Some time ago, I have published a post about creating a Pan Tilt camera using UV4L library on a Raspberry Pi. It works really well. The camera position can be adjusted via a web interface while providing a live stream. It seems like a nice little thing. However, after upgrade of my Raspberry Pi from Debian Stretch to Debian Buster it broke down. The live stream is no longer available through UV4L on Debian Buster. Here is how to fix it!
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Recently I have discovered a serious bug in the new version of STM32CubeMX. It considers the clock configuration but does not always appears and is hard to debug.
I would like to present a simple project which involves Raspberry Pi with a camera. Sounds boring, right! But the camera can be tilted in two axis using two servos which are directly controlled via STM32 microcontroller which in turn communicates with Raspberry Pi. Furthermore, the RPi is hosting a web server with interface to control position and speed of the camera and of course the video is streamed so you can see what is going on i.e. in your room. If you are even a bit intrigued then keep reading.
Here I describe how to set up secure video streaming using Raspberry Pi and a dedicated camera with UV4L. This post is written in tutorial–like form and the set–up presented here will be used in my other projects.
Today, I would like to ponder on a subject of creating some measurement systems, or in more general, embedded systems. There are multiple approaches to make such a system. Let’s consider a few of them — the most popular approaches.
Embedded systems are gaining popularity by the day. Those systems are used in Internet of Things (IoT) but also in more advanced control systems. However, sometimes a need of more sophisticated system is emerging which requires more computational power. I would like to present some architectures of such systems and highlight some features of those approaches.
Long range wireless communication is getting more and more attention. Today, I would like to share with you my experience with a LoRa module — SX1278 and also the drive for this device.
Recently, I have written an article Automatic router reboot device with Arduino where I have presented a simple Arduino–based solution to reset router periodically. Since this is not the best idea to reset it, even if it does not require resetting, I have applied purely software–based solution. As the title says I have used a Python script which runs on Raspberry Pi connected to a local network.
For quite some time I was experiencing a problem with my router. When the temperature rises behind the window it just starts to freak out. Every once two weeks or so it drops the Internet connection. Local network is still running but the incoming or outcoming connections are terminated. The only solution to this problem is to reset the router or just take out the plug and insert it after a few seconds. However, it requires me to do this every few days to make sure that the connection is good and running. I have decided to automatize the process with Arduino since it was laying around.
Some time ago I have decided to make myself a electric bike — e-bike as they are called now. However, buying a stock solution, I mean a stock e-bike, was not an option because mainly of two reasons. The stock e-bikes are quite expensive stuff, this is one. The second one is about the actual parameters of the electrical bicycle. Manufactures sell e-bikes which have limited power output to 250 Watts and are only meant to support you and not drive themselves. But wait a minute … This should be about a spot welder not the e-bike ;). Well, each electric devices ought to have a power source and this is how the idea about making my own spot welder was born.
Recently, when I wanted to install IPython notebook server I discovered that for quite some time the project had changed its name to Jupyter. You may recall one of my posts about the IPython where I presented how to install it on Raspberry Pi. Now, I would like to present the Jupyter project to you which comes with some neat new features.
Many articles here and there describe how to use OpenCV on Raspberry Pi. However, most of them are about setting up the environment by hand — meaning compiling OpenCV from sources. There are two main disadvantages to this approach. Firstly, you have to spend some time to compile it. On Raspberry Pi 3 it takes quite some time, and not mentioning the earlier versions of this mini PC. Secondly, maintaining up–to–date version requires additional time. Still, you can go for middle ground — cross–compilation that requires less time but you have to set up the environment properly. Having above in mind I will introduce you to the OpenCV with Python interface installed from pre–compiled packages. If I have your attention keep reading 😉
Yes, I am still using the practically obsoleted ST-Link-V1 on a STM32F1 disco board. It still gets the job done but regretfully it is not supported out of the box by IDE I happen to use from time to time.
When I gave a try to the AC6 (SW4STM32) I found out that, to not much of a surprise, the ST-Link-V1 is not supported. Only V2 and V2.1 are supported. Well, I decided to change that unfortunate situation because I have two of the disco boards with this debugger laying around.
I would like to present one of my latest projects which is the GSM GPS tracker. Basically, it is a device which allows to send its current position using GPS via SMS. Also it is able to log the position on a microSD card.
Some time ago I have written SPL vs HAL: which one should you use where I have focused on differences between two main frameworks for STM32 — Standard Peripheral Library (SPL) and Hardware Abstraction Layer commonly known as HAL. Since the recent post only focuses on those two sets of libraries I have decided to write some examples which can tip the scale. What is more, at the end of previous article I have asked an important question for a developer — does the STM is going to introduce us to a brand new library. Answer to this and other questions are further in this post.
Good quality estimation of tilt angles such as roll and pitch is desired when it comes to an UAV control. Without good quality signal a proper work of flight controller is nearly impossible. However, the task of filtering is not an easy task, especially when it comes to DSP (digital signal processing). It is even harder when digital filter is inadequate. In this post a mechanical filter is presented that allows to significantly improve attitude estimation in terms of roll, pitch and yaw.