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

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

The challenge of modern test environments

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

AI-assisted authoring features

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

A concrete demonstration at NI Connect 2026

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

Statements

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

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

An AI-ready NI platform

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

Global access through Farnell

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why monitor air quality

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

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

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

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

The hardware of the project

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

Heltec WiFi Kit 32 module

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

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

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

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

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

The SGP30 sensor

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

The detailed hardware features of the breakout board are these:

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

Wiring the hardware

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

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

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

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

Software implementation

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

Installing MicroPython

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

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

Erasing the memory

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

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

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

Flashing the MicroPython interpreter

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

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

The control software

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

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

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

The Thonny IDE

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

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

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

System Setup and Main Loop

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

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

Listing 1 – System initialisation

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

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

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

Listing 2 – Main Loop

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

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

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

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

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

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

Configuration

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

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

Listing 3 – Configuration parameters

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

Conclusions

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

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

Related products

• CCS811 Air Quality Module

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

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

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

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

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

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

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

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

Hardware-Based Security Features Anticipating CRA and PQC

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

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

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

Improved Development Efficiency Through a Comprehensive Partner Ecosystem

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

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

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

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

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

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

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

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

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

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

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

Mouser Electronics, Inc., the authorised global distributor of the newest electronic components and industrial automation products, announces a new global distribution agreement with Amphenol CDI. Customers can now buy Amphenol CDI’s rugged RF connectors, high-performance RF cable assemblies, aerospace interconnect solutions and more through Mouser’s global distribution network. Mouser now stocks products from more than 60 Amphenol divisions.

RF connectors for defence applications

Amphenol CDI’s RF connectors for defence applications feature a compact design engineered to optimise assembly density. These connectors are built to meet stringent industry standards, letting designers improve electrical performance while minimising overall system complexity. Designed for robustness and high tolerance to radial and axial misalignment, Amphenol CDI’s military-grade RF connectors deliver reliable blind-mate performance. Thanks to their durability and reliability, these connectors withstand frequent mating cycles without compromising signal integrity, providing dependable performance in demanding defence and aerospace environments.

Right-angle RF connectors with swept output

Amphenol CDI’s right-angle RF connectors with swept output are designed for high-frequency applications that demand low loss and high signal integrity. The right-angle design simplifies cable routing while ensuring reliable RF connectivity in the most critical designs. Typical applications for these connectors include aerospace, defence and space systems, as well as industrial platforms where durability and signal integrity are essential.

RF cable assemblies for defence applications

Amphenol CDI’s RF cable assemblies for defence applications are a high-performance alternative to traditional semi-rigid coaxial solutions, delivering comparable electrical performance with greater installation flexibility. Designed for RF and microwave systems with high component density, these hand-formable cable assemblies can be easily routed inside enclosures and connected to external equipment while preserving signal integrity. Their construction combines a tinned copper braid, an inner layer of rugged metallic foil shielding, and a high-precision dielectric and centre conductor. A flexible outer jacket allows tight bends, excellent shape retention and fast, efficient field installation.

Precision RF adapters up to 110 GHz

Designed for frequencies up to 110 GHz, Amphenol CDI’s precision RF adapters offer low insertion loss and reliable performance in demanding RF environments. The many interface and mounting options, including solder-mount and field-replaceable versions, let designers optimise PCB layouts and signal paths. Available in side-entry, vertical, angled and narrow-body configurations, these adapters support optimal RF transitions and efficient integration into systems. Precision manufacturing, controlled-impedance design and high ruggedness ensure stable performance in aerospace systems, semiconductor test equipment, advanced instrumentation and other high-frequency applications.

To learn more about the Amphenol CDI products available from Mouser, visit the Amphenol CDI page.

For more news about Mouser and our latest product releases, visit the Mouser newsroom.

Mouser technical resources and support

As an authorised global distributor, Mouser offers the widest selection of semiconductors, next-generation electronic components and the newest industrial automation products. Mouser customers can count on original, 100% certified and fully traceable products from every one of its partner manufacturers. To help speed up customer designs, Mouser’s website hosts a vast library of technical resources, including a Technical Resource Centre, and offers product datasheets, supplier-specific reference designs, application notes, design technical data, engineering tools and other useful information.

Thanks to Mouser’s free newsletter, engineers can keep up with the latest news on products, technologies and applications. Mouser’s email news and reference resource subscriptions can be customised to match customers’ and subscribers’ specific and changing project needs. No other distributor gives engineers this level of customisation and control over the information they receive. Discover emerging technologies, product trends and much more by signing up today.

Mouser offers thousands of industrial automation products, available in stock and ready for immediate shipment. From predictive maintenance devices and control panels to a full range of industrial power, sensor and safety products, Mouser’s portfolio is there for customers to help them design, build and maintain complete automation solutions. For more information, visit the industrial automation solutions page and follow Mouser on social media.

