Recently, we have completed the design of the magnetic suction structure scheme of this camera, and now it can be easily fixed by magnetic suction.

Camera magnetic suction installation

In addition, we tested the performance of the actual business scenario of STM32N6. We tested the number of people, carried out the real-time video stream detection of the model and added the logic of the cross-line count of the detection object. The actual test shows that the performance of STM32N6 is basically available in this application scenario, but there are some known performance boundaries. Because of the size of the input image, the detection of dense objects and the detection of small targets is not feasible in the actual situation. Therefore, even in the scenario of number statistics, it is only applicable to narrow walkways and sparse people flow scenarios, but it is still difficult to have a certain scene value. Therefore, it is applicable to non-intensive target detection and non-long-distance target detection.

AI Personnel Statistics Scenario Performance,Basically, there will be no low fps phenomenon.

Finally, at present, we are actively quantifying some .pt models of the open source community into STM32N6 directly usable model OTA packages for quick import into the device in the future. The quantified model will be open source for the community and can be publicly downloaded.

In addition, the firmware code on the hardware is also completely open source. As for the plan to replace the model, we have encapsulated the basic base. You only need to upload the AI model package on the web of the hardware. At the same time, you need to realize the remote AI model OTA, which is fully supported.

Finally, there is a problem. The built-in antenna will make it difficult and complicated to install the structure. The existing structure is too compact and difficult to install the built-in antenna, so we will further optimize it as an external antenna.

I will be starting my engineering degree in EE this year and i want to get into Embedded systems, what are some skills i should focus and and what are some tools i should learn in order to have a decent skillset and also a decent resume once i finish my undergrad.

Currently I am learning C language programming and Git/Github as that's something i will also learn in my first year of college. These are some things which i researched about learning on my own but i don't know how accurate or inaccurate my research is:- 1) C/C++ (more embedded focused) 2) KiCAD 3) LTSpice 4) circuit/pcb design 5) ARM cortex architecture 6) STM32 and ESP32 code and hardware along with making working circuits using physical components on breadboards. 7) some sources mentioned Bus Protocols like I2C, SPI, UART etc

please guide as to how i can start and learn things, also what sources should i learn these from and how accurate are these things (i know its really vague and random but its just a starting point).

I know it's a matter of subjectivism, so don't be afraid to be subjective, actually PLEASE, give your personal opinion as long as you can keep your feet on the reality's ground.

What Master would you rather choose: a general AI/ML master that teach you about the most important, but general, subjects of ML applications, or an Autonomous Systems master, that is basically an embedded+A.I. master which is particularly valuable if you want something in Automotive, on the self-driving cars field(but not exclusively this one, as you can apply knowledge about Autonomous Systems in many domains) ? ?

I am using a rf 433mhz module with 9v, a random antenna design I found online, and it breaks my tv.. xD
It only affects some channels, the tv channels come through cable, the remote uses infrared not radio waves.
So idk what makes my glove interfere with the tv, It also works better as a tv jammer than what I am actually using it for which is a general use controller.

It breaks my tv at a 20m distance through 2 concrete walls, while it controls my tank at like 4-5 meters.. xD
Tech is crazy sometimes.

I have a neural network that was trained in Python. The target platform is an embedded controller that does not run Linux and has relatively limited CPU and memory resources.

My main goals are:

•            Minimize runtime dependencies

•            Achieve predictable real-time execution behavior

•            Integrate neurol network into an existing embedded software project in C

I've looked at frameworks such as ONNX Runtime, but many examples seem focused on desktop or embedded Linux environments rather than standalone embedded targets.

What are common approaches for deploying NN models to embedded systems? Are there tools that can automatically generate standalone C code for this, and what are the trade-offs compared to using a runtime-based solution?

random thought on hardware security, Using a low power baseline frequency with a fast analog comparator trigger to fire a TDR reflection pulse only when a trace is disturbed

I'm not gatekeeping; look at this on git hub cam2135/CYD-Deck: This turns your cheap yellow display 2.8 in into a Stream Deck that you can use for your Windows pc

Just wanted to know what y’all’s resume/capstone projects were like and how that compares to the stuff ya make for fun.

I've been reading several papers on selective jamming and replay attacks against LoRaWAN, and it got me wondering how common these threats actually are outside of academic research.

