Waveshare UNO-Q-DSI-TOUCH-10.1A is a 10.1-inch touchscreen display designed for the Arduino UNO Q single board computer. The Arduino UNO Q exposes its MIPI DSI interface through the 60-pin JMEDIA and JMISC headers, and for all practical purposes, you normally need a board like the Arduino UNO Media Carrier to make use of it. The UNO-Q-DSI-TOUCH-10.1A display solves that by integrating the headers so users can simply insert the Arduino UNO Q into it without an additional adapter board or special wiring. Waveshare UNO-Q-DSI-TOUCH-10.1A specifications: Screen Size – 10.1-inch Panel type – IPS panel Native Resolution – 1280 x 800 Refresh Rate – 60 Hz Colors – 16.7 millions Viewing Angle – 178° Brightness – 300 cd/m² Contrast Ratio – 800:1 Response Time – 35 ms Multi-touch support Capacitive multi-touch (we’re told the actual number of touch points  somehow depends on the software system) Interface I2C Tempered glass, optical bonding Camera [...]

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When you’re developing systems that support air navigation services, the margin for compromise is essentially zero. Reliability isn’t a “nice to have”, it’s the baseline. That’s the world in which Techno Sky operates, and it’s what makes their decision to build around the Arduino® Opta RS485 microPLC responsive to the project’s identified requirements.

Techno Sky is an ENAV Group company that manages and maintains hardware and software systems supporting air navigation services, and develops innovative technologies for the Air Traffic Management (ATM) sector. Their R&D department works on solutions for air navigation service providers (ANSPs), airports, aviation authorities, and organizations worldwide – systems designed for use under demanding operational conditions.

The challenge: a compact, reliable visual emergency signaling system

The project that brought Techno Sky to Arduino was the development of a visual emergency signaling system for integration within ENAV’s Digital Tower environment. The requirements were unambiguous: industrial-grade reliability, compact form factor, continuous operating capability, Ethernet and industrial communication interfaces, integrated I/O, and the ability to develop and maintain the system without building dedicated custom electronics from scratch.

A fully custom hardware design was on the table, but it would have added significant time and complexity – hardware design, integration, and validation cycles that would delay the project without necessarily improving the outcome. The team needed a platform that would let them focus on application logic and system integration, not on reinventing the controller underneath it.

Opta: a non-conventional, but versatile and effective path to innovation

The R&D team evaluated conventional PLCs, custom embedded electronics, and single-board computers before landing on Opta. It selected Opta based on its combination of industrial characteristics and software flexibility in a single package. The team determined that the conventional PLCs it evaluated offered less development agility for this project. Custom electronics would have added hardware design overhead. The single-board computers evaluated by the team did not meet the project’s identified specifications.

The Opta RS485 offered integrated Ethernet and RS485 communications, relay outputs, and the flexibility of the Arduino programming environment and library ecosystem – all in a compact, DIN rail-mountable form factor designed for industrial applications. Critically, it allowed new functionality to be implemented through software without adding dedicated hardware, a quality that would prove important as the system evolved.

An architecture for in-house deployment within the year

In the deployed system, Opta acts as the main embedded controller, managing signaling outputs, Ethernet communication, RS485 communication with external equipment, operating modes and timing, supervision functions, and communication integrity and security. The system currently implements eight signaling modes on a single controller – functions that the Techno Sky team determined could otherwise have required additional hardware or a more complex architecture.

The main engineering challenge during development was coordinating communications, physical outputs, signaling sequences, and supervision in parallel. The team addressed this through a modular software architecture and the use of watchdog and communication-validation mechanisms – approaches intended to support a manageable codebase and consistent system behavior. Development was carried out internally by Techno Sky’s R&D team using Arduino documentation, libraries, and development resources.

According to Techno Sky, the project started in October and reached operational deployment in under 12 months. One Opta controller integrating more than ten main control functions, designed for continuous operation, with the same architecture carried through from R&D prototype to operational system.

