Oddly Specific Objects has launched a crowdfunding campaign for the Open Book Touch, an ESP32-S3-based e-reader with a 4.26-inch e-paper display with 480 x 800 resolution and a capacitive touch screen. The open-source hardware device comes with 16 MB flash, 8MB PSRAM, a frontlight with warm and cool LEDs, a microSD card for storage, and an 800 mAh user-replaceable LiPo battery with everything housed in a 3D printed enclosure. Open Book Touch specifications: SoC – Espressif Systems ESP32-S3 dual-core MCU with Wi-Fi and Bluetooth LE connectivity Memory – 8 MB octal SPI PSRAM Storage 16 MB quad SPI flash MicroSD card slot Display 4.26-inch, 480 × 800 e-paper; 1-bit and 2-bit grayscale with dithering Capacitive touchscreen based on FT6336 low-power controller Frontlight – five warm + five cool LEDs, independently dimmable USB – USB Type-C for power/charging and programming Misc – 3D-printed plastic enclosure; snap-fit, with 3D-printable CAD for custom shells; [...]

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Makerfabs MaUWB for Home Assistant is an ESP32-S3-based Ultra-Wideband (UWB) module designed for indoor positioning and Real-Time Location Systems (RTLS). It runs ESPHome for Home Assistant integration, enabling it to be used as part of a UWB-based positioning system to track tagged devices in homes, offices, and other indoor environments. The module combines an ESP32-S3 with a MaUWB UWB module based on the Qorvo DW3000, featuring a PA/LNA for long-range ranging. Compared to the earlier ESP32-S3 version, it drops the OLED and adds a WIZnet W5500 Ethernet controller that supports PoE. The module also supports Wi-Fi 4 and Bluetooth 5.0, and comes in an enclosure for fixed installations. Makerfabs MaUWB for Home Assistant specifications: Wireless Module – ESP32-S3-WROOM-1 SoC – Espressif Systems ESP32-S3 CPU – Dual-core Tensilica LX7 up to 240 MHz with vector extension for AI/ML workloads RAM – 512KB SRAM; 8MB PSRAM Wireless – WiFi 4 and Bluetooth LE 5 [...]

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Last week, I wrote about the ESP32-C5-LCD-1.47 as one of the first ESP32-C5 devkits with a built-in display enabling dual-band WiFi 6 connectivity for IoT and HMI projects. However, one reader lamented the lack of touch-capable screens, at which point kwchow pointed to the ESP32-C5-Touch-LCD-2.8 model offering just that by pairing an ESP32-C5 with a 2.8-inch IPS  display with capacitive touch and 320×480 resolution. The new board also features a microSD card, a USB-C port for power and programming, a built-in microphone and a speaker connector, a few sensors, and various I/Os connectors for expansion. Let’s have a closer look. ESP32-C5-Touch-LCD-2.8 specifications: Wireless module – Espressif Systems ESP32-C5-WROOM-1-N32R8 SoC – ESP32-C5HR8 CPU Single-core 32-bit RISC-V processor @ up to 240 MHz Low-power RISC-V core @ 40 MHz acting as the main processor for power-sensitive applications Memory – 384 KB SRAM on-chip, 8MB PSRAM Storage – 320 KB ROM Wireless Connectivity [...]

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We’ve covered plenty of connected displays based on ESP32 chips over the years, but the Waveshare ESP32-C5-LCD-1.47 is a little different, since it’s the first to feature an ESP32-C5 chip for dual-band WiFi 6 connectivity. It’s basically based on the ESP32-C6-LCD-1.47 design, but replaces the ESP32-C6 supporting 2.4 GHz WiFi 6 with an ESP32-C5 capable of handling both 2.4 GHz and 5 GHz WiFi 6. Other features mostly remain the same: a 1.47-inch 262K-color display with 320 x 172 resolution, 4MB of flash, an RGB LED, a microSD card slot, and two 9-pin headers for GPIO expansion. Waveshare ESP32-C5-LCD-1.47 specifications: Wireless MCU – Espressif Systems ESP32-C5FH4 CPU Single-core 32-bit RISC-V processor @ up to 240 MHz Low-power RISC-V core @ 40 MHz acting as the main processor for power-sensitive applications Memory – 384 KB SRAM on-chip Storage – 320 KB ROM, 4MB flash Wireless Connectivity Dual-band (2.4GHz/5 GHz) 802.11ax WiFi [...]