For more information about Mouser Electronics, visit mouser.it.

The post Mouser and Amphenol CDI Sign Global Distribution Agreement for RF Connectors appeared first on Open Electronics.

Left: my old tip35c Right: my new “tip35c” submitted by /u/Jack_Hinrichs [link] [comments]

Microchip Technology and Marelli have announced a new solution that allows graphics and video generated by the vehicle’s central computer to be accessed directly from the displays via ASA Motion Link (ASA-ML), an open standard for automotive video connectivity. The demonstrator of this solution helps vehicle manufacturers simplify display architectures, optimise system costs and increase sourcing flexibility, while supporting the transition to software-defined vehicles (SDVs).

ASA Motion Link: an open standard for video connectivity

ASA Motion Link is an open standard for automotive video connectivity and SerDes (serializer-deserializer) communication, defined by the Automotive SerDes Alliance (ASA), a global consortium of vehicle manufacturers, Tier 1 suppliers and semiconductor companies. By enabling secure, high-speed video and graphics transmission between products from multiple technology vendors, the standard supports greater compatibility across the automotive ecosystem and offers a scalable alternative to proprietary video transmission technologies.

As a founding member of ASA, Microchip is driving the automotive industry’s transition from proprietary connectivity technologies to open, interoperable standards, and has demonstrated broad interoperability with its camera solutions, now extending these solutions to enable display connectivity.

The joint solution for centralised architectures

As vehicle architectures evolve toward centralised computing and software-defined vehicles (SDVs), vehicle manufacturers are looking for connectivity solutions that can support growing performance demands while reducing complexity, controlling costs and enabling scalable vehicle electronic architectures. In the joint demonstration solution, graphics and video content generated by the vehicle’s central computer is processed and transmitted through Microchip’s ASA-ML VS7000 chipset over a standardised high-speed link.

Marelli is a pioneer in display-side integration of the technology, enabling configuration and optimisation of the ASA-ML deserializer to decode standardised video streams directly on the display. This ensures accurate reception, synchronisation and distribution of centrally generated content, including camera feeds, navigation maps, infotainment graphics and vehicle control interfaces, while enabling simplified electronic architectures on the display side.

What the protagonists say

“The transition to software-defined vehicles is fuelling demand for more open, flexible and scalable electronic architectures,” said Kelei Shen, president of Marelli Electronics Business. “As an integrator within the automotive ecosystem, Marelli brings together best-in-class technologies from different partners to deliver complete solutions to vehicle manufacturers. This collaboration highlights how open standards can accelerate innovation while delivering the interoperability needed for the next generation of vehicles.”

“Open standards play a fundamental role in accelerating innovation by reducing dependence on proprietary technologies,” said Kevin So, Vice President of Communications Business Unit at Microchip. “Our collaboration with Marelli demonstrates how ASA Motion Link gives automotive manufacturers a secure, interoperable and scalable connectivity solution that simplifies integration across the ecosystem.”

Technical strengths

• Open interoperability enabling seamless connectivity between cockpit systems and displays from different technology vendors.
• Greater supplier flexibility thanks to open protocol specifications supported across the automotive ecosystem.
• Built-in security with link-level authentication and encryption integrated into the communication channel.
• Robust data transmission with lossless communication and high immunity to electromagnetic noise.
• Scalable performance supporting data rates up to 16 Gbps to meet a range of display needs.
• Optimised efficiency made possible by integrated Time Division Duplex (TDD) functionality and simplified Forward Error Correction (FEC), helping to lower power consumption.
• Paths toward progressively more Ethernet-centric architectures that contribute to ever lower power consumption.

The collaboration demonstrates how open-standard connectivity supports the vehicle’s on-board network and electronics by enabling secure communication between centralised computing systems and displays. It also reflects Marelli’s broader commitment to adopting open standards that help simplify development, optimise system costs and foster a more open automotive ecosystem.

For more information on Microchip’s ASA-ML solutions, visit the dedicated website.

The post Microchip and Marelli: Open-Standard Display Connectivity for Software-Defined Vehicles appeared first on Open Electronics.

The next phase of AI evolution moves from fact-finding chatbots to a learnable system that can plan tasks, invoke tools, search, and take various steps (workflows). On the leading edge of this evolution are AI agents that utilize LLMs, tools, and context to move toward an explicit goal. When adopting agentic AI, a key architectural decision concerns whether to operate a single agent or manage many specialised agents.

Single Agent vs Multi Agent AI Systems allows organisations to determine the best architecture for their day-to-day activities. Single-agent systems are easier to manage and keep simple, though multi-agent system architectures help split larger workflows among different agents. That said, some people think more agents don’t necessarily mean better results. It all comes down to task type, security, lag, and cost.