From prototype to production on the same platform

That last point deserves emphasis. R&D teams may prototype on one platform and later transition hardware when moving toward deployment. Techno Sky didn’t need to make that transition. The architecture based on Opta developed during R&D was the architecture retained for operational use – a decision that they report was based on its assessment of the platform for this application.

Techno Sky summarized their experience: “Opta enabled us to move rapidly from an R&D concept to an operational solution. Its industrial hardware, combined with the Arduino programming environment and library ecosystem, allowed us to integrate and expand multiple functions without increasing hardware complexity. The fact that we are retaining Opta in the operational system reflects our positive assessment of its effectiveness and reliability for this application.”

More ideas, more innovation

The Techno Sky team has been working with Arduino platforms across their R&D activities for several years, and this project showcases their reinforced confidence in their chosen Arduino product line. Future applications under consideration include monitoring systems, remote control, sensor integration, robotics, automated test equipment, and ATM infrastructure – a range of potential uses that Techno Sky is evaluating.

For teams facing similar integration challenges – multiple control and communication functions, demanding reliability requirements, and pressure to move quickly from concept to deployment – Techno Sky’s assessment is direct: Opta is well suited, in Techno Sky’s assessment, when those functions need to come together in a compact system, without the overhead of custom hardware design.

Ready to explore options for your industrial application? Find out more about the Opta family and Arduino’s industrial-grade range at arduino.cc/pro.

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

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MAXXTANG published a project on Hackster.io that puts a USB-only Brother HL-1110 laser printer on a WiFi network using an ESP32-S3. The computer prepares the job with the normal Brother driver and sends it to TCP port 9100. The ESP32-S3 passes the bytes  through its USB Host port to the printer.

The board’s native USB port goes to an OTG adapter and then the printer, while the separate UART port handles flashing and power. A charge-only adapter will not work. The firmware is an Arduino sketch built with the Espressif ESP32 board package, and it is set up for an N16R8 board with 16 MB flash and OPI PSRAM.

On first boot the board brings up a PrintServer-Setup access point at 192.168.4.1 for entering 2.4 GHz WiFi credentials. After that, a browser dashboard at http://hl1110.local shows printer status, WiFi settings and a restart control. The build also includes mDNS, a watchdog, OTA updates and a 512 KB PSRAM print buffer.

This is a raw port-9100 bridge with no AirPrint, IPP or LPD, and no authentication, so it is meant for a trusted local network. Code is MIT licensed.

See more at hackster.io and content on GitHub.

It’s almost that time of year again! No chance to 3D print your Arduino®-powered pumpkin project? Didn’t get around to animating the LEDs on your costume? There’s still time, and here’s a half-dozen Hallowuino-themed projects to get you inspired.

Spooky, Sassy Pumpkin

You’ve heard of Spooky, Scary Skeletons, well here’s a Spooky, Sassy Pumpkin that is built to use a USB webcam to identify visitors as they approach, then answer their questions about what it sees using the Arduino App Lab Vision-Language Model (VLM) Brick. This jack-o’-lantern packs a whole lot more smarts than your average fruit thanks to the dual-brain VENTUNO Q concealed underneath it. All processing is designed to be handled offline, on-device without sending users’ faces and voices  across networks or to the cloud.

Arduino-Powered Animatronic Supergroup

Bands break up for all kinds of reasons, so we were pleased to see Arduino Project Hub member nerdabilly figure out a way to sync the Costco Skeleton Punk Rocker animatronic with the Lowe’s Reaper Band. The band members are centered around a Bluetooth speaker, while the rocker is triggered by a timer or motion sensor. Not wanting to permanently alter these expensive displays, nerdabilly used the existing analog signal from the band leader to trigger the pink in place of its motion-sensing trigger, so the whole crew can rock out with their skulls out. Nerdabilly, we salute you for keeping this spooky symphony in sync!

Simple Spookiness with Modulinos and MP3s

Here’s some simple steps to sprinkle some spookiness into your season from our own team. Using the UNO R4-based Plug and Make Kit, plus a DFRobot DFPlayer MP3 player module, we show you how to trigger spooky lights and sounds when the Modulino® Distance sensor detects a presence. You can even connect to the Arduino Cloud to activate, customize, and monitor your project remotely. And that’s just the start – the seven included modules and built-in Wi-Fi give you plenty of options for creating your own frightful projects!