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Most machines warn you before they fail. A motor begins to vibrate differently. A pump slowly drifts out of balance. A bearing develops a new mechanical signature. A cooling fan starts producing frequencies that were not present during normal operation.

These changes can appear well before a complete breakdown. The challenge is detecting them early, reliably, and at a cost that makes monitoring practical across more than just the factory’s most expensive machines.

UNO Q provides a flexible platform for building a compact predictive maintenance node that collects vibration data, runs a machine learning model locally, and turns unusual machine behavior into actionable alerts. The result is a practical way to begin monitoring motors, pumps, fans, bearings, compressors, and other rotating equipment without immediately deploying a complex cloud infrastructure.

Pickin’ up good (and bad!) vibrations

Vibration is one of the most useful signals for understanding the condition of rotating machinery. When a machine is operating normally, its motor, bearings, shafts, and mechanical components produce a relatively consistent vibration pattern. Changes in alignment, balance, friction, mounting, or component wear can alter that pattern.

A traditional monitoring system might trigger an alert whenever vibration exceeds a predefined value. That approach can be useful, but machines rarely operate under perfectly fixed conditions. Speed, load, product type, temperature, mounting position, and operating mode can all influence the vibration signal. This is where anomaly detection becomes especially valuable.

A vibration sensor does not automatically identify every mechanical fault. It provides the raw signal from which meaningful patterns can be extracted. UNO Q – perhaps starting with an Arduino^® Modulino Movement sensor and later adding a more precise vibration sensor – can capture acceleration data across three axes. Mounted securely on a motor or pump housing, it can record how the machine behaves during normal operation and how that behavior changes when a fault begins to develop.

The sensor can capture more than a single vibration value. It can record a time series that describes the direction, amplitude, and frequency of the movement. This gives a machine learning model more information than a simple threshold alarm.

A team can collect representative data from a healthy machine, train an anomaly detection model, and deploy it to the edge. When the model sees a vibration pattern that is sufficiently different from what it learned, it produces a higher anomaly score.

The system does not need to know the name of every possible fault before deployment. It can begin by answering a more practical question: “Is this machine still behaving as expected?”

See this pattern in action: This sense-infer-respond loop is exactly what’s demonstrated in the AI Guard Demo with Arduino UNO Q, Modulino sensors, and local NPU face recognition, published on Arduino^® Project Hub by user shivaylamba. The project is built around face recognition, but the underlying architecture – a Modulino sensor triggering local inference, which then drives a physical response – is the same blueprint a vibration-based anomaly detector would follow, just with different sensors and a different model.

Or check out how AudioLog uses Arduino UNO Q Edge AI to “listen” to machines, detecting early signs of failure to prevent costly industrial downtime.

From raw data to business intelligence

A first predictive maintenance project can begin with one machine and a limited number of operating conditions.

The out-of-the-box example shipped with Arduino^® App Lab is a great starting point.

The Modulino Movement sensor is mounted firmly on the equipment. UNO Q records acceleration data while the machine is idle, starting, running normally, operating under different loads, and shutting down.

The microcontroller side manages sensor acquisition, sending the data to the Linux side that manages data logging, model execution, dashboards.

Collecting this range of normal behavior is important. A model trained on only one operating condition may incorrectly classify a legitimate speed or load change as a fault.

Depending on the application, the project can use classification or anomaly detection.

Classification is useful when the team already has examples of known conditions, such as normal operation, imbalance, misalignment, or a loose mounting.

Anomaly detection is useful when fault data is limited or when intentionally damaging equipment to create training examples would be unsafe or impractical. In this case, the model learns normal behavior and highlights signals that do not fit that baseline.

Worth a look: For a hands-on look at what running ML locally on UNO Q actually feels like in practice, check out Running ML/AI on Arduino UNO Q on Hackster. It’s a capability demo rather than a predictive maintenance project specifically, but it walks through the App Lab sample apps and on-device inference experience that the classification and anomaly detection workflows above are built on top of.

Looking for even more inspiration? Check out this predictive maintenance project that reads automotive CAN bus raw data to determine systematic drifts early, and alert you when the line is not in sync with specs.

A large model is not necessarily a better model

The model for a vibration monitoring application has a narrow job. It does not need to understand images, natural language, or hundreds of unrelated machine types. It only needs to distinguish the relevant operating patterns of the equipment being monitored. This focus on smaller, task-specific models helps make always-on monitoring practical. 

