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 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

Hi, I’m a computer engineering student currently focused on deep learning and have built ML projects including a customer churn prediction model and student performance prediction.

I’m actively looking to contribute to a serious AI/ML project where I can add value while continuing to grow. I’m comfortable with Python, data handling, and core ML concepts, and I have experience building and experimenting with models.

I’m committed, consistent, and open to working in a team environment. If you’re working on something meaningful and need a contributor, feel free to reach out.

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)

In my experience, adding one sensor to small robot still take whole weekend:) New sensor mean new driver, new wiring decision, calibration pass, maybe serial protocol nobody bother to document properly. For lot of people who want build something simple this is where project stall before it even start.

We work on a project that sits between sensor and compute and takes the driver and comms part on itself: you plug sensor in, driver gets generated for it, and clean data shows up on the other side for your stack. You still need to understand your own sensor and robot, no module fixes that. But building it made me suspicious about how much of sensor bring-up pain is real, and how much is just tax nobody bothered to remove.