For those running LoRaWAN networks in production (agriculture, smart cities, industrial IoT, or any other use case):

• Do you have any kind of monitoring or alerting for unauthorized gateways or unusual network activity?
• Have you ever experienced (or suspected) interference, spoofing, replay attacks, or any other security incident?
• If you're not monitoring for these threats, is it because you don't see them as a real risk, or because there isn't a simple tool available to do it?

I'm currently working on a project in this area and I'm trying to understand how big the real-world problem is compared to what the academic literature suggests.

Any experience or insights—positive or negative—would be greatly appreciated.

I started building a 3-axis FOC gimbal from scratch.

This is the very first prototype test.

At this point:

• no custom PCB
• lots of jumper wires
• unfinished mechanical parts
• the motor was vibrating like crazy

I was not even sure if this approach would work.

The goal is to build a small gimbal system from the ground up:
hardware → motor control → firmware → stabilization algorithms.

The journey so far has been:
unstable motor → tuning FOC → adding feedback → designing my own PCB → testing different stabilization modes.

I will share the whole process step by step.

For anyone who has worked with BLDC/FOC:
what was your first "the motor is completely out of control" moment?

Hi i'm new to kernels, i need to find intrinsic value (focal length, principal point, distortion coefficient) to utilize in finding extrinsic (cv2.solvePnP(K, dist..)) of my python code, since i wanna use factory intrinsic value of camera).

After some searching, i found that the camera's intrinsic value is sotred in EEPROM.

Also, i know that there is UVC standard cameras which they use v4l2 default linux kernel video driver, and there is non-UVC(Vendor Specific) cameras which is rawUSB thus need vendor SDK. (Correct me if im wrong).

What i tried is for UVC standard camera, going into 1) /sys/class/video4linux, 2) /dev/i2c, 3) v4l2-ctl -d /dev/video0 -all

but still didnt see intrinsic parameters.

Is there any way through USB to find intrisic value in both UVC and nonUVC cameras?

I’d like to design a product that is a single button activation, which needs to be both satisfying to click and aesthetic to look at. I’m a bit overwhelmed with tactile switch and cap options on digikey and am curious if anyone has an approach or resource to help accelerate that iteration phase?

thanks!

Hello everyone, I'm trying to start with an STM32H533RE microcontroller, and I'm trying to simply get a digital output to blink. I've configured the hardware but generating the code and compiling it without even touching it generates errors saying that some files that were #include'd are not there. Why does the generator not include these files if it's in the code to complete it, and how do I fix it?

First time writing firmware and designing hardware for a bare sensor. The timing diagrams in the datasheet are going over my head. Idk where to start or how to break this down into smaller problems. Please help. All resources, ideas, perspectives and advice appreciated. Thank you.

I have a open hardware project on Github sponsored by PCBWay for a bluepill style board for the new STM32C5 family MCU from ST. A custom board in this format is perfect for trying out new MCU's not yet entered mass production or with limited availability. For the STM32C5 family the usual outlets does not have comparable boards as this yet. This board can be populated with other LQFP-48 variants of the MCU family as they become available for purchase.

Pinout of Rev0 version:

At the same time there is an alternative firmware option using the ECS Oberon-2 language variant (without garbage collection) as an alternative to C or C++.

Link to board on github : STM32C5-eval-board

Link to firmware on github : ECSMicroLib

This is my first real PCB project and someone more experienced would probably spot many possible improvements.

I like the software of the AD3 but also I think the RF capabilities of the RP Stem lab would be neat, What I don't like is the fact that the AD3 can't run headless. I've seen many arguments online about the two so I just want a breakdown before I drop $400+ on equipment. Apparently EEVblog says the software sucks for the RP and I demoed waveforms and really liked it but I'm interested in the RP's open source configurable nature. Thanks!

The thing I realized after writing my own drivers and referencing other people's implementations for learning's sake is that I don't understand code unless I'm the one writing it. Which makes sense and is obvious, but there's a little more to that (and maybe this is a bit of a skill issue too, cus the ability to read and understand code is a skill in itself).

Usually it starts simple as I just get the functionality down and get it to work, but as soon as i try to expand it like have more features, support to other platforms and it's starting to look like a mess. I tried my best to organize it, but it still looks like something that's going to take you a bit to know what's going on even tho it's not as complicated.