Continuous vibration monitoring can generate a large amount of data, but sending every raw sample to the cloud is not always necessary – especially considering it can increase bandwidth consumption, introduce recurring infrastructure costs, and make the monitoring system dependent on network availability.

UNO Q processes vibration windows locally and stores or transmit only useful information, such as the current health state, anomaly score, operating mode, timestamp, or alert event. A local dashboard can show recent machine behavior and event history. When an anomaly exceeds a validated threshold for a defined period, the system can activate a warning light, sound a buzzer, log the event, or send a message to a maintenance service.

Cloud connectivity can still be added when it provides value. The difference is that the core detection process does not need to stop when the internet connection is unavailable.

Going deeper: Edge Impulse has specific predictive maintenance guidance around monitoring equipment while it runs, so service teams can act before failure occurs. Its optimization tools are designed precisely for constrained edge deployments like this one: quantized int8 models and RAM-optimized compilation matter here because always-on monitoring needs lower compute, lower memory use, and better power behavior over the long run.

The real value of predictive maintenance

Predictive maintenance works when people are given enough warning to inspect a machine before an unexpected stoppage: there still has to be time to act. The great news is UNO Q now brings the sensing, local intelligence, Linux applications, connectivity, and machine-facing control needed to build that workflow on a single platform. It allows teams to start with a simple question – “Is this machine still behaving normally?” – and to develop the answer into a scalable maintenance system.

Ready to never be caught off-guard by a faulty machine or worn-down part again? Build a custom predictive maintenance system that you can easily train on your specific data, with UNO Q and Arduino App Lab.

UNO Q is available to order from the Arduino Store as well as DigiKey, Farnell, Mouser, Newark, RS Components, and Robu.in; along with our other authorized distributors and resellers.

Arduino, UNO, Modulino and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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jetpax on GitHub has dropped PiZZa, running Arduino on the Raspberry Pi Zero 2 W.

Write sketches in the Arduino IDE and run them on a Raspberry Pi Zero 2 W or the original Raspberry Pi Zero W. A small firmware on the SD card (a Zephyr “loader”) loads your compiled sketch as a runtime module — so after a one-time setup, uploading is one button. No SD swap, no manual re-flash.

• A Raspberry Pi Zero 2 W or an original Raspberry Pi Zero W.
• A microSD card (≥ 2 GB) and a way to write it (Raspberry Pi Imager).
• A micro-USB data cable (not a charge-only cable).
• Arduino IDE 2.x on Apple-Silicon macOS, Linux, or Windows.

What works:

Pi Zero 2 W: GPIO, SPI, Wire (I²C), Serial (USB-CDC; mini-UART as Serial1), and WiFi (the WiFi library / brcmfmac).

Pi Zero W (original): GPIO, Serial (USB-CDC; mini-UART as Serial1). SPI / Wire / WiFi are pending — the BCM283x driver family is already ported for the 2 W and re-basing to BCM2835 is mostly mechanical, but each takes a bring-up cycle.

• Not available on either yet: PWM / analogWrite.
• Not supported due to hardware limitation: analogRead() (no on-chip ADC, but external SPI/I²C ADCs like ADS1115 or MCP3008 work).

Check it out on GitHub.

The Adafruit ADS7128 8-Channel ADC and GPIO Expander is a bit of a Platypus of the I2C expander world: hard to categorize as just an ADC or a GPIO expander since it combines the capabilities of both! Featuring the aforementioned ADS7128, it can run on 3.3 or 5V logic and power, and all 8 I/O pins can individually configured to perform either 12-bit SAR analog input, push-pull or open drain digital output, or digital input.

The Adafruit ADS7128 8-Channel ADC and GPIO Expander guide has everything you need to get started with this ADC. There’s pages for overview, pinouts, CircuitPython, Arduino and resources for download.

Read more at Adafruit ADS7128 8-Channel ADC and GPIO Expander

Version 0.56.0 of the Arduino^® Core on Zephyr is live – and it’s a sizable update to the earlier release. Think optimized performance, expanded hardware capabilities. We’re still smoothing some edges towards the official Stable release, but if you’ve been testing the beta, prepare for a meaningful upgrade.

Multimedia expansion: Arduino^® GIGA Display Shield & Arduino^® Portenta H7 board video support

Multimedia capabilities take a major step forward in this release, establishing native support for advanced visual outputs.

We have introduced official video support for Portenta H7 alongside full compatibility for GIGA Display Shield when paired with the Arduino^® GIGA R1 WiFi. Whether you are building industrial interfaces, interactive kiosks, or custom dashboards, these display features are now fully integrated into the core and ready for your application code.