Understanding Single-Agent AI Systems

A single-agent AI system has a single primary agent. That agent accepts input, decides what action to take, chooses a tool, and produces an ending result. It can interact with databases, utilize an application programming interface, invoke enterprise knowledge systems, or execute any of the others as it operates. Its architecture includes a language model, the system instructions, the tools you might have, and a context manager. It receives a request, decides what to do next, and observes the result.

For example, a customer service person might check the status of an order, refer to a database of delivery schedules, explain the reason for a delay, and write a response- all in the same conversation without transferring the conversation between different AI agents. The major benefit of single-agent AI design is that it is simple. Developers can tweak the main system prompt, adjust the tool authorizations, trial the model’s behaviour, and observe how it makes decisions.

Although a single-agent approach is preferable when it is capable of fulfilling the requirements, Google Cloud advises deploying a single agent. It can regress when one agent needs to handle too many tools, tasks, or conflicting prompts. These limitations may make it necessary to utilize multi-agent architecture.

What Are Multi-Agent AI Systems?

Multi-agent AI is when multiple agents work together to cooperate on a shared goal. Each of the individual agents might be given different source material, instructions, scope, and access to different tools, and therefore one may do one step. For example, in the flow of a piece of enterprise research, each of the agents might be given a job such as finding info, fact-checking the technical points, assessing the impact on the market, and then writing up the report.

This method is based on AI agent orchestration- initiating tasks, disseminating data, defining interdependencies, and consolidating the results. It is possible to guide the approach or use it on demand, where agents determine their next action.

These patterns are popular:

1. Sequential Orchestration: The agents follow a predetermined order, and one agent makes use of the output of the previous one.
2. Parallel execution: Completing various components simultaneously could decrease the amount of time to complete the task.
3. Coordinate-based architecture: A central agent assigns tasks to specialised agents and pools their results.
4. Hierarchical task decomposition: A lead agent breaks down a complex goal into a number of simple goals (possible till being simple enough to be assigned to low agents).
5. Collaborative or group-chat architecture: The agent shares outputs, challenges concepts, and iterates on a shared product.

The AI agent’s approach to orchestration explains these patterns and provides an example of how a trade-off exists in terms of the level of coordination, speed, and flexibility for any given deployment.

Single-Agent vs Multi-Agent AI Systems: Key Architectural Differences

The primary difference is in how you slice and dice each responsibility and what decision you’re making. In a single agent design, a single agent takes care of the complete workflow, even when talking to more than one tool. In a multi agent AI system, a set of agents are divided between responsibilities and communicate with each other through orchestration.

Task complexity is also important. Many structured tasks like answering customer questions, summarising documents, information retrieval, and the majority of data analysis can be achieved quickly by a single agent. Multi-agent systems are better if a workflow requires clear specialist roles, planning, or individual investigations.

The scalability and maintenance are also different. Adding new features and new tools can make a single agent system too complex to be manageable. Multi-agent modular systems will allow developers to change a single agent without causing all these implications in the system, as long as the interface or port to the agent is well specified. But multi agent systems also add the problem of additional communication, states, testing, and monitoring.

Cost also matters. A single-agent workflow might be able to use fewer model calls, but a multi-agent workflow could lead to the usage of more tokens and greater computing power. Parallel execution might lower the time taken for completing the task, but will bump up the peak resource consumption. Therefore, organisations need to assess single agent vs multi-agent architecture with actual task success rates, response times, and cost failure rates.

Real-World Use Cases of Single-Agent AI Systems

Single-agent AI use cases are more interesting if that one agent can do a workflow with only a few tools and KBs. Customer service: A single agent could answer a question about a product, find an order, and suggest troubleshooting steps. Productivity tools- summarize a document, search enterprise KBs, prepare for a meeting.

A further application is software development. A coding agent can examine a code repository, create code, execute tests using authorized applications, and make recommendations for repairs. An IT support agent could explore common problems, query monitoring systems, and make recommendations. These applications could take advantage of a fairly simple control structure. Organisations could set up rules for permissions, examine results, and incorporate human approval for critical actions without having to coordinate a large network of agents.

Multi-Agent AI Use Cases Across Industries

Multi-agent use cases are especially valuable when a business process can be achieved using multiple different functionalities. Within financial markets, various specialised agents can gather market information, monitor the risk profile of the portfolio, perform rule checks and make suggestions for actions. Financial transactions and other large transactions should still be subject to correct authorisation and regulation.

Functions might also be linked to, for example, watch sensor data from machinery, view your maintenance history, inventories of spare parts, and support fixing. A chain like this to combine functions would foretell failure and avoid downtime, yet to be safe, devices require suitable deterministic controls with people involved. For instance, one actor reacts to a security alarm, while another deals with connected phenomena, while the other starts compiling a report. Controls will need to restrict and authenticate access, because a wrong conclusion might cause a negative reaction to the incident.

