allowing cellular infrastructure to detect and track objects without dedicated sensing equipment.

Recent trials are starting to show what it could look like in practice. For example, in July 2026, AT&T and Ericsson used existing 5G infrastructure outside the AT&T Stadium in Texas to detect, locate, and track multiple drones flying between 300 and 400 feet. The system used existing massive multiple-input/multiple-output radios, signal processing, and AI-enabled sensing rather than a separate radar system.

The demonstration is a useful proof point, but it is not yet a replacement for dedicated radar. It’s more likely a near-term role as an additional layer of sensing, particularly in places where cellular infrastructure is already widely deployed. Drone detection, perimeter security, industrial sites, ports, and logistics facilities are the likely first use cases.

The bigger test will be whether ISAC can move beyond controlled trials and demonstrate performance across different environments, weather conditions, distances, and object types. If ISAC can handle those conditions, it could give AI-driven networks another important input, allowing them to make decisions based on what is happening in the physical environment and inside the network.

What to watch

The next few years will show whether 6G can deliver on the larger idea that it is less about a faster connection and more about the network becoming an intelligence platform. The clearest checkpoints are on the standards calendar: Release 20 studies conclude in 2027, the architecture is due to be finalized in 2028, and the first normative 6G specs freeze in March 2029. This will determine the technical standards, but what operators do in practice comes down to whether AI, distributed computing, and sensing can create enough value to justify the cost and complexity of putting them deeper into the network.

This is what truly differentiates 6G from previous generations. The network is becoming part of the computing infrastructure and can process AI workloads closer to the source, make decisions about how network resources are used, and gather information about the physical environment. That moves 6G beyond the traditional role of connecting devices and toward a more intelligent network.

This is a follow-up to ABI Research’s 5G & 6G: Adoption, Technologies and Use Cases.

The post 5G to 6G: AI moves into the network appeared first on EDN.

A Steam Controller, Valve’s gamepad, now runs Zork, the famous text adventure from 1980. Owen Feldman has written an Intel 8080 emulator in Rust that runs directly on the controller’s microcontroller. The emulator boots CP/M, the operating system of that era, and the Z-machine, the virtual machine that interprets Infocom games. The result is a fully playable game, with the computer connected via USB acting as a terminal.

The Arm Cortex-M4 and the 8080 emulator

The heart of the project is the Steam Controller’s main microcontroller, an Arm Cortex-M4. According to Feldman, this chip is orders of magnitude more powerful than the entire Apple IIc, the computer on which many people played Zork in the 1980s. The Intel 8080 emulator was written from scratch in Rust, a language known for safety and performance. The emulator, together with the Z-machine and Zork, is installed on the controller using the firmware update tool included in the Steam package for Linux.

The game needs no graphical resources: Zork is text only. However, the controller has neither a screen nor a keyboard. Feldman therefore wrote a small server application that runs on the connected computer. This application displays the game text and sends commands to the engine over USB. In practice, the PC becomes a serial terminal, while the gamepad does all the computational work.

The failure and repair with a Raspberry Pi Pico

During the process, Feldman damaged his Steam Controller. The original firmware was compromised and the gamepad no longer responded. To repair it, he used a Raspberry Pi Pico soldered to the debug pins on the board. With the Pico he restored the original firmware, bringing the controller back to life. This step shows how useful it is to have a spare microcontroller for low-level debugging.

The project is documented in Owen Feldman’s video, where he shows the development process and the repair. It is a perfect example of how modern consumer hardware can be pushed beyond its limits. A gaming pad, designed for input and vibration, becomes a complete computer capable of running an operating system and a retro gaming classic.

• The gamepad’s Arm Cortex-M4 microcontroller runs the Intel 8080 emulator.
• CP/M and the Z-machine run inside the emulator, with no external hardware.
• The PC connected via USB acts as a terminal for text and commands.
• A Raspberry Pi Pico made it possible to restore the firmware after a failure.

For those who want to replicate the project, you need a Steam Controller, a computer with Steam for Linux, and a bit of patience. The most delicate part is flashing the firmware: a mistake can render the gamepad unusable, as happened to Feldman. Having a Raspberry Pi Pico available for debugging is a smart precaution. In addition, those who want to experiment with microcontrollers may find the Pico Primer kit with sensors and tutorials useful, although for this project only the bare Pico is needed.

Source: https://youtu.be/M5XUR8Fnjf4?si=IhDVd-LKypGjz5rw

The post Zork on a Steam Controller: when the gamepad becomes a mainframe appeared first on Open Electronics.

You don’t have to be Superman (or even just use phone booths as changing stations) to find this author’s recent research results disturbing.

I was recently looking for some information about neon lamps when I came across a series of websites with content which, courtesy of my blissful naïveté, I found rather jarring. I merged a collection of screen shots into one image for your ease of perusal (Figure 1).

Figure 1 Warning: this collection of screen shots may be angst-inducing.

Looking further into the “radioactive additive” matter, I found a paper about that Krypton stuff (PDF). And looking further yet, I came across two more screen shots, which IMHO are very much deserving of your attention (Figures 2 and 3).

Figure 2 This screenshot’s information covers Krypton 85 itself.