Core optimizations and network bug fixes

For our supported board lineup, this release delivers several updates to performance, pin management, and connectivity:

• RAM usage optimization: We have optimized internal memory management across the core. This effectively lowers the core’s background footprint, freeing up more RAM for your sketches, complex variables, and larger application buffers.
• Dynamic pin-muxing improvements: Runtime pin multiplex configurations have been refined. This improvement allows for more flexible and reliable dynamic pin reassignment, ensuring better stability when managing hardware peripherals programmatically.
• Network fixes and improvements: Rather than adding new features, this time around we focused on essential bug fixing within the network stack. These improvements resolve ongoing connection issues and optimize socket management to make your connected prototypes more dependable.

How to get started

To update, open the Arduino^® IDE, search for “zephyr” in the Board Manager, and install the 0.56.0 release. For a granular breakdown of specific code commits and fixes, you can view the full release notes on GitHub.

First use: flashing the Zephyr loader

To prepare a supported board for running Zephyr-based sketches for the first time, you must install the Zephyr loader firmware onto your hardware. Follow these steps within the Arduino IDE 2:

1. Enter bootloader mode: Double-click the physical RESET button on your board.
2. Select a programmer: Go to the Tools -> Programmer menu and select any available programmer.
3. Burn the loader: Navigate to Tools and click Burn Bootloader to write the Zephyr loader to the board.
4. Upload your first sketch: Once the loader is successfully installed, put the board into bootloader mode by double-clicking the RESET button one more time, and upload your sketch. After this initial setup, subsequent uploads will happen automatically without manual resets.

Important reminder: It is highly recommended to update the Zephyr loader with each new core release. Keeping the loader aligned with the current core version ensures your board remains fully functional, secure, and compatible with future framework modifications.

A streamlined workflow for the Arduino^® UNO Q board

If you are using UNO Q, you can completely skip the manual process above. Starting with version 0.56.0, the core automatically checks the loader version behind the scenes during every single sketch upload and handles any necessary updates natively. You can write your code, click upload, and let Arduino IDE and Arduino^® App Lab take care of the rest. 

We are actively working to bring this automated behavior to all other supported boards in future releases.

Help us shape the final release!

Your real-world testing continues to be invaluable as we head towards the Stable milestone. Please share your feedback, report bugs, or contribute on our GitHub Issues page. Thank you for being an active part of the Arduino community!

Arduino, GIGA, Portenta, GIGA R1, and UNO and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

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Waveshare ESP32-C6-Zero-B is a variant of the company’s ESP32-C6-Zero USB-C board that adds 5V-36V wide supply voltage support for industrial applications It’s based on the ESP32-C6FH8 wireless RISC-V SoC with 8MB flash, 2.4 GHz WiFi 6, Bluetooth 5.x LE, and an 802.15.4 radio for Zigbee and Thread. It also exposes up to 20 GPIOs via headers and pads, and features an RGB LED, as well as Boot and Reset buttons. Waveshare ESP32-C6-Zero-B specifications: SoC – Espressif Systems ESP32-C6FH8 CPU Single-core 32-bit RISC-V clocked up to 160 MHz Low-power RISC-V core @ up to 20 MHz Memory – 512KB SRAM Storage – 320KB ROM, 8MB flash Wireless – 2.4 GHz WiFi 6, Bluetooth 5.3, and 802.15.4 radio for Thread/Zigbee USB – USB Type-C port for program download and debugging Expansion 2x 9-pin headers with up to 13x GPIOs, UART, I2C, SPI, 6x ADC, I2S, PWM, Vin, 3.3V out, GND 7x solder pads [...]

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Arduino has just introduced three new Modulino modules: the Modulino Hub to support up to eight I2C devices (64 devices with 8 Hubs), the Modulino Extender to extend the I2C range to up to 30 meters, and the Modulino Motors to control two DC motors or a single stepper motor with precision. I’ll focus on the I2C modules in this post, as I find the Modulino Extender module particularly interesting, since I always considered I2C to be a short-range communication protocol, and never thought of using it for (relatively) long-range wired communication. Modulino Hub While the I2C protocol supports up to 127 devices in theory, address conflicts can become an issue in complex builds where multiple sensors or actuators share the same default address. The Modulino Hub aims to solve that, and multiple Hubs can be daisy-chained, each with its own address, to support up to 64 devices. Modulino Hub specifications: [...]

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