The consequences might be beneficial for other links in the supply chain, too. For example, each agent could review inventory, track records of their suppliers, delays in shipping, and demand forecasts before producing the suggestion to human planners. For publishing and research, agents could handle source discovery, fact-checking, analysis, and writing. The results, however, still need human review, particularly if there are technical claims or fast-charging data.

Challenges, Security and Governance

However, multi-agent AI systems also pose new risks which organisations need to manage before deploying. For example, agents may generate inaccurate information, misinterpret commands, or deliver substandard results to other agents. The presence of several different elements can also make failures harder to track.

Security is crucial when agents have access to business apps, private files, or are equipped with tools that can modify data. Every agent should be assigned the minimum permissions they require. Businesses should verify tool inputs, restrict access to sensitive information, log key activities, and seek human approval for critical decisions.

Another challenge is context management. Agents require useful context to perform their tasks, but more context can add to processing costs and create distraction. Systems should deliver brief, task-specific context between agents, and shared state should be preserved when needed. Testing should test the whole workflow, not just individual agents. Appropriate metrics would be task success and accuracy, tool call error, latency, cost per task success, and human intervention.

Choosing the Right AI Agent Architecture

Choose between a single-agent or multi-agent system based on the task you are trying to solve, and not by the number of agents you want to use. If it’s something within a well-defined, relatively narrow scope and with narrow tool access, and where workload performance can be improved by providing more specific instructions or by designing the workflow, then you should probably want to use a single agent. Multi-agent design is appropriate when responsibilities entail specialised capabilities, isolated security domains, parallel execution, or coordination.

The most pragmatic solution may be to construct and test the simplest architecture capable of achieving the performance requirements; then, assuming that measurable constraints remain, deploy specialised agents and orchestration following. Long-term, the future of an agentic AI architecture isn’t simply about more agents but about more reliable, secure, measurable, and useful ones. For businesses, success will be designing an architecture that’s commensurate with real-world needs and ensures the costs, accountability, and human oversight are all well managed.

The post Single-Agent vs Multi-Agent AI Systems: Architecture, Key Differences and Enterprise Use Cases appeared first on ELE Times.

Interim steps to the end destination are often a wise policy, economically if for no other reason. And, it turns out, non-bleeding-edge gear can be made wiser in the process, too.

As recently mentioned, I shortly plan to begin migrating my LAN from 1 to 2.5 GbE (and 10 GbE in some spans). That said, to be clear, I don’t plan to do so in one fell swoop. And frankly, I may never complete the transition.

Most of my LAN clients connect to either the main router or mesh points over wireless, and it doesn’t make sense to update any switch hardware tethered to them until they’re upgraded beyond the Wi-Fi 5-plus-1 GbE backhaul technology threshold. And even if a LAN client hooks into the LAN fully in a wired fashion, most of them are 1 GbE-only.

There are exceptions, of course; my Apple Mac Studio in the office is 10 GbE-supportive, and I’ve upgraded the highest-end of my three QNAP NASs in the furnace room below it for 10 GbE cognizance, too. And then there are the multiple MoCA 2.x adapters, one in the furnace room, that I may reintroduce in the future. Perhaps obviously, therefore, the multi-port switches in both locations will get upgraded first; if the Cat 5e span between them turns out not to be 10 GbE-reliable, I’ve got 50 feet of optical cable and two SFP+ modules ready to replace it.

For the switch in the furnace room, sitting right next to the router and cable modem, and therefore acting as the LAN nexus, the mix of legacy and more modern, higher speed clients proved to be manageable in combination with an open-box (truly so, it turned out; never unpacked, far from used) Zyxel device, the XGS1010-12, that I bought for $49.99 on eBay.

As you can see from the photo, it baseline-integrates eight 1 GbE ports, the same quantity and speed as with the switch I’m currently using, a TRENDnet TEG-S81g. But there are also two 2.5 GbE ports, along with two 10G SFP+ cages. Not only will the XGS1010-12 expand my total port allotment at that location, its RJ-45 and SFP+ additions will deliver higher speeds, too. And, it turns out, another pleasant surprise also awaited me post-purchase.

Switch types

Both my existing switch and the Zyxel XGS1010-12 are unmanaged, meaning (to quote Wikipedia) that they have no configuration interface or options. They are plug and play. They are typically the least expensive switches, and therefore often used in a small office/home office environment. Although likely running firmware, it’s usually non-upgradeable, and no settings or broader graphical user interface (GUI) exist for the user to access, either. Therein the lack of a MAC address on my XGS1010-12’s underside.

Managed switches, on the other end of the spectrum, are (as the name implies) user-configurable and -updateable. Why would you want such a thing? One example is virtual LAN (VLAN) support, the ability to isolate various clients (and clusters of them) so that they can’t access other client groups and, in some cases, the WAN either. One well-known example here is the “guest network” feature bundled with many modern routers, which provides visitors with the ability to use the Internet without allowing them to “see” devices on the main LAN.

