Recently I worked on a magnetometer calibration method. This method is based on Levenberg-Marquardt Algorithm (LMA), a non-linear least-squares optimization algorithm. The method is implemented on ArduPilot and PX4, an open-source flight controller firmware.
I have to admit, formulizing mathematical notion from code is not straightforward. I spent several days to learn from LMA basic and finally understanding the sphere fit and ellipsoid fit algorithm.
In case you are wondering about the mathematical part, I write the formulation of the algorithm on PDF since it can’t be viewed on WordPress (unless I pay more for the plugin).
Recently MIT released a course on Computational Thinking with code 18.S191 and it is available on YouTube. I can code in C++ and Python, so the founder’s claim that this code is as fast as C and as easy as Python gains my interest.
Introduction to Julia
Julia is created in 2009 and first introduced to public in 2012. The developers aimed for scientific computing, machine learning, data mining, and large-scale linear algebra. We might have heard this application on Python, but Julia gives advantages to programmer compared to Python.
Although war is not happening, the dangerous impact is still tangible today. Landmine has been one of the threats left by the past wars, killing 15,000–20,000 people every year according to UN Mine Action Service. Demining efforts cost US$ 300–1000 per mine and imposing danger to people, resulting one person is killed and two are injured for every mines cleared.
Robot can be really helpful in solving this problem, as it is designed to do the “dull, dirty, dangerous, and difficult” tasks. In 2017, IEEE Robotics Automation Society’s Special Interest Group on Humanitarian Technology (RAS–SIGHT) held a competition. The competition was Humanitarian Robotics and Automation Technology Challenge (HRATC), held at the 2017 International Conference on Robotics and Automation (ICRA’17).
Extracting text from an image can be exhausting, especially when you have a lot to extract. One commonly known text extraction library is PyTesseract, an optical character recognition (OCR). This library will provide you text given an image.
PyTesseract is really helpful, the first time I knew PyTesseract, I directly used it to detect some a short text and the result is satisfying. Then, I used it to detect text from a table but the algorithm failed perform.
Figure 1 depicts the text detection result, with green boxes enclosing the detected words. You may realized that most of the text can’t be detected by the algorithm, especially numbers. In my case, these numbers are the essentials of the data, giving me value of daily COVID-19 cases from a local government in my hometown. So, how extract these information?
When writing an algorithm, I always try to think as if I’m teaching the algorithm the way humans do. This way, I can easily put the idea into more detailed algorithms.
When you’re reading a table, the first thing you might notice is the cells. A cell might be separated from another cell using a border (lines), which can be vertical or horizontal. After you identify the cell, you proceed to read the information within. Converting it into algorithm, you may divide the process into three processes, namely cells detection, region of interest (ROI) selection, and text extraction.
Recently I had an interview for an internship position with Volocopter. It is an Urban Air Mobility (UAM) company based in Germany. As far as I know, this company is one of the most advance in UAM competition.
One part of the interview was an online coding. The interviewer asked me to “provide a list of all prime number up to 1000”. I found this question interesting as a there’s only one characteristic of a prime number.