Figure 3 And this screenshot discusses Kr-85’s uses and cancer risks.

Having been jolted into newfound awareness of the presence of radioactive material where I would never have expected it (due, again, to my own naïveté, of course.), I am reminded of how exposure to lead has been recently recognized as having no minimum exposure level that can be considered safe for anyone. Leaded gasoline, for example, is now out of use.

Mercury exposure is another example. And X-ray exposure effects are of similar concern, albeit minimally accepted only as necessary for medical diagnostics purposes.

My sense is that the same “no minimum acceptable exposure” rule should also apply to radiation exposure from electronics components, with regard to the possibilities of “cancer induction”. And right now, for lack of such, one should handle Krypton 85-bearing items with some care and, if accidentally broken, with extreme care. Although what precise steps would be called for, I do not know.

John Dunn is an electronics consultant and a graduate of The Polytechnic Institute of Brooklyn (BSEE) and of New York University (MSEE).

Related Content

• Radon sensor embeds sizeable alpha particle detector
• Radon: Level detection, risk determination, and as-needed mitigation
• My Geiger counter doesn’t count (sob sob)

The post Neon lamps and Krypton 85 appeared first on EDN.

📰 Газета "Київський політехнік" № 31-32 за 2026 (.pdf) Інформація КП пт, 09/25/2026 - 14:54

Renesas Electronics Corporation on 24th September, 2026, announced the SLG46801, the industry’s smallest GreenPAK configurable mixed-signal device, available in both WLCSP and STQFN packages. The 9-ball WLCSP measures just 1.155 mm × 1.155 mm and combines an ultra-small footprint with multi-time programmability, helping designers reduce PCB space in compact products such as smart rings, smartwatches, fitness bands, VR glasses and portable electronics.

Consumers are demanding smaller, more feature-rich products, while designers need to add functionality while reducing component count, PCB area and bill-of-materials (BoM) costs. The SLG46801 addresses these requirements by combining commonly used analog, timing and logic functions in a compact, low-power device. It can complement an MCU or replace multiple discrete components, helping designers reduce board space and simplify designs.

“Designers are being asked to integrate more functionality into increasingly compact products without increasing system cost or development effort, while also reducing power consumption.” said Jason Kim, Vice President and General Manager of the Core Analog Division at Renesas. “The SLG46801 expands our GreenPAK portfolio with an ultra-compact WLCSP option and flexible in-system configurability, helping customers create smaller, more feature-rich products and implement updates even after deployment.”

The SLG46801 integrates two high-speed analog comparators, configurable lookup tables, counters and delays, 10 kHz and 25 MHz oscillators, voltage-tolerant GPIOs and an I²C-compatible serial interface. These resources support sensing, control, glue logic, timing and system housekeeping functions in applications across consumer electronics, handheld devices, smart-home systems, networking and communications, computing and storage, industrial control and IoT sensor nodes.

In addition to the 9-ball WLCSP, the SLG46801 is available in a 12-lead, 1.6 mm × 1.6 mm × 0.55 mm STQFN package. The two package options allow customers to balance minimum board area with additional GPIO availability and industrial package preferences. The device also offers one of the lowest cost per GPIO in the GreenPAK family.

Renesas is a leader in configurable mixed-signal technology, with more than four billion GreenPAK devices shipped worldwide. GreenPAK ICs enable designers to combine analog and digital system functions in small, low-power devices that can be configured using the Renesas Go Configure Software Hub. The free GUI-based development environment helps customers develop custom hardware functions without the need for additional discrete components or complex firmware development.

The SLG46801 includes multi-time programmable non-volatile memory that can be configured in system through its I²C interface. This allows customers to implement bug fixes, configuration changes and product upgrades after the device has been integrated into the end application, helping reduce redesign cycles and additional hardware revisions.

The device supports operation and programming across the full 1.71 V to 5.5 V supply range—the widest operating and configurable range among MTP GreenPAK devices. GPIO pins can also be repurposed dynamically for the I²C interface, maximizing flexibility in designs with limited pin availability. Optional CRC-8 and read-back protection support more robust and controlled configurable implementations.

The post Renesas Introduces Smallest GreenPAK Configurable Mixed-Signal IC with Multi-Time Programmability appeared first on ELE Times.

Cadence on September 24, 2026, announced a new agent for the Cadence ChipStack AI Super Agent that automates front-end digital design and verification, covering power, performance and area (PPA)-driven spec-to-RTL generation, RTL analysis and refinement through natural language prompts. Building on the industry’s first agentic workflow for front-end design and verification announced in February 2026, the RTL Generation Agent extends the ChipStack AI Super Agent from autonomous verification and debug to high-quality RTL creation and optimization. In early evaluations, the RTL Generation Agent delivered an average 24% reduction in area and 18% reduction in power compared with pure foundation model code generation, while ensuring 100% functionally accurate RTL, according to Cadence.

Building on the industry’s first agentic workflow for front end design and verification announced in February 2026, this RTL Generation Agent extends the ChipStack AI Super Agent from autonomous verification and debug into high quality RTL creation and optimization.