Ultrasonic Spider

Everyone has to start their Arduino journey somewhere, and for Project Hub member ParzivalUK, that place was a half-decade old project video demonstrating a fake spider dropping on unsuspecting passers-by. Unfortunately, ParzivalUK found that the accompanying code no longer worked for their project, and there weren’t detailed steps on how to recreate it. Driven by a desire to delight their son with a fun Halloween prank, ParzivalUK pushed on, combining a servo with an ultrasonic sensor to drop a toy spider on anyone who comes within approximately 40cm of the trap, with a continuous rotation servo to wind it back up, ready for the next victim!

Portal Sentry Turret

We love seeing projects shared on the unofficial Arduino Discord server. User jaysicks’ Aperture Science Sentry Turret costume really caught our eye, and we asked for a little more detail. The resulting Instructable is a detailed step-by-step guide for recreating the iconic tripod robot from one of the most beloved puzzle platformers of all time. With no 3D printer at hand, the costume relies on cardboard construction but comes out looking great. A red LED in the eye responds to the audio output from the incorporated GD380D-M audio playback module when a button is pressed, and the whole thing is controlled by an ATtiny85V microcontroller, programmed using the Arduino IDE. We think jaysicks’ son will be the envy of the neighborhood this Halloween, or at least among those with a solid appreciation of classic video games!

Cyber Research Systems T-1-7 Warrior Robot

The T-800 and T-1000 may get most of the screen time, but Redditor u/parsupo recreated the more niche T-1-7 Warrior Robot for their annual Halloween project. Built around an electric wheelchair base, the bot uses an Arduino Mega 2560 to receive and process commands from a remote control, and the same ATmega328 chips found in the UNO to control lights, gun movement, and the head. It may not be made of mimetic polyalloy, but we wouldn’t want to run into it in a dark alley!

What will you make?

Got a spooky Arduino-based project to share? Or need help realizing your creation? Head over to the Arduino subreddit and let us know!

The post How about a Hallowuino half-dozen? appeared first on Arduino Blog.

Arduino, Qualcomm Technologies, and Edge Impulse recently teamed up with Hackster to host the Invent the Future competition. The competition attracted 4,028 registrants and 1,202 hardware applications, culminating in 153 final project submissions across a wide range of real-world applications.

There were several categories in the competition, all unified by the UNO Q board and Arduino® App Lab. The judging is officially complete and the winners have been announced, which means we get to show you the top projects that most impressed the judges and took home the awards!

These winning creations illustrate a range of uses for the UNO Q – and stay tuned, because we’ll also be shining the spotlight on many other fantastic contest submissions in the coming days.

Best Social Impact

While sustainability was a consideration for every category, the Best Social Impact award went to a project built explicitly to make the world a better place. The winning project was the Smart Water Guardian AI, built by KOCHONI Biaou David Barthélémy.

This project puts the edge AI capabilities demonstrated by UNO Q to good use, by monitoring water-quality indicators in real time. Through a host of sensors, it is designed to measure everything from temperature and pH to turbidity and dissolved solids content, allowing for detection of anomalies that correlate with poor water quality – detection that could save lives.

Best in Home Automation

Home automation is a thriving industry, and there are all kinds of tech products you can use to build the smart house of your dreams. But what about your chickens’ house? That’s what Coders Cafe’s Smart Coop 2.0 is for.

Smart Coop 2.0 is a system design, driven by an UNO Q, for intended to support routine chicken care. It is built to handle the basics, like dispensing feed on a schedule and heating the space, using local AI models designed to detect eggs and potential predators. The creator designed Smart Coop 2.0 to make certain chicken-care tasks more convenient and to detect potential predators, including coyotes and foxes.

Best in Gaming

RollerCoaster Tycoon was a popular video game generally among two types of people: those who enjoy creative building and those who like maximizing metrics. Kevin Loeffler is the latter type, and he wondered how real-life roller coasters might stack up. The result is RCT IRL, which is a wearable sensor system for classifying and quantifying roller coaster rides.

