Embedded Systems
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News, articles, guides, analytics, projects and startups from the embedded industry.

Topics of the channel:
◽️ machine learning
◽️ internet of things
◽️ embedded software
◽️ advanced electronics
◽️ new engineering ideas

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📟 Framework32 for ESP32 (TTGO T-Display)

Framework32 is designed to give your ESP32 (currently TTGO T-Display) the ability to have multiple, graphical apps, controlled by an intuitive navigational system.

Current feature are:
▫️ Load multiple applications onto your device and choose what to run at runtime
▫️ Graphical "app-style" menu system with infinite scrolling
▫️ Navigate "home" from anywhere by double clicking the left button
▫️ Status bar
▫️ Time (from NTP)
▫️ Wi-Fi strength indicator
▫️ Battery level indicator
▫️ Sleep if not being used (but can be overridden by the open app)
▫️ Config file (with the plan to make it dynamically updatable)
▫️ Easy button handlers provided

More information...

#project #framework #esp32 #display #software
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📟 ESP32 SIM800L GPS Tracker | Vehicle Tracking System using ESP32 + 3D-printable case

By using this project you can track anything from anywhere in the world by using sms and does not require any Wi-Fi router. I am using gsm network for get the GPS coordinates. 2G SIM Card is required in this project. You can track vehicles, bikes, bicycles, pets or anything you want by using this esp32 GPS tracker gsm.

Components Required:
▫️ ESP32 Development Board
▫️ Ublox NEO6M GPS Module
▫️ SIM800L GSM Module
▫️ 2G SIM Card
▫️ Battery 18650 3.7v 2000mAh

You can use this device as a portable or fixed mount somewhere. The enclosure design is compatible with waterproofing (IP 64) so you can mount this outside.

More information:
The main tutorial
The GitHub repository
The enclosure 3D files

#project #esp32 #gps
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🧠 AI Image Generation on an RP2350 Microcontroller

This project brings generative AI image generation to a $1 RP2350 microcontroller, as used in the 🍓 Raspberry Pi Pico 2. Its compact model and inference code fit within 4 MB and run using 520 KB of RAM, generating 128×128 RGB face images in roughly 10–20 seconds.

Features of the project:
➡️Latent flow diffusion transformer
➡️Five-class conditional generation
➡️Int8 post-training quantization
➡️VGA and USB output

The project demonstrates how far generative AI can be pushed on extremely constrained hardware, combining model compression with a highly optimized inference engine. The source code is available on the dedicated 😹 GitHub repository alongside with examples.

⛓ More information...

#project #raspberry #artificial_intelligence
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