“These latest agentic AI advancements take us from AI assisted tools to coordinated agentic workflows that behave more like virtual design engineers with expert-level command of the underlying technologies,” said Chin-Chi Teng, senior vice president and general manager in the Digital & Signoff Group at Cadence. “By pairing agentic automation of spec-to-RTL and RTL refinement with our proven implementation and signoff engines, we enable customers to achieve better design outcomes with higher productivity and stronger correlation across the design flow, further extending Cadence’s leadership in AI driven, end to end chip design.”

Cadence’s transformational approach to applying agentic AI to engineering design is founded on a hierarchy of solutions—super agents orchestrate task-specific agents, which in turn use trusted electronic design automation (EDA) software, optimized for agentic workflows. The new RTL Generation Agent converts high level specification into production ready RTL optimized for PPA.

Customer Validation from Honda

Early collaborations with Honda R&D demonstrate how these agentic AI capabilities translate into real world PPA and productivity gains on next generation SoCs.

Honda is evaluating the RTL Generation Agent on advanced automotive SoCs, where safety critical requirements and tight power and cost envelopes demand highly optimized RTL.

“As a key enabler of Software-Defined Vehicles (SDVs), AI technology for autonomous driving is advancing rapidly. However, the long development cycle of SoCs remains a major challenge. With the Cadence ChipStack AI Super Agent’s RTL Generation Agent and AI-powered automation, Honda R&D is working to improve productivity from specification through RTL development,” said Tomoya Nishino, chief engineer and general manager, Digital Engine Development Division, SDV R&D Center, Honda R&D Co., Ltd.

Smarter RTL Updates and Early PPA Insight

In addition to new RTL creation within the RTL Generation Agent, Cadence is introducing technology for design updates to existing RTL based on new requirements. This RTL upgrade flow brings AI automation to accelerate RTL revision, enabling customers to rapidly adapt legacy RTL to new architecture requirements, new PPA targets and new functional requirements. Engineers describe changes at a high level, and the agents carry out the updates while analyzing and verifying PPA and functionality.

Advancing Cadence’s Agentic AI Vision

These enhancements build on Cadence’s “Design for AI and AI for Design” strategy highlighted at CadenceLIVE and Computex, further extending the company’s leadership in AI driven chip design. From the initial ChipStack AI Super Agent launch through June’s announcement of the industry’s first fully autonomous virtual engineer for chip design, and now today’s RTL Generation Agent, Cadence continues to expand the scope of agentic workflows across the design stack. Together with the broader ChipStack, InnoStack and ViraStack AI Super Agent portfolio, they advance a scalable platform that applies AI across digital, analog and verification domains.

The post Cadence Expands ChipStack AI Super Agent with AI-Powered RTL Generation and PPA Optimization appeared first on ELE Times.

Let’s find out how to use the MAX7219 to drive an LED matrix. Wiring, Arduino libraries and sketches will guide us through building custom graphic and numeric displays.

An LED matrix display is one of the most fascinating components for anyone starting to experiment with Arduino: with a small square module we can bring to life numbers, letters, symbols and animations that immediately catch the eye. The problem? An 8×8 matrix means no fewer than 64 LEDs to control individually: an almost impossible task without dedicated support. That is where the MAX7219 chip comes in, an IC designed specifically to simplify the management of matrix and 7-segment displays, reducing the connection to the Arduino board to just a few pins. In this article we will see how to connect the MAX7219 to an 8×8 matrix, how to use the ready-made modules available on the market and how to program everything with the LedControl library.

The MAX7219

The MAX7219 chip has 24 pins. The 8×8 display (Fig. 1) is connected so that the rows are linked to the DIG pins and the columns to the SEG pins of the MAX7219.

Brightness is varied in software after setting the maximum current with an external resistor connected to the Iset pin. Three pins are dedicated to communication with the control board: DIN (to transfer data from the board to the chip), CS (for device selection) and CLK (for the data clock). A further pin, DOUT, is used to connect the DIN of the next chip, in case you want to chain several LED matrices together (for example to build scrolling text).

The complete schematic of the connection between the chip and the display is shown in Fig. 2, with particular emphasis on the links to the Arduino board and to a second chip. Using the MAX7219 is made even simpler by the availability on the market of modules (such as those in Fig. 3) that integrate both the chip and the LED matrix, along with the relevant wiring.

Using a module reduces the wiring to just the connections between the module and the Arduino board, and between modules possibly arranged in a chain. Each module has 5 input pins (VCC, GND, DIN, CS/LOAD and CLK, to be connected to a board such as the Arduino UNO R3) and 5 output pins (VCC, GND, DOUT, CS and CLK, for any subsequent modules in the chain). The Arduino UNO R3 board can be replaced by the more recent Arduino UNO R4 versions, available in the Minima and WIFI models, both fully compatible electrically and in software with the previous R3. Both versions keep the same pin layout and are compatible with most shields and libraries already developed for the R3.

Programming with Arduino

Several libraries make programming the MAX7219 easier; among them, in particular, the LedControl library, which is very widespread and simple to use.