You might also want to, for example, be able to isolate “smart home” IoT devices from the rest of the LAN so that, should one of them get hacked, the infection can’t then spread elsewhere. Managed switches also enable you, for example, to prioritize traffic flowing through a particular port (for VoIP applications, say), to hard-wire various ports’ bandwidth and duplex settings, to implement MAC filtering and other network access rules, to aggregate multiple ports for the same connection (to a multi-port NAS, for example), and to more broadly monitor real-time network traffic conditions, along with other features.

And between these two extremes is the “smart” switch, implementing a subset of the fully managed switch’s capabilities, across a subset of the total available ports, but in a more user-friendly fashion. Take, for example, the front panel multi-position configuration switch offered with this low-priced Tenda device.

The lion in sheep’s clothing

All of which leads to the XGS1010-12’s managed “big brother”, the XGS1210-12. Notice the familial name similarity? They could, in fact, be twins, judged by their visual commonality (aside, of course, from the respective product names on the front panels, and the MAC address stamped on the XGS1210-12 underside).

It turns out, in fact, that the hardware allotment is identical between the two, aside from one other front panel deviation; the XGS1210-12 extends a PCB-mounted reset button to the exterior for easy user access. They otherwise differ (I’m speaking specifically of my XGS1010-12 with hardware version A1; hold that thought) only in the software running inside. Have you guessed yet where this tale is headed?

After purchasing the XGS1010-12 unmanaged switch, and while waiting for the eBay to ship it to me, I did a Google search on the product code to see what else I could find out about it. Near the top of the results page, I excitedly learned, was a Reddit post (along with subsequent follow-up posts from the original poster, and broader r/homelab community discussion) describing how to hack the XGS1010-12 and upload the XGS1210-12 firmware to it, converting it to the managed-switch version in the process.

“But Brian,” you might be thinking right about now, “I thought you said that unmanaged switches don’t have user-accessible GUIs?” I did, actually, but I never said anything about a command-line user interface (CLI), did I? What Redditor zeda1ert realized was that in-between two of the side passive airflow vents was a standard UART console header.

The Reddit post has detailed instructions; you’ll see in the ensuing conversation that three years later I chimed in with a bunch of questions. Fortuitously, zeda1ert was still monitoring the post and graciously helped me. The following few paragraphs summarize the process.

Connect a USB-UART adapter (I own both this 3.3V one and this 5V one and can use either, because the switch’s 3.3-V UART circuitry is self-powered, so only the adapter’s Tx, Rx and GND need to be hooked up, although the latter’s cable is conveniently longer) to the switch and your computer, start up a CLI terminal session (I used MacOS’s built-in command-line “screen” utility), and issue a timing-critical ESC to terminate the switch’s boot sequence before it finishes.

Now configure a MAC address for the switch, along with static IP addresses for both the computer and switch (different, but on the same subnet), then fire up a TFTP server session on the computer (MacOS has a built-in one, but it’s also command line-based and fairly cryptic, so I instead went with SoftPerfect’s excellent freeware GUI utility, which I’ll include in the next best-of list). Use it to download and install the most recent v1 XGS1210-12 firmware to the switch. Then reboot, and the switch is now managed.

And speaking of “re”(boot), the missing hardware “re”set button isn’t a problem in practice, as you can alternatively accomplish the same reset outcome through either the switch’s CLI or GUI.

Hardware fortuity

I’d earlier explicitly noted that mine was an A1 hardware version unit. The distinction was initially important, and remains so for “stock” firmware, although broadly no longer (stay tuned). Partway through its ongoing product life, Zyxel redesigned the XGS1010-12, switching from Realtek’s RTL8226 2.5 GbE PHYs to the newer RTL8221B with reduced power consumption, therefore also lower heat dissipation.

Unfortunately, this caused an incompatibility with the legacy firmware for the XGS1210-12, which wasn’t similarly hardware-updated. Installing XGS1210-12 firmware on a version B1 XGS1010-12 resulted in nonfunctional multi-gig ports.

A further nuance involves the various A1 revisions, A, B, and C. Originally, it was not possible to install any newer v2 XGS1210-12 firmware (patch 0 link, patch 1 link with OpenSSL vulnerability fix) on a version A1, revision C XGS1010-12 unit such as mine.

Scroll down in the Reddit thread, though, and you’ll see that another contributor later figured out how, via resistor relocation, to spoof the board identifier, enabling successful v2 firmware update. But I haven’t bothered doing so, because I have a more robust alternative next step planned for eventual implementation.