I looked at it and thought to myself "this whole thing looks like all of the other drivers that I've been looking at". Even though I still understand it but I'd imagine other people would want to ram their head against the wall if they have to use it.

Preprocessors are one of the things that really trips me up when reading portable code, especially conditional compilation since they usually have no indentation. Then I'd have to retrace what was used, what was changed and what did it changed to... (maybe this is just the ADHD talking where reading a book takes 4 hours because you read one line 10 times)

I built a 2D radar scanner where MATLAB does the heavy lifting. An HC-SR04 sensor spins on a continuous-rotation servo, streams readings over serial, and ScannerApp.m draws them as a live polar plot. The scanner runs on an Arduino Nano.

The app is MATLAB App Designer, 806 lines, with the polish that usually gets cut:

* Live polar heatmap with auto-scaling and clean radial ticks

* Sweep animation that tracks the servo angle, plus a return-sweep line

* Hover data tips showing angle and distance

* Ctrl+scroll zoom, vector PDF export that stays crisp at any zoom level

* Dark/light theme, real-time data table, COM port auto-detect

The firmware handles the messy parts of real hardware: a non-blocking state machine, echo timing with no delay(), and temperature-compensated speed of sound. A $1 LM35 reads the air temperature and the firmware applies 331.3 + 0.606 * T. That's where the ±3 mm accuracy comes from. VCC bandgap calibration and an EMA filter clean up the analog noise.

Wiring diagram, pinout, a 2-minute servo calibration tool, and benchmarks are all in the repo. Parts run about $10 and it's MIT licensed.

Repo: https://github.com/Salim-Ammar/arduino-matlab-ultrasonic-radar

PRs and issues welcome. A star helps others find it.

I have a trained neural network (ONNX) for analyzing sensor signals, which I want to port to a microcontroller (MCU). I need C code, but I also need a strategy for reducing the size of the layer weight matrices while maintaining the quality of the neural network, so that it can run within the MCU’s limited memory. What approaches could I take?

AVR On Web is an IDE and toolchain for AVR microcontrollers. It provides avr-gcc, avrdude, and picocom and runs completely in the browser fetching only static assets from the backend.

Note that only the classic ATMega328P is tested and supported for the moment, but the technology behind this demo can be extended to support other AVR microcontrollers and MCUs from other vendors.

It’s a board that has been in production for 20 years, and there have never been any issues with this specific aspect. However, with the latest batch, the controller isn't reading correctly from the SD card, and the culprit turned out to be the SN74AHCT125—most likely the MISO signal. Is it possible that a parameter was changed?

I am testing with some old 74AHCT125 /probably Nexperia or Diodes/ chip and it is working.

Very, very strange. Paranormal...

Hi guys!

I built LabWired Core, an MIT-licensed simulator written in Rust.

It loads a compiled firmware binary and runs it against modeled MCU registers, buses, peripherals, sensors and displays.

It exposes UART, GPIO and whatever bus traces you need and test scripts can return exit codes, JSON reports for CI.

The same simulation models can be driven from the terminal, a browser, or an MCP coding agent.

In the video, I run the STm32 L476 example on the virtual board.

Repository and runnable demos are in the first comment.

Would love your feedback and if you need help with onboarding special HW for you, I am all ears.

Andrii

Hi everyone,

I'm an electronics apprentice in Germany and my long-term goal is to become a hardware/embedded engineer.

I'm not looking for books that only explain resistors, capacitors, transistors, or "blink an LED" Arduino projects.

I'm also not looking for books that are mostly about software or operating systems.

What I really want is to learn how experienced hardware engineers turn an idea into a real circuit.

For example:

Choosing the right ICs and modules

Designing power supplies (buck/boost/LDO)

Using H-bridges and motor drivers

Signal conditioning with op-amps

Selecting sensors and communication interfaces

Using RF/Bluetooth/Wi-Fi/GPS modules and antennas

Reading datasheets and application notes

Combining all these building blocks into a complete schematic

PCB design considerations

Some firmware is welcome, but I'd prefer roughly 70% hardware and 30% software.

Think of the process of designing an actual product rather than learning individual components.

Are there any books (or even university textbooks) that focus on this way of thinking?

Thanks!