The basic commands are as follows:

#include “LedControl.h” LedControl LC=LedControl (DIN, CLK, CS, number_of_modules)

An object of the LedControl class is created, to which an identifying name is assigned (for example, LC). The DIN, CLK and CS parameters will be replaced with the numbers of the Arduino pins (for example: 2, 4, 3) to which the respective signals are connected.

LC.SHUT (module_number, 0/1)

Enables or disables the chip. The value 0 makes it operational, while 1 puts it in standby. On power-up, the chip is in standby mode by default. The module_number parameter identifies the module in a serial chain, numbered starting from 0.

lc.setIntensity (module_number, intensity)

Adjusts the brightness of the LEDs, with a value between 0 (minimum) and 15 (maximum). The value 0 does not turn the LEDs completely off; to do that, you need to use LC.Shutdown(module_number, 1).

lc.clearDisplay (module_number)

Turns off all the LEDs of the specified module, clearing the displayed content.

lc.setLed (module_number, row_number, column_number, state)

Turns a single LED on or off. row_number and column_number indicate the position of the LED (numbered from 0 to 7). state = true (or 1) turns the LED on, false (or 0) turns it off. Rows are numbered from 0 (top) to 7 (bottom), columns from 0 (left) to 7 (right). For example, the top-left LED occupies position (0, 0), the bottom-right one (7, 7).

lc.setRow (module_number, row_number, byte)

Lets you turn all the LEDs of a row on or off, specifying their state with a binary byte. For example, to turn on the first four LEDs of a row you use: B11110000.

lc.setColumn (module_number, column_number, byte)

Works in a similar way to setRow, but acts on a column. The byte defines which LEDs to turn on or off in the specified column. Let’s now look at some practical examples of use.

Example 1

The first sketch, shown in Listing 1, is meant to display the 8 rows in sequence, one at a time, starting from the top; then the 8 columns, one at a time, starting from the left; then the 8 rows starting from the bottom; then the 8 columns starting from the right. Finally, all the LEDs are turned on gradually in pairs of rows, starting from the two middle rows and following the order: 4-5, 3-6, 2-7, 1-8. In this last phase, the lighting happens at low intensity, with brightness varying progressively from 10 to 2.

Example 2

The purpose of this sketch (Listing 2) is to gradually light up the LEDs of the matrix rows starting from the bottom, as a consequence of a voltage varying between 0 V and 5 V set by a potentiometer connected to pin A5; the comments inside the sketch describe how the gradual lighting works.

Using arrays

The goals of the two previous examples can be achieved in a similar way using arrays. One approach is to define the row structure inside an array of bytes (for example in binary): with 8 elements of 8 bits you describe the LED states of the whole display. The first cell of the array corresponds to the state of the eight LEDs of row 0 (from left to right), the second to that of row 1, and so on up to row 7.

If, for example, you want to turn off all the LEDs of the first four rows and turn on those of the last four, you can use the following array:

byte array [8]={B00000000,B00000000,B00000000,B00000000,B11111111,B11111111,B11111111,B11111111};

To display it on the screen you can proceed using the following code:

for (row=0; row<8; row++) {lc.setRow(0,row,array[row]);}delay (3000);

An editor that lets you obtain the binary codes of the most commonly used symbols more quickly is available at the following link: https://xantorohara.github.io/led-matrix-editor

Example 3

The purpose of this sketch (Listing 3) is to display in sequence all the numbers between 0 and 9 using 10 arrays that define the numbers and 10 for loops that call them up.

Example 4

The purpose of this sketch (Listing 4) is to show when certain temperature and humidity thresholds (set in the program) are exceeded, as shown in Fig. 4. In the left half of the display (columns 0-1-2) the temperature data is shown, while in the right half (columns 5-6-7) the humidity data is shown. In the upper part of the display (rows 0-1-2) the letters T and U appear; in the lower part (rows 4-5-6-7) the lighting of the LEDs indicates that a given temperature or humidity threshold has been reached.

Four thresholds are defined in the code: as the value increases, the corresponding rows light up progressively, starting from row 7. Temperature and humidity are measured with the HTS221 sensor, integrated into the STMicroelectronics IKS01A3 expansion board (Fig. 5), mounted on the Arduino UNO board. Those who do not have this board can use other sensors, such as the DHT11 or DHT22, adapting the data acquisition part of the software accordingly.

Conclusion

The MAX7219 makes LED matrix management accessible to everyone, turning a complex task into a fun, stimulating and creative experience. Once you understand the basic commands, the possibilities become practically endless: custom scrolling text, small animations, graphic indicators, simple light games and real-time data visualisations. All that is left is to experiment, adapt the sketches provided and let yourself be inspired: with a simple LED matrix your Arduino project can finally “speak with light” in a clear, dynamic and original way.

Related products

• 4 x 8&#215;8 LED Matrix Display Module with MAX7219 – Green LEDs

The post MAX7219 LED Matrix with Arduino: Wiring and Library Guide appeared first on Open Electronics.

👍 Всеукраїнський день бібліотек у Бібліотеці КПІ! kpi пт, 09/25/2026 - 12:09

Optical image stabilization (OIS) has become one of the most high-valued features in the smartphone business. Over the past decade, the content we consume shifted from primarily text-based to image-driven before video content—long and short, horizontal then vertical—began to dominate what average joes, influencers, and businesses alike are creating.