Open source once again saves the day

Three years back, when the original Reddit thread post was published, OpenWrt development for the XGS1010-12 was immature and insufficiently staffed, with the resulting available build functionally incomplete. A lot has changed since then, notably including the inclusion of support for version B1 units’ RTL8221B PHYs (in spite of Realtek’s longstanding and ongoing open-source reticence) beginning in mid-2026.

Right now, the v1 “stock” XGS1210-12 conversion does everything I need, and since the switch isn’t exposed to the Internet, I’m not worried about its ongoing “Heartbleed” OpenSSL compromise. But at some point, I’ll probably take the plunge and further migrate to OpenWrt instead. If and when I do so, I’ll report the result here in the form of another blog post. And until then, I as-always welcome your thoughts in the comment on what I’ve discussed in this post!

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

Related Content

• The multi-gig migration: Motivations and implementations
• NAS upgrades: proprietary lockouts and surmounts
• The whole-house LAN: Achilles-heel alternatives, tradeoffs, and plans
• SFP: Add-in module delivers diminutive performance, flexibility
• Heartbleed: the wakeup call the open-source community needed?

The post Hacking a network switch for management enhancements appeared first on EDN.

The winners of the 2026 Infineon Start-up Challenge – the competition for critical technologies for the next generation of humanoid robots – have been announced by Infineon. After four months of hard work, the jury finally chose 15 startups from more than 100 submissions to pitch their solutions during the Infineon Start-up Challenge Demo Day at EBSCON – the Electronic Based Systems Conference in Graz, Austria.

The challenge united start-ups from Europe and Asia, investors, industry innovators, and technology leaders around robotics and physical AI innovation. The participants innovated solutions to enable humanoid robots to more effectively sense their surroundings and interact with the environment, handle objects, and execute precise movements. The winners were chosen by an international jury of industry, venture capital, start-up ecosystem, and technology innovation experts.

“Strategic collaboration with start-ups creates a win-win situation, combining novel ideas with industrially proven semiconductor technologies and thereby accelerating innovative, market-ready applications,” said Sören Jehmlich, Vice President Ventures, Start-ups & Ecosystems at Infineon. “The Start-up Challenge is a key pillar of our innovation culture, bringing together entrepreneurial courage, technological expertise and rapid implementation.”

“The quality of innovation we have seen throughout this year’s challenge has been remarkable. Start-ups from Europe and Asia demonstrated how diverse perspectives and technologies can come together to solve some of the most important challenges in humanoid robotics. Programs like the Infineon Start-up Challenge are essential for connecting innovators across regions and accelerating the path from breakthrough ideas to real-world impact,” highlights Jerry Park, Director Asia Pacific at Infineon.

The winners are shaping the future of humanoid robotics

First place went to AdapTronics S.r.l. from Italy with its innovative gripping technology, which allows robots to safely handle a wide variety of objects without conventional suction cups or mechanical grippers. Such flexible object handling is a key capability for next-generation humanoid robots. The technology also addresses applications in logistics, packaging, and future space-robotics. The jury highlighted its versatility, innovation, and potential for industrial use. “Winning the Infineon Start-up Challenge is a great recognition of our team’s work and vision,” said Lorenzo Agostini, Founder and CEO at AdapTronics S.r.l. “The challenge gave us valuable access to technology experts, industry insights and a strong innovation network. We look forward to further advancing our solution and exploring new opportunities for collaboration.”

COSMOKE and TECHiFAB complete the podium

Second place went to COSMOKE from South Korea. The start-up developed COSKIN, an innovative flexible magnetic tactile sensing technology that enables humanoid robots to perceive touch, pressure, and physical interactions. By providing essential tactile feedback for dexterous manipulation and safer human robot interaction, COSKIN plays a key role in advancing next-generation humanoid robotics and physical AI.

TECHiFAB from Germany took third place with its In-Memory Computing based approach to real-time motor control for robotic actuators. This is physical AI by definition and is designed to enable faster, more energy-efficient and safer control while improving performance directly within the drive train of humanoid robots.

The solutions presented during the challenge reflect the technologies required for the next generation of humanoid robots, from tactile sensing to dexterous gripping and precise motor control. To support the start-ups in developing and validating their concepts, Infineon provided access to dedicated hardware and software platforms, including Environmental Sensing Kits, Artificial Skin Kits, Motor Control Kits, Visual Feedback Kits, and in-house AI development software, enabling teams to accelerate prototyping and transform innovative ideas into scalable applications.

“Humanoid robots are the most complex form of physical AI and highly relevant for Infineon,” said Dirk Geiger, Senior Director & Team Lead Humanoid Robotics at Infineon. “They combine power, sensing, real-time connectivity, safety and security with software and AI compute, turning machines into autonomous systems. No single company can master
this complexity alone. With the Start-up Challenge, we gave start-ups early access to our hardware stack as well as our software and system offering and brought them together with our partners and application experts. The solutions shown prove how quickly ideas can become building blocks for scalable humanoid platforms and give valuable insight into where the technology is heading.”