“Wearable” here means an UNO Q, a SparkFun 9DoF IMU (inertial measurement unit), and a battery stuffed into a fanny pack. During a coaster ride, the dual-brain board from Arduino is structured to collect movement data through the IMU. Afterwards, three Edge Impulse models are designed to process the data and generate model-based scores labeled Excitement, Intensity, and Nausea. Together with interesting stats like ride time and maximum g-forces, those values populate a report card for the roller coaster, just like in the game.

Best in Industrial IoT

HaloSense V1 is pasquale887’s attempt to give humans new senses via neuroplasticity. In short, neuroplasticity is your brain’s ability to accommodate new functions. Of course, there isn’t anything supernatural or superhuman about that, and your brain can only work with the sensory input it receives. HaloSense V1 provides new information through the senses you already have.

Or, to put it another way, HaloSense V1 is a haptic feedback headset. Through an array of 13 haptic feedback motors, it pulses and buzzes to convey information gathered through sensors connected to an UNO Q. The project is intended to explore whether, over time, a wearer can learn to interpret the haptic patterns as information.

Best in Robotics

We’re in the midst of a shift in robotics towards human-friendly designs. Mohammed Akramuddin’s Curie is engineered to be as friendly as it gets. This little desktop robot is cute, with facial expressions displayed on an OLED screen. The creator designed it to help users, including those with ADHD, manage tasks. 

Like all of the other projects in this competition, Curie contains an UNO Q board. It connects to various APIs, including Groq (a cloud interface that is built to let developers run open-source artificial intelligence models at high speeds), to help its owner manage their workload. It is designed to do that in the usual digital assistant ways, by taking notes and setting reminders, but also by providing more tailored assistance. It can, for example, break a complex task up into small pieces that are more approachable. It is also designed to identify potential distraction and provide a nudge to help users return to their task.

Best in Show

The Best in Show award went to the overall competition winner, regardless of category. The winner was Jorge Eldis González for his work on the Edge-AI Spectrophotometer, which is, as its name indicates, a spectrophotometer that integrates AI on the edge.

A spectrophotometer measures the wavelength of light, in order to detect the properties of sampled materials. That is a non-destructive analysis and can, for instance, tell you if a sample contains contaminants.

The Edge-AI Spectrophotometer is built using commercially available components, using an UNO Q and a SparkFun Electronics AS7343 Triad Spectroscopy Sensor. It then runs a machine learning inference model on the edge, which detects deviations from known baselines. The creator designed the project to make its testing workflow more accessible to less-experienced operators and to explore additional spectrophotometry applications.

Congratulations to all the winners! 

Qualcomm branded products are products of Qualcomm Technologies, Inc. and/or its subsidiaries. Arduino, UNO, and the Arduino logo are trademarks or registered trademarks of Arduino S.r.l.

The post Inside the winning Arduino® UNO Q projects from Hackster’s Invent the Future competition appeared first on Arduino Blog.

The LattePanda Sigma 2 is a compact single board server/computer powered by an Intel Core Ultra X7 358H Panther Lake-H processor with Intel Arc B390 graphics and a 50 TOPS NPU delivering up to 180 TOPS of combined performance for local AI workloads. The board features up to 64GB of LPDDR5X-8533 RAM, three M.2 M-Key slots, one M.2 E-Key slot, and an OCuLink port for storage, wireless, and accelerator expansion, as well as two Thunderbolt 4 ports, dual 10GbE, USB 3.2 Gen 2 and USB 2.0 ports, and HDMI 2.1, DisplayPort 2.1, and eDP 1.5 display interfaces. Key Specifications SoC – Intel Core Ultra X7 Processor 358H CPU – 16-core (4P + 8E + 4LPE) @ 1.9/1.5/1.5 GHz (base frequency), 4.8/3.5/3.5 GHz (Turbo frequency) Cache – 18 MB Intel Smart Cache GPU – 12-core Intel Arc B390 GPU @ 2.5 GHz up to 122 TOPS (INT8) AI accelerator – 50 [...]