Let’s take a quick look at the marketing industry, a major force in video-driven social platforms and websites. Here, 89% of businesses use video marketing and 56% of brands are focusing on user-generated content. From day jobs to side hustles, quality video is no longer a nice-to-have, it’s a necessity.

Crisp, blur-free photos and videos are a must, so it’s no surprise that Tech Radar suggests the camera is the most important consideration for people when buying a new phone.

Till now, OIS has only been available in high-end mobile devices because it’s costly to implement. But as consumer demand for high-quality mobile photography and videography continues to surge, OIS is becoming a must-have feature in a variety of devices at different price points.

Finding focus: EIS and OIS

Electronic image stabilization (EIS) is currently more common in mid-range and flagship devices. EIS compensates for hand jitters that cause shaky videos and is a very effective technology.

Via a high-performance motion sensor, EIS tracks the source of the handshake or vehicle motion and integrates that information during the current video frame by cropping the viewable image from a stream of video frames through the imaging pipeline. This creates a unique frame sync input that allows precise alignment with video frames, essentially synchronizing the two pipelines in the system.

However, instead of relying on an electronic sensor, OIS systems bring a hardware solution to shaky images and video. Here, OIS systems rely on inertial measurement units (IMUs) to track motion. Devices within the IMU sense the motion and send that data to a microcontroller that moves the camera’s lens or sensor in real-time to physically counteract unwanted movement.

Figure 1 Optical image stabilization (OIS) brings a hardware solution to shaky images and video. Source: TDK InvenSense

While EIS has historically been less costly, it falls short of the superior image quality of OIS and can experience image degradation. As a result, OIS has been restricted to higher-end smartphones so far for reasons of cost, size, and energy usage. However, that’s set to change, thanks to IMU advancement.

Better IMUs mean more cost-effective OIS

IMUs track motion successfully by incorporating micro electrical mechanical systems (MEMS) sensors. These compact, high-precision devices integrate both mechanical and electronic components into a single silicon chip. IMUs combine MEMS accelerometers, gyroscopes, and compasses with algorithms and firmware that intelligently process, synthesize, and calibrate sensor output to maximize performance and accuracy.

MEMS sensors in IMUs offer some distinct advantages:

• They are easily integrated due to being small and lightweight.
• They are highly robust and durable as they have fewer mechanical components exposed to physical degradation.
• They deliver superior sensitivity and accuracy.
• They are produced using semiconductor fabrication techniques, allowing them to be manufactured in high volumes at a lower cost.
• Lastly, they consume significantly less power than many conventional sensor technologies.

Keeping images crisp on even more devices

While OIS offers better overall image quality and IMUs help OIS systems function better, the challenge is how to make the combination of both technologies more easily accessible. TDK InvenSense, backed by 15 years developing OIS/EIS custom sensing solutions for select smartphone and camera OEMs, claims to have an IMU that lets manufacturers more affordably bring advanced OIS into many more devices.

The ICM-536xx IMU supports up to 6.4 kHz ODR and up to 20-bit data resolution for optimal image quality. The ICM-536xx family, featuring 6-axis IMUs, comes in a 2.5 × 3 × 0.91 mm package that contains multiple motion sensors: a 3‑axis gyroscope and a 3‑axis accelerometer on the same silicon die.

Figure 2 The ICM-536xx IMU offers a thinner, power-efficient, and mainstream solution without compromising on performance: Source: TDK InvenSense

The IMU also features pin-for-pin drop-in compatibility, so IMUs can be easily added into existing mobile device designs. Moreover, design engineers can offload sensor management from the operating system of a mobile device when combining these IMUs with the InvenSense MotionApps Platform. This on-chip sensor fusion software ensures motion‑based complexities are abstracted using a structured set of APIs for application development.

While smartphones, tablets, and motion cameras are great initial uses for the ICM-536xx family, their 6-axis abilities at a lower cost and energy consumption make them suitable for smart glasses, augmented reality (AR) glasses, surveillance cameras, advanced driver assistance systems (ADAS), self-driving vehicles, and drones.

Last year, mobile device users may have viewed OIS as a bonus. But being able to capture a perfect picture is becoming a key differentiator for today’s smartphone. That’s why OIS is set to become an essential capability.

If your business wants to improve the performance of products with a camera system, especially those that experience movement, shock, and vibration, adding OIS can not only improve the device but also improve the value that users perceive. It’s an opportunity to improve user experience while building trust with a user base set to produce even more video in the coming years.

Song Li is director of product marketing at TDK InvenSense.

Related Content

• Optical Image Stabilization
• Triple Cameras: Are Three Better Than Two?
• Security video camera design for all lighting conditions, 24/7
• Digital camera design: Determine pixel size, focal ratio, and field of view
• Digital camera design: Motion considerations for frame rate, exposure time, and shuttering

The post More cost-effective OIS brings better photo/video quality to mobile devices appeared first on EDN.

Physical AI needs a board that can think and act at the same time. This project pairs a Qualcomm Dragonwing IQ8 processor with an STM32H5 in a dual-brain architecture. The first runs AI models with 40 TOPS, while the second controls motors and peripherals in real time. The result is a complete platform for robotics and automation.