The Infineon Start-up Challenge is part of the global Infineon Start-up Co-Innovation Program, in which Infineon collaborates with start-ups as a technology and development partner. As the winner of the Infineon Start-up Challenge 2026, AdapTronics will now join the Infineon Start-up Co-Innovation Program to further develop and scale its solution together with Infineon. The collaboration will officially kick off at the Start-up Night in Munich in November, where Infineon showcases successful start-up partnerships and highlights the value of start-up-driven innovation. Infineon continues to engage with other promising participants for future technological and business collaboration. By combining the agility and creativity of start-ups with Infineon’s technological expertise and global capabilities, the program supports the development of scalable applications and accelerates innovation in humanoid robotics and physical AI.

The post Infineon Announces Winners of 2026 Start-up Challenge on Humanoid Robotics appeared first on ELE Times.

Recently, I suddenly had a strong desire to make an interesting USB device. Once again, when I was browsing AliExpress, I came across an STM32F070 for about 49 cents, and I bought it right away. When it arrived, the question arose of convenient debugging during development, and for that purpose I made this board. I wanted to share it. submitted by /u/Potential_Step1619 [link] [comments]

Waveshare has put together a devkit built around the ESP32-C5-WROOM-1U module and a 3.5-inch capacitive touch IPS display. It is the largest ESP32-C5 board with a touch display seen so far, and it is meant for building voice and visual interfaces that talk to large online AI models. The 32-bit RISC-V processor runs at up to 240 MHz, with 384 KB of SRAM, 320 KB of ROM, 8 MB of PSRAM and 32 MB of Flash. The low-power RISC-V core, meanwhile, runs at 40 MHz.

The radio covers dual-band Wi-Fi at 2.4 GHz and 5 GHz, Bluetooth 5 LE and IEEE 802.15.4 for Zigbee 3.0 and Thread. So the board is not limited to classic connectivity: it can join a Matter or Thread home automation network without extra modules. Anyone who wants to start from a simpler base can still find an ESP32 development board with Wi-Fi and Bluetooth for their first experiments.

Display, touch and sensors on the I2C bus

The capacitive IPS panel measures 3.5 inches, has a resolution of 320 x 480 pixels and supports 262K colours. The ST7796 driver drives the display over SPI, while the FT6336 touch chip answers on the I2C bus. The board also integrates a six-axis QMI8658 IMU, a PCF85063 RTC and an SHTC3 temperature and humidity sensor. For storage there is a TF card slot, handy for logs and multimedia files.

The audio section relies on the ES8311 codec and the NS4150B amplifier, with microphone and speaker already on board. The board can therefore record voice, play audio and handle a conversation with a remote assistant. The factory firmware is based on ESPBrookesia, while the tutorial for the XiaoZhi AI voice assistant shows how to connect to online AI model platforms. The manufacturer collects examples and instructions on the Waveshare maker site.

AXP2101 power and expansion pins

Power management goes through the AXP2101 chip. The optional 3.7 V lithium battery is charged through the MX1.25 connector or from the USB Type-C port. The board measures 95.11 x 63.67 x 14.10 mm and offers a 32-pin expansion connector with 2.54 mm pitch, so it can be plugged into a breadboard without adapters.

• ESP32-C5-WROOM-1U: 32-bit RISC-V up to 240 MHz, 384 KB SRAM, 320 KB ROM, 8 MB PSRAM, 32 MB Flash
• Display: 3.5-inch capacitive touch IPS, 320 x 480 pixels, 262K colours, ST7796 driver over SPI
• Touch: FT6336 chip over I2C
• Sensors: six-axis QMI8658 IMU, PCF85063 RTC, SHTC3 temperature and humidity
• Audio: ES8311 codec, NS4150B amplifier, microphone and speaker
• Power: AXP2101 PMU, 3.7 V lithium battery, USB Type-C or MX1.25
• Optional camera: BF3901 at 320 x 240 pixels

On the software side you can choose between ESP-IDF, Arduino IDE and Visual Studio Code with the Espressif IDF plugin. For anyone who wants to add external memory to the project, the catalogue also offers a microSD card reader with SPI interface, useful when the 32 MB of Flash are not enough for audio files.

Prices for the development kit range from 31.99 to 35.99 dollars in the manufacturer’s online store, from 37.99 to 43.99 dollars on AliExpress and from 43.99 to 47.99 dollars on Amazon, depending on whether you choose the version with or without camera and battery. Finally, anyone who wants to measure the environmental conditions around the board can add a module with a DHT11 sensor on the 32-pin connector.

Source: https://docs.waveshare.com/ESP32-C5-Touch-LCD-3.5

Related products

• ESP32 – 32 GPIO development board with WiFi and Bluetooth
• MicroSD Card Reader module with SPI interface
• DHT11 Temperature and Humidity Sensor Board

The post ESP32-C5 with 3.5-inch touch display: the Waveshare devkit for voice AI appeared first on Open Electronics.