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TMRh20Projects posted on the Seeed Studio forum about adding encryption to RF24Gateway. The work builds on nrf_to_nrf, a library for direct radio communication on XIAO nRF52840 and nRF54L15 boards. It does not use Bluetooth or Zigbee. It uses the Enhanced ShockBurst (ESB) protocol and direct radio access.

The library already included AES/CCM encryption, and that is now enabled for both nRF52x and nRF54x parts. The next step is bringing encryption to RF24Gateway, which lets nRF24, nRF52x and nRF54x devices act as wireless Ethernet adapters carrying TCP/IP and UDP. That means MQTT, HTTP and similar protocols can run over the radio network. The Gateway needs a Linux device with GPIO and SPI, such as a Raspberry Pi.

The code is written and the pull requests are in place. The author expects a full release within a day or two.

See more at forum.seeedstudio.com and content on GitHub.

Shawn Hymel wrote a guide in Make: Volume 99 on training a two-wheeled balance bot with reinforcement learning instead of a classical control loop. The agent is trained with proximal policy optimization, or PPO, the algorithm OpenAI published in 2017 that is still used for robot control and for fine-tuning large language models.

Training was in Google Colab using MuJoCo as the physics simulator, no local install is needed. Eight parallel environments run 500,000 timesteps each. The actor and critic are separate networks with two hidden layers of 32 nodes. Training runs in three phases of curriculum learning: balance first, then domain randomization for sensor noise, action delay and motor torque, then randomized nudges, part masses and wheel friction.

The finished policy is exported as an ONNX file and as an actor.h C header. Hymel drops that header into an Arduino sketch for the M5Stack Bala2-Fire kit, where the ESP32 runs inference directly in a 5ms control loop and scales the network’s two outputs to PWM values for the motors. Only the actor is deployed. The critic is left behind after training.

See the video below and more at makezine.com and content on GitHub.

There are plenty of “robot” projects out there that are really just a small remote control platform of some sort. We often like to think of robots of having some sort of autonomy though. [Luca Fagioli] demonstrates a great example of this with his explorer robot that uses Simultaneous Localization and Mapping (SLAM) to navigate its environment.

The robot is a built on a small plastic chassis, with one gear motor per drive wheel. Running the show is an Arduino Uno R4 Wi-Fi, paired with the Sensor Shield 5.0 and an L298N motor driver board. There’s also an HC-SR04 ultrasonic sensor module onboard, mounted atop an SG-90 servo motor so it can be panned around. The ultrasonic sensor determines the range between the robot and surrounding walls, and a map is constructed at the same time as the robot is localizing itself within it. There are also infrared sensors on the bottom of the robot for detecting tape marks on the floor for checkpointing purposes.

Often, SLAM mapping is done with advanced laser rangefinders or time of flight sensors, but there are many ways to go about it as this project demonstrates. We’ve even seen it done in a distributed manner with coordinated drones combining their efforts. Overall, it’s a highly useful technique for robot navigation and wayfinding.

Lolren has published nrf54-arduino-core on GitHub, an Arduino core for Nordic’s nRF54L family that talks to the hardware at the register level. There is no Zephyr runtime underneath and no separate nRF Connect SDK install. Targets include the Seeed XIAO nRF54L15 and XIAO nRF54LM20A, HOLYIOT modules, generic modules and the Nordic nRF54L15 DK.

The usual Arduino calls work: pinMode, digitalWrite, analogRead, Wire, SPI and Serial.

Bluetooth LE is the main wireless surface, with a bundled Bluefruit52Lib compatibility layer so Adafruit-style sketches port over with small changes. ArduinoBLE is not the native BLE library here.

The core also exposes the VPR RISC-V coprocessor and handles board-specific sleep, including the nPM1300 PMIC and QSPI flash deep power-down on the LM20A.

It is installed through the Arduino IDE Boards Manager, or use PlatformIO, which has been supported since release 1.0.21 with GDB debugging and Unity tests. Thread, Matter, Zigbee and Channel Sounding are included for evaluation and are marked experimental, outside the stable feature claim.

See more on GitHub.