The board carries 16 GB of LPDDR5 RAM and 64 GB of eMMC storage. Connectivity includes tri-band Wi-Fi 6, Bluetooth 5.3, 2.5 Gb Ethernet, and CAN-FD. The board with integrated display from the Arduino UNO Q family offers a similar starting point for anyone approaching this world. The project board is open source and free of proprietary lock-ins.

How the dual-brain architecture works

The Qualcomm Dragonwing IQ8 processor handles the artificial intelligence. The STM32H5, on the other hand, guarantees deterministic control over motors, CAN bus, and other peripherals. The two processors communicate efficiently, so the AI can make decisions and the hardware executes without unpredictable latencies.

The preinstalled operating system is Ubuntu with an Ubuntu Pro license. The Arduino core runs on Zephyr RTOS, which offers guaranteed response times. In addition, the environment supports VS Code, PyCharm, Jupyter, and Docker for development.

AI models optimized for the NPU run through Arduino App Lab. The platform supports importing GGUF models from Hugging Face and training with Edge Impulse Studio. There are over 100 ready-to-use examples.

• 40 TOPS of AI power
• 16 GB LPDDR5 RAM
• 64 GB eMMC
• Tri-band Wi-Fi 6 (2.4/5/6 GHz)
• Bluetooth 5.3
• 2.5 Gb Ethernet
• CAN-FD

Why a board for Physical AI is needed

Modern robotics requires perception, decision, and action in a single device. This board unifies everything in an open format. Makers can prototype with Arduino UNO shields and Raspberry Pi HATs. Moreover, ROS 2 support and the CAN-FD, I2C/I3C, SPI, PWM, and UART interfaces make it suitable for professional projects.

Compatibility with existing shields lets you reuse sensors and actuators you already own. For example, those with the more powerful processor board from the Raspberry Pi family can compare performance. In addition, the Works with Arduino program allows scaling prototypes to production level with certified SOMs from SECO and Toradex.

An open ecosystem for physical AI

The board uses Ubuntu Pro as its main operating system. Zephyr RTOS handles real-time hardware control. This mix ensures flexibility for development and robustness for execution.

Arduino App Lab is the access point for AI models. It supports importing from Hugging Face and training with Edge Impulse. There are also over 100 ready-made examples to get started right away.

The board is powered by a 65W USB-C power supply. It is designed for those who want to move from prototyping to production without changing platforms. Finally, support for Arduino shields and Raspberry Pi HATs makes it versatile.

For those starting out with embedded AI, the board with the STM32 microcontroller offers a simpler alternative. However, this board represents the next step for advanced robotics projects. Physical AI thus becomes accessible to makers, educators, and professionals.

Source: https://www.qualcomm.com/internet-of-things/products/iq8-series

The post Arduino UNO Q 4GB: A Dual-Brain Board for Physical AI appeared first on Open Electronics.

From discovering new component vendors and evaluating production equipment to addressing gaps in domestic manufacturing, electronica India and productronica India 2026 in Bengaluru were marked by three days of intensive commercial and technical engagement across the electronics value chain.

Both trade fair was organised by Messe Muenchen India from 16–18 September 2026 at the Bangalore International Exhibition Centre (BIEC). The co-located trade fairs welcomed 793 exhibitors and 52,411 business visitors. The exhibition occupied 60,000 square metres and featured exhibiting companies from over 30 countries. Together, visitors, exhibitors, speakers and industry delegations represented more than 50 countries.

The event covered electronic components, embedded technologies, printed circuit boards, electronic manufacturing services, production machinery, automation, testing, inspection and related manufacturing solutions. Its breadth allowed decision makers to examine several stages of electronics production within a single business platform.

The Bengaluru edition was inaugurated by Shri M. B. Patil, Hon’ble Minister for Large and Medium Industries and Infrastructure Development, Government of Karnataka, and Mr. Hiroshi Nawata, Consul-General of Japan in Bengaluru.

Industry attention shifts from capacity to capability

The conversations across the exhibition indicated a clear industry priority: increasing manufacturing capacity must now be supported by stronger capabilities in components, equipment, quality, testing, design and production processes.

Manufacturers attending the event evaluated technologies against practical requirements such as application suitability, production output, process control, inspection accuracy, automation and supplier support. Component and equipment providers, in turn, gained direct exposure to the requirements emerging from Indian electronics manufacturers.

Bhupinder Singh, President – IMEA, Messe München, and CEO, Messe Muenchen India, said, “The value of a trade fair is ultimately determined by the quality of the business conversations it enables. In Bengaluru, we saw manufacturers arriving with specific sourcing, production and technology requirements. Suppliers were able to respond with relevant capabilities and technical expertise. This level of engagement shows that India’s electronics sector is progressing from broad expansion plans towards more defined manufacturing decisions.”

The Buyer–Seller Forum supported this process through 3,225 focused B2B meetings over three days with 645 unique buyer companies and 1,682 VIP buyers. The discussions gave both sides a structured setting to examine technical requirements, supplier capabilities and potential areas of cooperation.