Infineon Technologies AG is expanding its technology partnership with NVIDIA based on 800 Volt direct current (VDC) power architectures for AI data centres. The latest from the companies is an announcement timed to NVIDIA’s GTC Berlin developer conference based on the high-voltage intermediate bus converter (IBC), which is one of the most critical and challenging conversion stages in the power chain of AI data centres, with Infineon’s gallium nitride (GaN) emerging as a preferred technology. This builds on the companies’ prior collaboration announced in May 2025, and elaborated on in October 2025, which included power-path protection and hot-swap functionality for 800 VDC server architectures.

“AI is fundamentally reshaping the demands we place on power infrastructure, and semiconductors are at the heart of solving that challenge,” said Jochen Hanebeck, CEO, Infineon Technologies AG. “Our collaboration with NVIDIA on 800 Volt power architectures shows what is possible when deep semiconductor expertise meets the pace of innovation required by AI. Gallium nitride-based system solutions are a key enabler of this next generation of data centres, and Infineon is proud to help power the AI era from grid to core.”

The growing power demands of AI workloads have driven data centre designers to adopt intermediate-bus architectures. An intermediate bus converter (IBC) steps down the main DC bus voltage and supplies a lower voltage to subsequent power-conversion stages, which generate the voltage levels required by AI processors. This multi-stage approach helps manage power-distribution losses and improve overall efficiency while meeting strict constraints on board space, thermal management and system reliability. As AI computing requirements grow, 800 VDC is emerging as an additional power-distribution approach designed to support higher power densities in AI data centres.

Gallium nitride is well suited to meet these requirements. Compared to silicon, GaN allows power devices to switch significantly faster with lower losses, which enables higher switching frequencies of up to 1 MHz at the IBC stage. This in turn allows the size of magnetic components and passives to shrink considerably, directly translating into higher power density and lower board heights, a critical advantage as rack space becomes an increasingly scarce resource in AI data centres. At the same time, GaN’s low conduction and switching losses support best-in-class conversion efficiency, helping to limit heat generation even as power levels per rack continue to climb toward the megawatt range. These characteristics make GaN the preferred semiconductor technology today capable of meeting the combined demands of power density, efficiency, and fast transient response at the IBC stage, a position Infineon has built on the back of many years of leadership in GaN development and manufacturing. In combination with PSoC control C3 and EiceDRIVER product families, full system solutions for IBCs are provided by Infineon.

Infineon’s GaN portfolio supports the full range of conversion ratios required at the HV IBC stage, including 800 V to 50 V, 800 V to 12 V, and 800 V to 6 V, giving data centre operators the flexibility to optimize rack architectures without compromising on power density or efficiency. At this combination of high switching frequency, power density and efficiency, the GaN power switches are used to their full benefits. Infineon’s technology leadership in power semiconductors relies on extensive in-house manufacturing capabilities as well as application and system expertise built up over many years. The company’s portfolio spans IBC solutions across silicon, gallium nitride and silicon carbide, supporting customers along the entire range of their design requirements.

Infineon and NVIDIA collaborate along the entire data centre power chain. In addition to IBCs, this also includes power path protection solutions, as announced in October 2025, targeting safety and service aspects such as the hot-swap controller functionality and enabling future server boards to operate in 800 VDC power architectures.

This advanced collaboration underscores Infineon’s position as a grid-to-core power semiconductor partner for AI infrastructure, supplying solutions across the full power delivery chain, from grid-facing power supply units to the point of load at the processor.

The post Infineon and NVIDIA Advance GaN-Based Power Conversion for 800 VDC AI Data Centres appeared first on ELE Times.

Futura Elettronica, the company behind Open Electronics, and its Italian magazine Elettronica In are Technical Partners of Maker Faire Rome again this year. The fair is the yearly meeting point of the European maker community.

23 to 25 October at the Gazometro Ostiense

The 2026 edition takes place from 23 to 25 October, once again at the Gazometro Ostiense in Rome. In recent years this striking industrial venue has proved a perfect home for three days dedicated to makers.

Find us at stand M15

Look for stand M15, where you can talk to the Futura Elettronica team about your projects, ask for technical advice and see the products up close.

Starter kits, boards and books at the fair

Futura Elettronica will bring a large part of its range to Rome: starter kits, boards, components, tools, the Elettronica In magazine and books (the latter two are in Italian). The catalog counts about 6,000 products, so we cannot bring all of them. If there is a specific product you would like to see in person, let us know before the fair and we will try to have it with us.

Tickets and the full programme are available on the official website, makerfairerome.eu.

The post Open Electronics at Maker Faire Rome 2026: Come and Meet Us appeared first on Open Electronics.