The exhibition floor was complemented by the Podcast Studio, Executive Club Lounge and dedicated networking areas, creating additional settings for conversations among business leaders, technology specialists and industry representatives.

International participation reflects demand for closer market engagement

Companies from Germany, China, France, Italy, the United Kingdom, Japan, Israel, the United States, Korea and other markets participated in the Bengaluru edition.

Dedicated pavilions from Germany, Japan, Taiwan and China brought together technologies and suppliers from their respective electronics industries. The Japan Pavilion was organised in partnership with the Japan External Trade Organization (JETRO), while the Taiwan Pavilion was presented with the Taiwan Electrical and Electronic Manufacturers’ Association (TEEMA).

Rather than serving only as international showcases, the pavilions enabled participating companies to hold direct discussions with Indian buyers, understand local manufacturing requirements and explore potential commercial relationships.

Dr. Reinhard Pfeiffer, CEO, Messe München GmbH, said, “Global electronics companies are increasingly in need of a closer understanding of India’s manufacturing requirements and business environment. The Bengaluru edition gave international suppliers direct access to the companies investing in production, sourcing and technology adoption. For Indian participants, it provided an efficient way to compare the capabilities of several established technology markets.”

Conference discussions examine the foundations of competitive manufacturing

The accompanying programme moved the industry conversation beyond manufacturing volume to the capabilities required for long-term competitiveness.

The India Electronics Conclave comprised 14 conferences at the BIEC Conference Centre. Across the event, approximately 150 speakers participated in 18 supporting programmes covering electronics policy, capital goods, artificial intelligence, exports, free trade agreements, printed circuit boards, flexible electronics, standards, compliance and power electronics.

The programme included the eFuture Conference with Avanteum as Knowledge Partner, CEO Forum with ELCINA, Industrial Electronics & Capital Goods Summit 2026 with ICEA, Exports and FTAs: Electronics Sector with MEDEPC, Bharat PCB Tech Conference with ELCINA, VDMA Symposium and OE-A Symposium on Flexible Electronics.

Reinforcing its role as a platform for industry dialogue and future-focused thinking, electronica India and productronica India 2026 hosted the launch of MMI and Avanteum Advisors’ e-paper, eFuture 2035: Engineering India’s Next Electronics Revolution. The report outlines the strategic shifts and opportunities that could shape India’s electronics manufacturing and its position in the global value chain over the next decade.

Technical learning was addressed through the IEEE Standards Workshop on EMI/EMC Compliance and the Power Electronics & Power Supply Design Workshop.

“India’s electronics industry is entering a phase where competitiveness will be measured by consistency, reliability, process maturity and the ability to meet global customer expectations. Forums at electronica India and productronica India 2026 helped bring sharper attention to these priorities by connecting industry leaders, technology providers and policy stakeholders around the practical capabilities required to move from capacity creation to globally competitive manufacturing,” said Rajoo Goel, Secretary General, ELCINA.

The Industrial Electronics & Capital Goods Summit 2026, organised with the India Cellular & Electronics Association, examined the role of domestically available equipment and production technologies in supporting manufacturing growth.

Shri Pankaj Mahindroo, Chairman, India Cellular & Electronics Association (ICEA), said: “As India’s electronics manufacturing ecosystem continues to expand, reliable access to advanced equipment, automation, testing and quality-control technologies will be critical to sustaining growth. The summit provided an important platform for industry and policymakers to examine these requirements from the perspective of real-world production needs and identify the areas where India’s capital-goods and technology ecosystem must continue to evolve.”

The OE-A Symposium brought international and Indian experts together to consider the commercial potential of flexible and printed electronics across mobility, healthcare, energy, consumer products and industrial applications.

Sandip Roy, General Manager, VDMA India, said, “The opportunity in flexible electronics depends on connecting research and  technology development with viable industrial applications. The symposium created a useful exchange between international specialists and Indian stakeholders exploring how these technologies can move towards adoption and commercial deployment.”

Speakers participating in the supporting programme said that the value of the discussions lay in bringing policy, manufacturing experience and technical expertise into the same forum.

“The session provided an opportunity to examine the emerging power paradigm in the context of the decisions manufacturers are making today. The discussion highlighted the immediate challenges facing the industry, while also bringing into focus the technologies and capabilities that need to be developed to meet the evolving demands of power electronics over the coming years,” said Amit Kumar, Vice President & Business Unit Head – Metering and Protection Systems, Schneider Electric India.

“India has a significant opportunity to strengthen its position in next-generation PCB design to manufacturing, but realising this potential will require closer collaboration among industry, government, academia and technology providers. The event brought these stakeholders together for a practical and meaningful exchange of perspectives on bridging the journey from design to manufacturability,” said Savita Ganjigatti, Sr. Vice President – Engineering & Operations, Sienna ECAD Technologies (An Avalon Group Company), during the session Engineering for Tomorrow: Design to Manufacturing.

Emerging engineers work on industry-defined challenges

The 2026 edition also introduced formats designed to connect emerging technical talent with practical industry problems.

Organised with HackCulture and supported by presenting partner DigiKey, the electronica India Tech Challenge featured a prize pool of ₹5 lakh. Participating teams developed responses to defined technology challenges and presented their work during the Hackathon Demo Day, where the three winning teams were recognised for the originality, technical merit and practical relevance of their solutions.

The AI Buildathon, conducted by Sarvam AI, and AI Masterclass conducted by GrowthX, explored the application of artificial intelligence to practical use cases. A Hand Soldering Skill Test brought attention to production skills at the operator level, while an industrial visit to the Central Manufacturing Technology Institute provided exposure to an established manufacturing and engineering environment.

At electronica India 2026, the Taipei Computer Association and IIIT-Bangalore signed an LoI to advance Taiwan–India collaboration across AI, electronics, smart manufacturing, smart cities and cybersecurity.

Together, these initiatives connected the event’s wider manufacturing agenda with engineering skills, applied problem-solving and future workforce requirements.

‘India’s Powerplay in Electronics’ reaches the industry nationwide

The Bengaluru edition was presented under the theme “India’s Powerplay in Electronics.” Cricket icon KL Rahul served as Brand Ambassador for the visitor campaign, which ran across outdoor, digital and print media through September 2026. At the event, the campaign shifted from a public-facing message to a physical demonstration of the breadth of companies, technologies and expertise in India’s electronics industry.

Exhibitors and buyers report focused business engagement

Exhibitors highlighted the quality of conversations with manufacturers, sourcing teams and technology decision-makers attending the Bengaluru edition.

“productronica India 2026 gave us a strong platform to showcase advanced technologies with our global partners. Unveiling Fuji’s CLT-FG for the first time outside Japan was the main highlight. The strong industry response reinforced the growing demand for advanced manufacturing solutions in India and opened new opportunities for collaboration,” said Soni Saran Singh, Founder, MD & CEO, NMTronics India Pvt. Ltd.

“India’s electronics ecosystem is evolving rapidly, with growing focus on technology adoption, local manufacturing and stronger supply chains. The exhibition enabled meaningful conversations around these priorities and how we can support customers across the electronics value chain, from design to delivery,” said Haresh Abichandani, Managing Director, Millennium Semiconductors.

“Visitors came with defined requirements and a clear understanding of the capabilities they wanted to evaluate. This allowed our discussions to move quickly from general enquiries to applications, technical specifications and potential projects,” said Nandini Balasubramanian, Director, Tescom Pvt. Ltd.

Buyers valued the opportunity to compare suppliers and technologies against specific production and sourcing requirements.

“We attended the exhibition with clearly defined requirements for passive components used in electronic control boards. The event provided an excellent opportunity to meet multiple relevant suppliers under one roof, enabling us to compare their technical capabilities, product offerings, quality standards, and application expertise. The interactions were valuable in identifying potential suppliers for further technical evaluation and future collaboration,” said Jagadeesha M H, Team Lead, R&D, Delta Electronics India Pvt Ltd.

“Our priority was to understand which technologies could support improvements in production/quality/testing/automation. The live discussions gave us information that would have taken considerably longer to gather through individual supplier meetings,” said Venkata Ravindra, Head R&D, SFO Technologies.

“The event was the seventh in a row for me. This time, I felt that the combination of Indian and international suppliers gave our team a wider view of the available options. We have shortlisted potential partners and will continue the technical and commercial discussions initiated at the event. It was well organized this time and thanks to the team for making it better every year,” said Anil Krishna K S, Associate Director – Procurement & Supply Chain, Pioneer India Electronics Pvt Ltd.

The trade fairs were supported by the Government of Karnataka, with ELCINA as Partner Association and ICEA as Industry Partner. ELCIA, CLIK, GEZIA and AIEA participated as Supporting Associations; MEDEPC and CEAMA as Strategic Partners; and  VDMA, IEEE and OE-A as Conclave Partners.

Yeemak and Delvitech were Gold Partners, while Mouser Electronics participated as Registration Partner. JETRO and TEEMA supported the Japan and Taiwan pavilions, respectively.

Expanded national format proceeds to Delhi-NCR

The conclusion of the Bengaluru edition completes the first year of the trade fairs’ expanded national model.

Previously conducted in alternate years in Noida and Bengaluru, electronica India and productronica India moved to annual editions in both markets in 2026. The change represents 50 percent growth under the new format and provides more regular access to the electronics manufacturing regions of northern and southern India.

The next edition will be held from 28–30 April 2027 in Delhi-NCR.

The programme will mark the launch of Defence Electronics NEXT, creating a dedicated platform for technologies, capabilities and partnerships supporting India’s defence electronics ecosystem. It will also maintain a strong focus on printed circuit boards through BPCA and on the semiconductor value chain through the India Semiconductor Conclave.

Supported by the Uttar Pradesh Host State Partnership, these initiatives align with the Government of Uttar Pradesh’s investment priorities in defence electronics, PCBs, semiconductors and advanced manufacturing. Together, they will connect industry, government and technology stakeholders around sectors critical to India’s electronics manufacturing ambitions.

The post Electronica India and Productronica India 2026 Conclude in Bengaluru with 793 Exhibitors and 52,411 Business Visitors appeared first on ELE Times.